This document provides an overview of quantitative finance advisory services. Section 1 defines quantitative finance and its applications in areas like corporate finance, derivatives pricing, and risk management. Section 2 outlines typical advisory services including earnings analysis, hedging strategies, and model validation. Section 3 lists technical competencies in areas such as stochastic processes, Monte Carlo simulation, and energy derivatives. Section 4 provides background on Navigant Consulting, a specialized advisory firm focused on uncertainty, risk, and significant change.
Model risk and validation are important processes for banks that rely on models. There are several potential sources of model risk over a model's lifecycle from data issues to changes over time that impact applicability. Effective validation ensures models are performing as intended and identifies limitations. It should include independent review and testing using quantitative and qualitative techniques on a regular basis to verify models continue to meet requirements.
The document describes an 8-phase statistical approach to developing a customer risk rating model. The model assesses money laundering risk for a bank's customers based on their profiles. Phase 1 defines risk categories like geography, customer, product/account, and transaction attributes. Phase 2 analyzes data completeness and variation to select meaningful attributes. Phase 3 tests attribute correlations. Phase 4 samples customer profiles for subject matter expert risk ratings used to calibrate the model in Phase 5. Phase 6 evaluates the model's performance and uncertainty. Phase 7 implements the optimized model to automatically rate customer risk.
This document summarizes a case study analyzing rules for mining data from the S&P 500 stock market index. It discusses potential biases in backtesting rules to select superior performers and statistical methods to minimize these biases. Specific topics covered include data mining biases, techniques to avoid data snooping bias by splitting samples, defining the case study statistically, transforming data series into market positions with rules, constructing technical analysis indicators from price and volume data, and categories of rules examined including trends, extremes/transitions, and divergence.
Rapid Model Refresh (RMR) in Online Fraud Detection EngineWenSui Liu
This document discusses Rapid Model Refresh (RMR), an approach used by PayPal to continuously update fraud detection models in real-time. It describes:
1. Traditional fraud detection tactics like heuristic, scoring, and rule-based approaches and their limitations for an online setting.
2. PayPal's multi-level fraud detection engine that uses risk scoring, rule induction, and agent review to identify high-risk transactions.
3. The implementation challenges of PayPal's growing international footprint and dynamic fraud trends.
4. How RMR addresses these challenges through automatic model development, real-time deployment, and daily monitoring across data, algorithm, and deployment layers.
This document discusses perspectives on active and passive money management. It begins by defining active and passive investors, with passive investors taking a buy-and-hold approach to minimize costs while active investors seek to outperform indexes by identifying individual stocks. It also explains the differences between relative and absolute return vehicles, as well as the concepts of alpha and beta. The document then covers the top-down fundamental analysis process and how stocks with solid fundamentals can outperform over long horizons. It provides examples of how active managers identify stocks and examines the record of professional money managers. The document concludes by discussing market efficiency, behavioral finance, and how information becomes incorporated into securities prices.
The document provides an approach for improving the efficacy of alerts from anti-money laundering transaction monitoring models by reducing false positives. The approach involves regularly evaluating rule efficacy, acquiring historic transaction and disposition data, analyzing the data to understand patterns, building a detection engine to test threshold combinations, and quantitatively and qualitatively analyzing the results to identify the combination with the best efficacy based on case and SAR retention proportions while minimizing false positives. Key steps include prioritizing rules for tuning, testing threshold permutations, sampling transactions for investigator review, and approving final threshold changes. The goal is to generate higher quality alerts while controlling compliance costs.
This document discusses data mining methods and implementation of predictive data mining architecture for stock market prediction. It describes predicting unknown data values using classification, regression, and time series analysis. Two types of predictions discussed are stock market and environmental predictions. Stock market prediction aims to determine future company stock prices, while various parameters like return on investment are analyzed. The document also covers data mining techniques like descriptive and predictive mining, algorithms, and performance evaluation metrics like correct profitable trade signals and annual return on investment.
Stochastic Modeling for Valuation and Risk ManagementRoderick Powell
This document discusses using stochastic modeling for valuation and risk management. It begins with an overview of stochastic modeling and its uses in valuation and risk analysis. It then covers simple random sampling and Monte Carlo simulation techniques. The document provides an example of simple random sampling using a dice roll. It also discusses stratified sampling and provides an example to show how it can produce a more representative sample than simple random sampling. Finally, it discusses applications of stochastic modeling to option-adjusted valuation of fixed income securities and mortgage-backed securities.
Model risk and validation are important processes for banks that rely on models. There are several potential sources of model risk over a model's lifecycle from data issues to changes over time that impact applicability. Effective validation ensures models are performing as intended and identifies limitations. It should include independent review and testing using quantitative and qualitative techniques on a regular basis to verify models continue to meet requirements.
The document describes an 8-phase statistical approach to developing a customer risk rating model. The model assesses money laundering risk for a bank's customers based on their profiles. Phase 1 defines risk categories like geography, customer, product/account, and transaction attributes. Phase 2 analyzes data completeness and variation to select meaningful attributes. Phase 3 tests attribute correlations. Phase 4 samples customer profiles for subject matter expert risk ratings used to calibrate the model in Phase 5. Phase 6 evaluates the model's performance and uncertainty. Phase 7 implements the optimized model to automatically rate customer risk.
This document summarizes a case study analyzing rules for mining data from the S&P 500 stock market index. It discusses potential biases in backtesting rules to select superior performers and statistical methods to minimize these biases. Specific topics covered include data mining biases, techniques to avoid data snooping bias by splitting samples, defining the case study statistically, transforming data series into market positions with rules, constructing technical analysis indicators from price and volume data, and categories of rules examined including trends, extremes/transitions, and divergence.
Rapid Model Refresh (RMR) in Online Fraud Detection EngineWenSui Liu
This document discusses Rapid Model Refresh (RMR), an approach used by PayPal to continuously update fraud detection models in real-time. It describes:
1. Traditional fraud detection tactics like heuristic, scoring, and rule-based approaches and their limitations for an online setting.
2. PayPal's multi-level fraud detection engine that uses risk scoring, rule induction, and agent review to identify high-risk transactions.
3. The implementation challenges of PayPal's growing international footprint and dynamic fraud trends.
4. How RMR addresses these challenges through automatic model development, real-time deployment, and daily monitoring across data, algorithm, and deployment layers.
This document discusses perspectives on active and passive money management. It begins by defining active and passive investors, with passive investors taking a buy-and-hold approach to minimize costs while active investors seek to outperform indexes by identifying individual stocks. It also explains the differences between relative and absolute return vehicles, as well as the concepts of alpha and beta. The document then covers the top-down fundamental analysis process and how stocks with solid fundamentals can outperform over long horizons. It provides examples of how active managers identify stocks and examines the record of professional money managers. The document concludes by discussing market efficiency, behavioral finance, and how information becomes incorporated into securities prices.
The document provides an approach for improving the efficacy of alerts from anti-money laundering transaction monitoring models by reducing false positives. The approach involves regularly evaluating rule efficacy, acquiring historic transaction and disposition data, analyzing the data to understand patterns, building a detection engine to test threshold combinations, and quantitatively and qualitatively analyzing the results to identify the combination with the best efficacy based on case and SAR retention proportions while minimizing false positives. Key steps include prioritizing rules for tuning, testing threshold permutations, sampling transactions for investigator review, and approving final threshold changes. The goal is to generate higher quality alerts while controlling compliance costs.
This document discusses data mining methods and implementation of predictive data mining architecture for stock market prediction. It describes predicting unknown data values using classification, regression, and time series analysis. Two types of predictions discussed are stock market and environmental predictions. Stock market prediction aims to determine future company stock prices, while various parameters like return on investment are analyzed. The document also covers data mining techniques like descriptive and predictive mining, algorithms, and performance evaluation metrics like correct profitable trade signals and annual return on investment.
Stochastic Modeling for Valuation and Risk ManagementRoderick Powell
This document discusses using stochastic modeling for valuation and risk management. It begins with an overview of stochastic modeling and its uses in valuation and risk analysis. It then covers simple random sampling and Monte Carlo simulation techniques. The document provides an example of simple random sampling using a dice roll. It also discusses stratified sampling and provides an example to show how it can produce a more representative sample than simple random sampling. Finally, it discusses applications of stochastic modeling to option-adjusted valuation of fixed income securities and mortgage-backed securities.
This document discusses modeling approaches for operational loss forecasts in stress testing. It describes the seven categories of operational loss events defined by Basel-II, and requirements for operational risk management programs including internal loss data, external loss data, scenario analysis, and business environment factors. It then covers three approaches to calculating operational risk capital and describes a regression-based method used for stress testing that links losses to macroeconomic scenarios. The document discusses defining units of measure, testing unit homogeneity, modeling frequency and severity, and considers Poisson, negative binomial, and time series regressions.
The document discusses the efficient market hypothesis (EMH) and theories of nonrandom price motion. It covers the three forms of EMH - weak, semi-strong, and strong - and defines what constitutes an efficient market. It also discusses criticisms of EMH, such as flaws in its assumptions that investors are rational and pricing errors are random. Behavioral finance theories are presented as alternatives that incorporate human irrationality and cognitive biases. Predictability studies showing prices can be predicted with public information are discussed as contradicting EMH.
The document discusses credit migration risk modeling for calculating the Incremental Risk Charge (IRC). It outlines the requirements for IRC models, including using a one-year capital horizon at a 99.9% confidence level. It also discusses model assumptions, such as assigning positions to liquidity buckets and using a constant level of risk trading strategy. The document then provides an initial outline for an IRC risk model and discusses considerations such as the need to model credit migration risk under both objective and risk-neutral probability measures.
This document discusses various approaches to selecting markets and issues for trading and investing. It covers factors to consider when choosing between futures and stock markets. It also describes top-down and bottom-up analysis approaches, with top-down starting at a macro level and drilling down, while bottom-up starts by analyzing individual companies. Additionally, it outlines methods for analyzing secular trends, business cycles, and relative strength, including the percentage change, alpha, trend slope, Levy, CANSLIM, and other models.
This document discusses system design and testing for trading systems. It covers the importance of using a systematic approach to trading rather than discretionary decisions. Key points include:
- Discretionary vs. non-discretionary systems are compared, with benefits of the latter including avoiding emotions and having a set structure.
- A complete trading system requires decisions around markets, position sizing, entries, stops, exits and tactics.
- Proper testing of a system is essential and involves using clean accurate data, addressing issues for futures data, exploring different testing methods, optimizing parameters within a range, and out-of-sample testing.
This document discusses a case study that analyzed over 6,400 rules for trading the S&P 500 using data mining techniques. It describes how data mining bias can lead to overstating a rule's expected future performance. The case study used statistical inference tests like White's reality check and Masters' Monte-Carlo permutation method to minimize this bias. It details the various rule types analyzed, including trends, extremes/transitions, and divergence. Input data series included raw time series, indicators, and other preprocessed data. The goal was to identify rules with genuine predictive power and evaluate their statistical and practical significance.
This document discusses two new exchanges that plan to trade securities futures - OneChicago and Nasdaq Liffe. OneChicago is a partnership between the CBOE, CME, and CBOT exchanges, with the goal of taking advantage of their combined resources to trade securities futures. Nasdaq Liffe is a joint venture between Nasdaq and Liffe that will leverage Nasdaq's technology and experience in the cash equities market and Liffe's derivatives expertise. Both exchanges received regulatory approval in 2002 and are preparing to launch securities futures trading to capitalize on new opportunities allowed under recent deregulation.
This document discusses system design and testing for trading systems. It covers the importance of using a systematic approach, comparing discretionary and non-discretionary systems, designing a complete trading system that includes markets, position sizing, entries, exits and risk management. It also discusses testing methods such as using clean historical data, addressing issues for futures data, common testing tools and parameters, and the risks of overfitting through optimization without out-of-sample testing. The goal is to develop rules-based systems that can be systematically evaluated before use to improve chances of future profitability.
This document discusses methods for valuing earn-outs, which are contingent payments in business transactions. It describes the probability weighted expected return method (PWERM) and option pricing method (OPM) as the primary approaches used under the income approach. The PWERM involves predicting multiple outcomes, weighting their probabilities, and discounting the results. The OPM models an earn-out as an option using inputs like the current metric value, exercise price, term, and volatility. Selecting an appropriate discount rate is challenging given earn-outs' non-linear payout structures.
This document discusses methodologies for calculating Value-at-Risk (VaR) for retail banking. It outlines some key challenges in applying traditional VaR models to retail banks due to the complex, option-laden nature of many retail banking products. It also discusses stochastic interest rate generation processes and modeling approaches that are better suited for retail banks, including the use of historical simulation and Monte Carlo simulation methods. Overall, the document examines how VaR can provide useful insights for risk management but also requires tailored modeling approaches for the unique characteristics of retail bank balance sheets.
The document discusses model risk management for banks. It defines models as simplified representations of real-world relationships that process input data to produce quantitative estimates. Regulators have issued guidance on model risk management. Models are used for various purposes like credit underwriting, capital planning, and stress testing. Model risk can occur if the input data, model itself, or model usage is flawed. Effective model risk management requires governance, model selection and development practices, validation processes, and oversight through auditing. The document provides examples of model risk considerations for regulatory stress testing and new credit loss accounting standards.
The document discusses various time-based trend calculation methods used in technical analysis including:
- Momentum, which measures price changes over time to identify trends.
- Moving averages, which smooth price data by calculating average prices over a set time period to filter out noise.
- Accumulative and reset accumulative averages, which calculate averages over all or reset periods of data.
- The drop-off effect, where simple moving averages are affected when oldest data values are removed from the calculation.
The document describes a stock market model called the "Fab Five" environmental model. The Fab Five model uses four main components - sentiment, monetary readings, combo, and tape (given double weight). Each component is made up of multiple indicators that are assigned values of +1, 0, or -1. The values are combined for each component and across components to assess overall market risk and determine whether conditions are bearish, neutral, or bullish. Examples of indicators include interest rates, market breadth, sentiment polls/surveys, and moving average crosses.
Model Risk Management | How to measure and quantify model risk?Genest Benoit
The aim of this paper is to present model risk situations and a methodology to measure and quantify the associated risk at model level, with different types of assumptions. Then, considering that in practice, a model risk management at model level is hardly feasible, this paper also outlines a method to measure and quantify model risk at risk category level (ex: Credit Risk).
In fact, one of the overarching drivers of this paper is to provide a model risk “value” which will enable you to analyse if the model risk is sufficiently covered. Indeed, although banks already allocate funds regarding this risk (portion of RWA attributed to conservative margins for credit risk, portion of Op risk Value at Risk, etc.), assessing the appropriateness of those funds remain complicated
Event: International Risk Management Conference - http://therisksociety.com
Lecture title: “Crude Oil Option Implied VaR and CvaR”
Date: June 14, 2016
Location: The Hebrew University of Jerusalem
Quick Reference Guide to BSA/AML Risk AssessmentMayank Johri
This document provides an overview of conducting an enterprise risk assessment for anti-money laundering (AML) compliance. It discusses key challenges such as sourcing and validating data from different business lines. The document recommends that financial institutions allocate a dedicated analytics team with AML expertise to automate the risk assessment process. Automating data extraction and leveraging statistical analysis can help set risk thresholds, calculate inherent risks and control effectiveness more robustly and efficiently. This allows the AML team to focus on qualitative work rather than manual data tasks. The end goal is to build a repeatable, defensible risk assessment model that identifies the institution's residual money laundering risks.
LITA Altmetrics and Digital Analytics Interest Group: 4/8/15Cody Behles
Cody Behles from the University of Memphis introduced altmetrics and the focus of the Altmetrics and Digital Analytics Interest Group. Altmetrics are non-traditional impact measures based on online activity that provide a broader view of impact beyond citations. Examples include social media mentions, blogs, mainstream media, and readership metrics. Potential issues with altmetrics include manipulation of data, difficulty standardizing across sources, and weak correlations with citations. The interest group aims to discuss successful integration of altmetrics into librarian tools and address concerns about their role in scholarly communication.
This document discusses modeling approaches for operational loss forecasts in stress testing. It describes the seven categories of operational loss events defined by Basel-II, and requirements for operational risk management programs including internal loss data, external loss data, scenario analysis, and business environment factors. It then covers three approaches to calculating operational risk capital and describes a regression-based method used for stress testing that links losses to macroeconomic scenarios. The document discusses defining units of measure, testing unit homogeneity, modeling frequency and severity, and considers Poisson, negative binomial, and time series regressions.
The document discusses the efficient market hypothesis (EMH) and theories of nonrandom price motion. It covers the three forms of EMH - weak, semi-strong, and strong - and defines what constitutes an efficient market. It also discusses criticisms of EMH, such as flaws in its assumptions that investors are rational and pricing errors are random. Behavioral finance theories are presented as alternatives that incorporate human irrationality and cognitive biases. Predictability studies showing prices can be predicted with public information are discussed as contradicting EMH.
The document discusses credit migration risk modeling for calculating the Incremental Risk Charge (IRC). It outlines the requirements for IRC models, including using a one-year capital horizon at a 99.9% confidence level. It also discusses model assumptions, such as assigning positions to liquidity buckets and using a constant level of risk trading strategy. The document then provides an initial outline for an IRC risk model and discusses considerations such as the need to model credit migration risk under both objective and risk-neutral probability measures.
This document discusses various approaches to selecting markets and issues for trading and investing. It covers factors to consider when choosing between futures and stock markets. It also describes top-down and bottom-up analysis approaches, with top-down starting at a macro level and drilling down, while bottom-up starts by analyzing individual companies. Additionally, it outlines methods for analyzing secular trends, business cycles, and relative strength, including the percentage change, alpha, trend slope, Levy, CANSLIM, and other models.
This document discusses system design and testing for trading systems. It covers the importance of using a systematic approach to trading rather than discretionary decisions. Key points include:
- Discretionary vs. non-discretionary systems are compared, with benefits of the latter including avoiding emotions and having a set structure.
- A complete trading system requires decisions around markets, position sizing, entries, stops, exits and tactics.
- Proper testing of a system is essential and involves using clean accurate data, addressing issues for futures data, exploring different testing methods, optimizing parameters within a range, and out-of-sample testing.
This document discusses a case study that analyzed over 6,400 rules for trading the S&P 500 using data mining techniques. It describes how data mining bias can lead to overstating a rule's expected future performance. The case study used statistical inference tests like White's reality check and Masters' Monte-Carlo permutation method to minimize this bias. It details the various rule types analyzed, including trends, extremes/transitions, and divergence. Input data series included raw time series, indicators, and other preprocessed data. The goal was to identify rules with genuine predictive power and evaluate their statistical and practical significance.
This document discusses two new exchanges that plan to trade securities futures - OneChicago and Nasdaq Liffe. OneChicago is a partnership between the CBOE, CME, and CBOT exchanges, with the goal of taking advantage of their combined resources to trade securities futures. Nasdaq Liffe is a joint venture between Nasdaq and Liffe that will leverage Nasdaq's technology and experience in the cash equities market and Liffe's derivatives expertise. Both exchanges received regulatory approval in 2002 and are preparing to launch securities futures trading to capitalize on new opportunities allowed under recent deregulation.
This document discusses system design and testing for trading systems. It covers the importance of using a systematic approach, comparing discretionary and non-discretionary systems, designing a complete trading system that includes markets, position sizing, entries, exits and risk management. It also discusses testing methods such as using clean historical data, addressing issues for futures data, common testing tools and parameters, and the risks of overfitting through optimization without out-of-sample testing. The goal is to develop rules-based systems that can be systematically evaluated before use to improve chances of future profitability.
This document discusses methods for valuing earn-outs, which are contingent payments in business transactions. It describes the probability weighted expected return method (PWERM) and option pricing method (OPM) as the primary approaches used under the income approach. The PWERM involves predicting multiple outcomes, weighting their probabilities, and discounting the results. The OPM models an earn-out as an option using inputs like the current metric value, exercise price, term, and volatility. Selecting an appropriate discount rate is challenging given earn-outs' non-linear payout structures.
This document discusses methodologies for calculating Value-at-Risk (VaR) for retail banking. It outlines some key challenges in applying traditional VaR models to retail banks due to the complex, option-laden nature of many retail banking products. It also discusses stochastic interest rate generation processes and modeling approaches that are better suited for retail banks, including the use of historical simulation and Monte Carlo simulation methods. Overall, the document examines how VaR can provide useful insights for risk management but also requires tailored modeling approaches for the unique characteristics of retail bank balance sheets.
The document discusses model risk management for banks. It defines models as simplified representations of real-world relationships that process input data to produce quantitative estimates. Regulators have issued guidance on model risk management. Models are used for various purposes like credit underwriting, capital planning, and stress testing. Model risk can occur if the input data, model itself, or model usage is flawed. Effective model risk management requires governance, model selection and development practices, validation processes, and oversight through auditing. The document provides examples of model risk considerations for regulatory stress testing and new credit loss accounting standards.
The document discusses various time-based trend calculation methods used in technical analysis including:
- Momentum, which measures price changes over time to identify trends.
- Moving averages, which smooth price data by calculating average prices over a set time period to filter out noise.
- Accumulative and reset accumulative averages, which calculate averages over all or reset periods of data.
- The drop-off effect, where simple moving averages are affected when oldest data values are removed from the calculation.
The document describes a stock market model called the "Fab Five" environmental model. The Fab Five model uses four main components - sentiment, monetary readings, combo, and tape (given double weight). Each component is made up of multiple indicators that are assigned values of +1, 0, or -1. The values are combined for each component and across components to assess overall market risk and determine whether conditions are bearish, neutral, or bullish. Examples of indicators include interest rates, market breadth, sentiment polls/surveys, and moving average crosses.
Model Risk Management | How to measure and quantify model risk?Genest Benoit
The aim of this paper is to present model risk situations and a methodology to measure and quantify the associated risk at model level, with different types of assumptions. Then, considering that in practice, a model risk management at model level is hardly feasible, this paper also outlines a method to measure and quantify model risk at risk category level (ex: Credit Risk).
In fact, one of the overarching drivers of this paper is to provide a model risk “value” which will enable you to analyse if the model risk is sufficiently covered. Indeed, although banks already allocate funds regarding this risk (portion of RWA attributed to conservative margins for credit risk, portion of Op risk Value at Risk, etc.), assessing the appropriateness of those funds remain complicated
Event: International Risk Management Conference - http://therisksociety.com
Lecture title: “Crude Oil Option Implied VaR and CvaR”
Date: June 14, 2016
Location: The Hebrew University of Jerusalem
Quick Reference Guide to BSA/AML Risk AssessmentMayank Johri
This document provides an overview of conducting an enterprise risk assessment for anti-money laundering (AML) compliance. It discusses key challenges such as sourcing and validating data from different business lines. The document recommends that financial institutions allocate a dedicated analytics team with AML expertise to automate the risk assessment process. Automating data extraction and leveraging statistical analysis can help set risk thresholds, calculate inherent risks and control effectiveness more robustly and efficiently. This allows the AML team to focus on qualitative work rather than manual data tasks. The end goal is to build a repeatable, defensible risk assessment model that identifies the institution's residual money laundering risks.
LITA Altmetrics and Digital Analytics Interest Group: 4/8/15Cody Behles
Cody Behles from the University of Memphis introduced altmetrics and the focus of the Altmetrics and Digital Analytics Interest Group. Altmetrics are non-traditional impact measures based on online activity that provide a broader view of impact beyond citations. Examples include social media mentions, blogs, mainstream media, and readership metrics. Potential issues with altmetrics include manipulation of data, difficulty standardizing across sources, and weak correlations with citations. The interest group aims to discuss successful integration of altmetrics into librarian tools and address concerns about their role in scholarly communication.
Cubism was an influential early 20th century avant-garde art movement that revolutionized European painting and sculpture. Led by Pablo Picasso and Georges Braque, Cubism involved depicting subjects from multiple viewpoints to represent the subject in a multidimensional way. Cubism was divided into two phases - Analytic Cubism from 1907 to 1912 focused on geometric forms, while Synthetic Cubism from 1912 to 1919 incorporated collage materials into compositions. Cubism's radical approach to depicting subjects without traditional perspective had a significant impact on modern art and design.
Ecuador tiene 4 regiones principales - la Sierra, la Costa, el Oriente y las Galápagos - cada una con características únicas que incluyen paisajes montañosos, cultura indígena, vida marina, volcanes y fauna. Las Galápagos son conocidas por sus tortugas gigantes y vida silvestre única, mientras que la Costa ofrece surf y diversión, la Sierra cultura y artesanía, y el Oriente la mayor reserva de biodiversidad del país.
Dokumen tersebut merupakan laporan rencana anggaran untuk melakukan survei lapangan selama 8 bulan dengan total biaya Rp. 26,5 miliar. Kegiatan utama meliputi persiapan dokumen, survei lokasi, mobilisasi, pematokan, pengukuran lapangan, pengolahan data, pembuatan peta, dan pelaporan. Biaya terbesar adalah untuk upah tenaga kerja seperti geodet, surveyor, dan asisten surveyor.
1. El documento analiza las posibilidades legales para impugnar dos procedimientos de ejecución coactiva.
2. Se concluye que el administrado puede solicitar la revisión judicial de la ejecución coactiva cuando esta viole el procedimiento legal, suspendiéndose temporalmente la ejecución para multas administrativas.
3. También se puede plantear un proceso de amparo si se violaron derechos constitucionales o el debido proceso, o negociar directamente la deuda con el ejecutor coactivo.
This document summarizes a student project on designing and analyzing pressure vessels using conventional and ASME standards methods. It includes:
- Design and analysis of pressure vessels using conventional design, 3D modeling, ANSYS analysis, and ASME code design using PV-Elite software.
- Comparison of designs from conventional versus ASME code methods to determine the safest and most economical approach.
- The project aims to avoid pressure vessel failures and accidents through optimized design and increased safety factors.
Thomas McNulty leads Navigant's commodity derivatives and hedging practice. He has over 30 years of experience in banking, corporate finance, risk management, and consulting. Navigant provides advisory services related to derivatives hedging strategies, model design, valuation, reporting, and compliance. Services include assessing risks, quantifying exposures, structuring hedges, executing trades, performing valuations, making credit adjustments, and monitoring hedge positions. Navigant uses quantitative modeling techniques like Monte Carlo simulation for assessments and has experience valuing over $9 billion in derivative instruments.
Derivatives and hedging advisory services july 2015Thomas J. McNulty
Tom McNulty leads Navigant's commodity derivatives and hedging practice. He has over 30 years of experience in banking, corporate finance, and consulting. Navigant provides advisory services related to derivatives hedging strategies, model design, valuation, reporting, and compliance. Services include assessing risks, quantifying exposures, developing hedge programs, executing trades, performing valuations, and stress testing portfolios. Navigant uses quantitative methods like Monte Carlo simulation to value instruments and make credit value adjustments.
Derivatives and hedging advisory services july 2015Thomas J. McNulty
Tom McNulty leads Navigant's commodity derivatives and hedging practice. He has over 30 years of experience in banking, corporate finance, risk management, and consulting. Navigant provides advisory services related to derivatives hedging strategies, model design, valuation, reporting, and compliance. Services include assessing risks, quantifying exposures, developing hedging plans, executing trades, performing valuations, making credit adjustments, and monitoring hedge positions. Navigant uses quantitative methods like Monte Carlo simulation and works with clients on novations, unwindings, and defaults.
Derivatives and hedging advisory services august 2015Thomas J. McNulty
Thomas McNulty leads Navigant's commodity derivatives and hedging practice. He has over 30 years of experience in banking, corporate finance, and consulting. Navigant provides advisory services related to hedge strategy, model design, valuation, reporting, and other areas for complex derivatives. McNulty has valued over $11 billion in derivative instruments and advises clients on complying with accounting standards and managing risks.
Derivatives and hedging advisory services july 2015Thomas J. McNulty
Thomas McNulty leads Navigant's commodity derivatives and hedging practice. He has over 30 years of experience in banking, corporate finance, and consulting. Navigant provides advisory services related to hedge strategy, model design, valuation, reporting, and more. McNulty has valued over $11 billion in derivative instruments and advises clients on complex issues related to derivatives.
Derivatives and hedging advisory services july 2015Thomas J. McNulty
Thomas McNulty leads Navigant's commodity derivatives and hedging practice. He has over 30 years of experience in banking, corporate finance, and consulting. Navigant provides advisory services related to hedge strategy, model design, valuation, reporting, and more. McNulty has valued over $11 billion in derivative instruments and advises clients on complex issues related to derivatives.
This document discusses applying quantitative analytics to regulatory compliance problems. It begins with an overview of common compliance challenges faced by banks, such as regulatory actions relating to compliance, fraud, sanctions, and money laundering. It then outlines a solution framework involving control metrics and reporting, enhanced data management and visualization, and learning from risk management disciplines. Examples are provided of applying analytics to know-your-customer processes, transaction monitoring, customer risk rating calculations, and model validation. Case studies demonstrate quantitative analytics for anti-money laundering transaction monitoring, customer risk rating calculation, and fraud detection.
Practical Aspects of Stochastic Modeling.pptxRon Harasym
The document provides an overview of stochastic modeling for actuaries. It defines stochastic modeling as a technique that uses random variables and simulations to model complex systems over time. The key advantages are the ability to study long-term outcomes under different scenarios and to better understand risk. Limitations include significant effort required and reliance on input assumptions. Stochastic modeling is preferred when risks are complex or path dependent. The document outlines the modeling steps and discusses concepts like the conditional tail expectation.
Xiaorong Zou has over 10 years of experience in model validation and risk management. She currently works as a Senior Manager at BMO Financial Group, where she manages a team that validates market risk models. Prior to this role, she worked as a lecturer teaching mathematics and finance courses. She has a PhD in Mathematics and masters degrees in Electrical Engineering, Actuarial Science, and Applied Math.
Khoo Guan Seng is the head of group risk models validation at Standard Chartered Bank. He gave a presentation on using Monte Carlo simulation techniques for investment risk and portfolio performance management. The presentation covered innovations with Monte Carlo methods, implementing the techniques in risk systems to enhance performance measurement, using simulations to mitigate risks through diversification, and validating Monte Carlo techniques. The overall objective is to minimize unexpected investment volatility and losses while maximizing consistent returns through flexible risk management.
Predictive modeling is a process used in predictive analytics to create statistical models that can forecast future outcomes based on historical data. Predictive analytics uses techniques from data mining, statistics, modeling, machine learning and AI to analyze current data to predict future events. The predictive modeling process involves collecting data, creating a model, testing and validating the model, and evaluating model performance. Predictive models are commonly used to predict customer behavior, risk levels, and other business outcomes.
Explore our students' cutting-edge project on predicting bank customer churn using advanced analytics techniques. This project employs machine learning algorithms to analyze customer data and forecast the likelihood of churn, offering valuable insights for financial institutions. Gain insights into customer retention strategies, predictive modeling, and the potential impact on banking operations. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
What we do; predictive and prescriptive analyticsWeibull AS
Prescriptive Analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.
Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.
Prescriptive analytics will also tell what probably will happen, but in addition: when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.
Dive deep into the world of insurance churn prediction with this captivating data analysis project presented by Boston Institute of Analytics. Our talented students embark on a journey to unravel the mysteries behind customer churn in the insurance industry, leveraging advanced data analysis techniques to forecast and anticipate customer behavior. From analyzing historical data and customer demographics to identifying predictive indicators and developing churn prediction models, this project offers a comprehensive exploration of the factors influencing insurance churn dynamics. Gain valuable insights and actionable recommendations derived from rigorous data analysis, presented in an engaging and informative format. Don't miss this opportunity to delve into the fascinating realm of data analysis and unlock new perspectives on insurance churn prediction. Explore the project now and embark on a journey of discovery with Boston Institute of Analytics. To learn more about our data science and artificial intelligence programs, visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
Bridging marke- credit risk-Modelling the Incremental Risk Charge.pptxGarima Singh Makhija
This document discusses modeling credit migration risk using a generator-based simulation approach. It outlines the key components of an incremental risk charge (IRC) model, including assigning positions to liquidity buckets, simulating rating transitions, pricing positions, and calculating profit and loss. The document discusses important modeling considerations like using through-the-cycle versus point-in-time transition data and calibrating to risk-neutral probabilities. It also provides mathematical background on representing rating transitions as a Markov process and using the generator matrix to describe time-dependent transition probabilities between discrete time periods. The goal is to develop a risk measurement model that is consistent with Basel capital requirements and can evaluate credit migration risk over a one-year horizon at a 99.9%
The document discusses building a machine learning model to predict customer churn for a telecommunications company using a dataset containing customer characteristics. It describes preprocessing the data, exploring the features, training various classification models including logistic regression, support vector machines, random forests and decision trees, and evaluating model performance. Logistic regression achieved the best results with 79% accuracy at predicting whether customers will churn. Future work could include reducing more features and testing additional models to improve accuracy for predicting telecom customer churn.
This document discusses predicting customer churn for a telecommunications company. It begins with an introduction to the problem and dataset, which contains information on 7,043 customers. It then preprocesses the data, which has 19 variables on demographic, account, and service characteristics. Various machine learning algorithms are trained and evaluated on the data, with logistic regression achieving the best accuracy of 79%. The document concludes with opportunities for future improvement and acknowledgments.
Himanshu Doneria is seeking a position focusing on financial risk modeling and algorithm development. He has a B.Tech and M.B.A in marketing and finance and over 7 years of experience in financial analysis, risk management, and quantitative modeling. His skills include programming in R and SAS, financial modeling, and business analytics. He is currently working as a financial analyst developing equity trading strategies and models.
In this study we survey practices and supervisory expectations for stress testing (ST), in a credit risk framework for banking book exposures. We introduce and motivate ST; and discuss the function, supervisory requirements and expectations, credit risk parameters, interpretation results
with respect to ST. This includes a typology of ST (uniform testing, risk factor sensitivities, scenario analysis; and historical, statistical and hypothetical scenarios) and procedures for con-ducting ST. We conclude with two simple and practical stress testing examples, one a ratings migration based approach, and the other a top-down ARIMA modeling approach.
Navigant valuation services accounting and taxation september 2015Thomas J. McNulty
This document provides an overview of valuation services for accounting and taxation purposes offered by Navigant Consulting. It discusses why valuations are needed, common required valuations for financial reporting and taxation, and risks and pricing considerations. Navigant focuses on providing thorough, accurate, and transparent valuations accepted by regulators and clients in complex industries like energy. Their strengths include expertise across industries, a collaborative process, integrity, and being a trusted advisor to clients.
Navigant provides valuation, advisory, and consulting services across the energy sector, including:
- Valuation of oil and gas assets, reserves, production contracts, and commodity derivatives
- Advisory services for lending, mergers and acquisitions, bankruptcies, and restructurings
- Technical upstream services such as reserves assessment, economic modeling, operations management, and asset management
- Derivatives valuation and hedging strategies, including commodity, interest rate, credit, and foreign exchange derivatives
- Why Navigant provides these services with over 3,500 professionals globally and expertise across the energy complex
Distressed production lending a stochastic responseThomas J. McNulty
The document discusses the use of stochastic analysis, such as Monte Carlo simulation, for evaluating oil and gas production loans during periods of commodity price volatility. Deterministic analyses are commonly used but do not adequately account for randomness and uncertainty. Stochastic models that incorporate probabilistic price forecasts and volatility are superior for assessing risk. Monte Carlo simulation can run thousands of simulations to provide a full range of potential valuation outcomes and borrowing base calculations under different price scenarios. This allows lenders and borrowers to better understand downside risks and make more informed decisions.
Navigant valuation services accounting and taxation oil and gas august 2015Thomas J. McNulty
The document provides an overview of valuation services for oil and gas companies for accounting, taxation, and commercial purposes. It discusses why valuations are needed, common valuation methods, and examples of required valuations for financial reporting, taxation, risks, pricing models, and essential success factors. The document also promotes Navigant Consulting as offering comprehensive energy industry expertise, experience providing valuations that stand up to regulatory scrutiny, and acting as a trusted advisor to clients.
Navigant valuation services disputes and litigation august 2015Thomas J. McNulty
Navigant Consulting provides valuation services for disputes and litigation. They have expertise in business valuation, shareholder oppression cases, business interruption damages, patent and non-patent intellectual property valuation, and economic analysis for securities cases. Navigant's strengths include their broad experience across industries, collaborative process, thorough and accurate work, and maintaining integrity as a trusted advisor rather than just trying to win cases.
Navigant valuation services accounting and taxation july 2015Thomas J. McNulty
212-220-4333
Direct: 212-220-4334
Cell: 646-334-5001
neeli.soulikohan@navigant.com
Professional History
- Navigant Consulting, Inc.
- Duff & Phelps, LLC
- Ernst & Young LLP
Education
- MBA, Finance, Columbia Business School
- BBA, Accounting, Baruch College
Neeli Souli Kohan is an Associate Director in the Valuation & Financial Risk Management practice of Navigant
Consulting, Inc. based in New York. She has over 10 years of experience providing valuation and financial
advisory services to public and private companies,
Navigant valuation services accounting and taxation july 2015Thomas J. McNulty
212-220-2900
Direct: 212-220-2901
Cell: 646-334-4982
neeli.soulikohan@navigant.com
Professional History
- Navigant Consulting, Inc.
- Duff & Phelps, LLC
- Ernst & Young LLP
Education
- MBA, Finance, Columbia Business School
- BA, Economics, Columbia University
Neeli Souli Kohan is an Associate Director in the Valuation & Financial Risk Management practice of Navigant
Consulting, Inc. based in New York. She has over 10 years of experience providing valuation and financial
advisory services to clients in a variety of industries including energy
Navigant valuation services disputes and litigation july 2015Thomas J. McNulty
Navigant Consulting provides valuation services for disputes and litigation. The document discusses Navigant's expertise in valuation for litigation purposes, including business valuation, shareholder oppression cases, intellectual property disputes, and more. It highlights Navigant's experience, credibility, and ability to clearly articulate findings to audiences like courts and juries. The document also outlines Navigant's comprehensive energy industry expertise and capabilities in valuing complex assets and securities.
Navigant valuation services accounting and taxation july 2015Thomas J. McNulty
This document provides an overview of valuation services for accounting and taxation purposes offered by Navigant Consulting. It discusses why valuations are needed, common required valuations for financial reporting and taxation, and risks and pricing considerations. Navigant focuses on providing thorough, accurate, and transparent valuations accepted by regulators and clients, leveraging its expertise across industries including energy. The company aims to serve as a trusted advisor through its collaborative approach and broad capabilities.
Navigant valuation services disputes and litigation june 2015Thomas J. McNulty
This document discusses valuation services for disputes and litigation provided by Navigant Consulting. It begins with an overview of the key considerations for any valuation, then focuses on valuations done for expert testimony in legal disputes. Examples of common types of valuations for disputes are provided. The document then discusses Navigant's capabilities and leadership in this practice area, emphasizing their experience, global reach, and independence compared to other firms. Resumes of several senior professionals demonstrate their qualifications.
Navigant valuation services accounting and taxation june 2015Thomas J. McNulty
Navigant Consulting provides valuation services for accounting and taxation purposes. Their services include valuations required for financial reporting, taxation, and other expert opinions. They have expertise in valuations across many industries, with a focus on energy companies. Navigant has experience providing valuations to meet requirements of various standards and for various uses such as M&A transactions, audit defense, and litigation.
The document discusses Navigant's new loan brokering effort. It combines traditional consulting expertise with loan trading experience and relationships. The team has backgrounds in consumer, residential, commercial real estate, and C&I loans, both performing and non-performing. Navigant positions itself as a fiduciary for clients, doing thorough analysis before any transactions. Recent qualifications include brokering various performing and non-performing residential and commercial loans, as well as student loans totaling over $550 million.
Navigant valuation services disputes and litigation april 2015Thomas J. McNulty
This document discusses valuation services for disputes and litigation provided by Navigant Consulting. It begins with an overview of the key considerations for any valuation, then focuses on valuations done for expert testimony in legal disputes. Examples of types of valuations for disputes are provided. Risks involved and how Navigant prices its services are mentioned. Success factors for expert testimony valuations are listed. Sections 2 and 3 discuss why clients choose Navigant, the firm's strengths and leadership. Resumes of several Navigant directors and their experience are also included.
Navigant valuation services commercial situations april 2015Thomas J. McNulty
This document provides an overview of Navigant Consulting's valuation services for commercial situations. It discusses why valuations are needed, types of commercial valuations including examples like fairness opinions and solvency opinions. It outlines Navigant's capabilities including experience across industries, quantitative finance skills, and the ability to deploy resources globally. The document introduces key leaders of Navigant's valuation practice and their backgrounds. It positions Navigant as a trusted advisor able to provide objective, thorough valuations and distinguishes Navigant from competitors based on its scale, expertise, and independence.
Navigant valuation services commercial situations march 2015Thomas J. McNulty
This document provides an overview of Navigant Consulting's valuation services for commercial situations. It discusses why valuations are needed, common types of commercial valuations including fairness opinions and solvency opinions. It highlights Navigant's expertise in various industries including energy, their leadership team, and capabilities in valuation models and risk analysis. Navigant positions itself as a trusted advisor able to provide independent, accurate valuations utilizing their industry and technical experience.
This document provides an overview of quantitative finance advisory services. Section 1 defines quantitative finance and its applications in areas like corporate finance, derivatives pricing, and risk management. Section 2 outlines typical advisory services including sensitivity analysis, forecasting, risk assessments, and modeling. Section 3 lists technical competencies including software skills and expertise in areas like fixed income, credit risk, stochastic volatility, and energy derivatives. Section 4 provides background on Navigant Consulting, a specialized advisory firm that could provide these quantitative finance services.
This document provides an overview of Quantitative Finance Advisory Services (QFAS) and the types of advisory services offered. Section 1 defines quantitative finance and its applications in areas like corporate finance, risk management, and valuation. Section 2 lists typical advisory services such as earnings and cash flow analysis, hedging strategies, and structuring derivative instruments. Section 3 outlines technical competencies including software skills, fixed income products, credit risk modeling, and energy derivatives. Section 4 discusses why clients choose Navigant for its expertise across industries, collaborative approach, and track record. Section 5 provides brief biographies of practice leaders Richard Hitt and Thomas McNulty.
Navigant valuation services disputes and litigation march 2015Thomas J. McNulty
Navigant Consulting provides valuation services for disputes and investigations. They have expertise valuing a wide range of assets and securities, especially those related to energy and commodities. Their strengths include industry experience, collaboration with clients, thoroughness, and integrity. They aim to serve as trusted advisors and have a strong reputation in the marketplace from work accepted by auditors, regulators, and rating agencies. The practice is led by Managing Director Richard Hitt from their Chicago office, who has over 25 years of experience valuing financial and energy companies.
Navigant valuation services commercial situations march 2015Thomas J. McNulty
This document provides an overview of Navigant Consulting's valuation services for commercial situations. It discusses why valuations are needed, common types of commercial valuations including fairness opinions and solvency opinions. It highlights Navigant's expertise in various industries including energy, their leadership team, and capabilities in valuation models and risk analysis. Navigant positions itself as a trusted advisor able to provide objective valuations with experience in both corporate and regulatory settings.
Navigant valuation services accounting and taxation march 2015Thomas J. McNulty
The document provides information on valuation services from Navigant, a specialized advisory firm. It discusses the types of valuations Navigant performs, including those required for accounting, taxation, financial reporting, and commercial purposes. It highlights Navigant's expertise in valuations across various industries, such as energy, and capabilities in performing valuations of different asset classes. The document also profiles Navigant's strengths, such as its experienced professionals and collaborative process, and distinguishes the firm from competitors like the Big 4 accounting firms and boutique valuation firms.
Vicinity Jobs’ data includes more than three million 2023 OJPs and thousands of skills. Most skills appear in less than 0.02% of job postings, so most postings rely on a small subset of commonly used terms, like teamwork.
Laura Adkins-Hackett, Economist, LMIC, and Sukriti Trehan, Data Scientist, LMIC, presented their research exploring trends in the skills listed in OJPs to develop a deeper understanding of in-demand skills. This research project uses pointwise mutual information and other methods to extract more information about common skills from the relationships between skills, occupations and regions.
Lecture slide titled Fraud Risk Mitigation, Webinar Lecture Delivered at the Society for West African Internal Audit Practitioners (SWAIAP) on Wednesday, November 8, 2023.
Abhay Bhutada, the Managing Director of Poonawalla Fincorp Limited, is an accomplished leader with over 15 years of experience in commercial and retail lending. A Qualified Chartered Accountant, he has been pivotal in leveraging technology to enhance financial services. Starting his career at Bank of India, he later founded TAB Capital Limited and co-founded Poonawalla Finance Private Limited, emphasizing digital lending. Under his leadership, Poonawalla Fincorp achieved a 'AAA' credit rating, integrating acquisitions and emphasizing corporate governance. Actively involved in industry forums and CSR initiatives, Abhay has been recognized with awards like "Young Entrepreneur of India 2017" and "40 under 40 Most Influential Leader for 2020-21." Personally, he values mindfulness, enjoys gardening, yoga, and sees every day as an opportunity for growth and improvement.
5 Tips for Creating Standard Financial ReportsEasyReports
Well-crafted financial reports serve as vital tools for decision-making and transparency within an organization. By following the undermentioned tips, you can create standardized financial reports that effectively communicate your company's financial health and performance to stakeholders.
Solution Manual For Financial Accounting, 8th Canadian Edition 2024, by Libby...Donc Test
Solution Manual For Financial Accounting, 8th Canadian Edition 2024, by Libby, Hodge, Verified Chapters 1 - 13, Complete Newest Version Solution Manual For Financial Accounting, 8th Canadian Edition by Libby, Hodge, Verified Chapters 1 - 13, Complete Newest Version Solution Manual For Financial Accounting 8th Canadian Edition Pdf Chapters Download Stuvia Solution Manual For Financial Accounting 8th Canadian Edition Ebook Download Stuvia Solution Manual For Financial Accounting 8th Canadian Edition Pdf Solution Manual For Financial Accounting 8th Canadian Edition Pdf Download Stuvia Financial Accounting 8th Canadian Edition Pdf Chapters Download Stuvia Financial Accounting 8th Canadian Edition Ebook Download Stuvia Financial Accounting 8th Canadian Edition Pdf Financial Accounting 8th Canadian Edition Pdf Download Stuvia
Seminar: Gender Board Diversity through Ownership NetworksGRAPE
Seminar on gender diversity spillovers through ownership networks at FAME|GRAPE. Presenting novel research. Studies in economics and management using econometrics methods.
BONKMILLON Unleashes Its Bonkers Potential on Solana.pdfcoingabbar
Introducing BONKMILLON - The Most Bonkers Meme Coin Yet
Let's be real for a second – the world of meme coins can feel like a bit of a circus at times. Every other day, there's a new token promising to take you "to the moon" or offering some groundbreaking utility that'll change the game forever. But how many of them actually deliver on that hype?
OJP data from firms like Vicinity Jobs have emerged as a complement to traditional sources of labour demand data, such as the Job Vacancy and Wages Survey (JVWS). Ibrahim Abuallail, PhD Candidate, University of Ottawa, presented research relating to bias in OJPs and a proposed approach to effectively adjust OJP data to complement existing official data (such as from the JVWS) and improve the measurement of labour demand.
Independent Study - College of Wooster Research (2023-2024) FDI, Culture, Glo...AntoniaOwensDetwiler
"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
My study abroad in Bali, Indonesia, inspired this research topic as I noticed how globalization is changing the culture of its people. I learned their language and way of life which helped me understand the beauty and importance of cultural preservation. I believe we could all benefit from learning new perspectives as they could help us ideate solutions to contemporary issues and empathize with others.
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
How Does CRISIL Evaluate Lenders in India for Credit RatingsShaheen Kumar
CRISIL evaluates lenders in India by analyzing financial performance, loan portfolio quality, risk management practices, capital adequacy, market position, and adherence to regulatory requirements. This comprehensive assessment ensures a thorough evaluation of creditworthiness and financial strength. Each criterion is meticulously examined to provide credible and reliable ratings.
STREETONOMICS: Exploring the Uncharted Territories of Informal Markets throug...sameer shah
Delve into the world of STREETONOMICS, where a team of 7 enthusiasts embarks on a journey to understand unorganized markets. By engaging with a coffee street vendor and crafting questionnaires, this project uncovers valuable insights into consumer behavior and market dynamics in informal settings."
6. What QFAS is not…………………
It is not Accounting,
It is not Business Valuation,
It is not Investment Banking,
It is not theoretical, it is scientific, and
Above all, it is NOT a cure for poor management, fraud, and/or weak leadership.
Quantitaive Finance must be coupled with qualitative, right-brain
thinking to be most effective.
8. Typical Advisory Services
• Earnings and cash flow sensitivity and volatility analysis,
• Cash flow forecasting and optimization,
• Market risk assessments,
• Pricing reviews and optimization studies,
• Client profitability optimization and segmentation,
• Quantitatively-driven operational transformation projects,
• Hedging strategies,
• Model validation,
• Curve construction,
• Limits setting, testing, & consulting,
• Model risk assessment, and
• Structuring & valuation of exotic derivative instruments.
9. Sample Applications
Fourier Analysis
Navigant can assist Clients to develop mathematical methods to decompose
data into specific frequency elements. This technique can be used to analyze
and price daily, weekly, and monthly variations in electric power demand, for
example.
Laplace Transform/Moment Analysis
Navigant can assist Clients to develop an appropriate mathematical method,
and model, to decompose data into its statistical moments, such as
distribution, kurtosis, and skewness. This capability allows for superior risk
models that go well beyond traditional VaR, CFaR, or mVaR; and advance the
ball towards a better grasp of “Black Swan” events, which are the most
devastating events of all.
10. Sample Applications cont’d
Differential Equations & Multivariate Regression Analysis
Navigant can assist Clients with solutions for equations that have first, second,
and higher order derivative relationships across multiple variables. This can
be essential in determining how various drivers impact outputs. For example,
this is the best way to properly model wholesale refining margins.
Factor Analysis
This is a statistical method that can be used to determine key unobserved
and/or underlying correlations between several variables. For example, for a
retail electric provider, Factor Analysis can be used to do a more accurate job
of quantifying customer profitability analysis.
11. Sample Applications cont’d
Error Propagation
Navigant can assist Clients with the development of uncertainty propagation
models, in order to determine the specific impact of variable variances,
correlations, and volatilities. This is useful in doing more sophisticated
EBTIDAX or cash flow volatility analysis based on the uncertainty of drivers
in the upstream oil & gas business.
Integer Optimization
This is an optimization technique that uses discrete constraints. It is easier to
optimize continuous parameters, but discrete constraints (like volume or
capacity) complicates optimization models and makes them slow to run. Plant
optimization and discrete market simulation (as in power markets) are two
examples of when this technique can be effective.
12. Sample Applications cont’d
Stochastic Probability Matrices
In simple terms, this service means that we help Clients to list and then map
out every possible event that might occur. Then, we work to assign a
probability to each of them. This work is coupled with expected valuations
per event, and is very useful in the analysis of commodity markets, cross-
correlation analysis for bank liquidity, as well as working capital optimization.
Stochastic Processes & Monte Carlo Simulation
Using software, or coding in Excel, Navigant can build simulation models
using random variables to determine their impact on important financial and
operational metrics. Specifically, hedging and trading strategies are well
suited to this approach.
14. Stochastic Market Analysis
How It Works
• Using SQL Server, we build as detailed a market model as possible of all
market inputs, which impact the Client’s operations (Midstream Crude Oil,
Polyethylene, ERCOT, etc.),
• Also using SQL Server, we prepare an output data file to store all market
data outputs,
• MCS is used to run simulations of various market strategies,
pulling/pushing on data in the SQL data bases, and then
• XLS is used to prepare summary output tables and metrics for leadership
discussions.
Applications
• Test M&A deals and their market impact,
• Test pricing strategies,
• Shock the market, and
• Quantify strategy and test its valuation impact.
17. Technical Competencies
Fixed-income products
• Fixed and floating rate instruments, bonds, swaps, caps and floors, FRAs and other delta
products.
• Yield, duration and convexity calculations.
• Bootstrapping to build up the yield curve from trading bonds and swaps.
• Curve stripping - Using reference rates & basis spreads, OIS discounting and dual-curve
stripping, cross-currency basis curves, and calculating the cost of funds.
• Interpolation methods, including piece wise constant forwards, piece wise linear, cubic
splines, smart quadratics, quartics, and monontone convex splines.
• Stochastic interest rate models, one and two factors.
• Model Calibration - Fitting the yield curve in simple models.
• Data analysis - Examining interest rate and yield curve data to find the best model.
18. Technical Competencies cont’d
Probabilistic methods for interest rates
• Using the Heath, Jarrow and Morton model, & modeling the yield curve.
• Determining risk factors of yield curve evolution and optimal volatility structure.
• Pricing interest rate derivatives by Monte Carlo Simulation.
• The Libor Market Model, calibrating the reference volatility structure by fitting to caplet or
swaption data.
• The SABR Model - Managing volatility risks, smiles, local volatility models, using the
SABR model and hedging stability.
• The Arbitrage Free SABR model - Reduction to the effective forward equation, arbitrage
free boundary conditions, comparison with historical data and hedging with the SABR
model.
19. Technical Competencies cont’d
Credit risk and credit derivatives
• Structural and Intensity models used for credit risk.
• CDS pricing, market approaches, and implied default probability, recovery rates, & default
time modeling.
• Synthetic CDO pricing, using the default probability distribution, default correlation,
tranche sensitivity, & pricing spreads.
• CDO/copula modeling using XLS spreadsheets.
• Correlation and state dependence analysis.
• Credit Valuation Adjustment (“CVA”): CVA exposure, modeling exposure, collateral,
wrong way risk & right way risk.
• Risk of default - The hazard rate, the implied hazard rate, the stochastic hazard rate and
credit rating, & capital structure arbitrage.
• Copulas - Pricing basket credit instruments by simulation.
• Statistical methods in estimating default probability - Ratings migration, transition
matrices, and Markov processes.
20. Technical Competencies cont’d
Stochastic volatility and jump diffusion
• Modeling and empirical evidence, pricing and hedging, mean-variance analysis, the
Merton model, jump distributions, expectations and worst case analysis.
• Non-probabilistic models - Uncertainty in parameter values versus randomness in
variables, & nonlinear equations.
• Static hedging - Hedging exotic target contracts with exchange-traded vanilla contracts,
optimal static hedging.
• Advanced Monte Carlo techniques - Low-discrepancy series for numerical quadrature.
Used for option pricing, speculation, and scenario analysis.
21. Technical Competencies cont’d
Energy derivatives
• Speculation using energy derivatives and risk management in energy derivatives.
• Cointegration - Modeling long term relationships, and statistical arbitrage using mean
reversion.
• Dynamic Asset Allocation - Convexity management, stochastic control, multi-period
projections, utility maximization, and the impact of transaction costs.
• Forecasting by using option prices - Volatility forecasting using historical asset prices and
current option prices.
• Inserting option prices into ARCH models, & typical ARCH results.
• Density forecasting - Criteria for good forecasts, estimating risk-neutral densities from
option prices, & risk-neutral to real-world densities.
23. Navigant Consulting, Inc.
Business Description
Navigant Consulting, Inc. is a
specialized, independent
advisory firm that supports
companies, lenders,
institutional investors, legal
counsel, and government
agencies. The company focuses
on entities and industries
facing the challenges of
uncertainty, risk, distress and
significant change, and on the
issues driving these
transformations.
Ticker NCI (NYSE)
2014 Revenue $859.6 million
Professionals 3,600+
Headquarters Chicago, IL
Senior Management
■ Julie Howard, CEO and
Chairman, Navigant
■ Cindy Baier, CFO and EVP
■ Richard D. Hitt, Managing
Director, Head of Navigant’s
Corporate Finance Practice
Navigant Consulting, Inc. (“Navigant”)
NCI’s 2,500+ professionals maintain a presence in 45 cities internationally
throughout North America, Europe and Asia, including a significant concentration of
resources in Chicago, Houston, London, New York, and Washington DC.
24. Comprehensive Energy Complex Coverage
Commodity Hedge Funds
Energy Services
Energy Trading & Marketing
LNG
Oil and Gas - Upstream
Oil and Gas – Midstream
Power Generation, Transmission, & Distribution
Refined Products
Renewable Energy
25. Our Strengths
Broad and deep expertise across the Energy Complex
We have consulting, banking, and corporate experience in diverse financial and commodity
markets, in addition to valuation & financial reporting technical capabilities. Our experienced
team includes MBAs, CPAs, CFAs, CBAs, ASAs, CAIAs, and MAI’s.
We are a one-stop shop, as we do complex commodity derivatives valuations alongside our
business and corporate valuation work.
Collaborative process
While our opinions are independent, you are involved throughout our process, because you
know your investments better than anyone. We want to understand your thinking, and we want
you to understand ours.
Thoroughness and accuracy
We give you more than just the conclusions. We give you thoroughly supported, accurate
valuation reports.
Integrity
We are only interested in getting to the right answer, employing generally accepted valuation
principles.
“Trusted Advisor” to our clients
Our clients view us as business partners, working together to establish best practices in valuation
and transaction work as competitive advantages for their firms.
What makes Navigant unique?
26. Accepted in the Marketplace
Our work has supported KPMG, PwC, Deloitte, E&Y, BDO, GT, and other
audit firms’ technical & audit staffs, undergoing detailed review of our fair
value analyses for financial reporting and tax purposes.
Our work has been reviewed and accepted by the SEC in numerous filings.
We have extensive experience defending our valuations before the IRS
(including Tax Court) and other tax and regulatory bodies.
Our senior professionals have performed technical reviews during their
tenure at the major accounting firms. We maintain continuing professional
relationships with current reviewers at these firms.
Our methodologies and findings have been presented to, and accepted by
numerous rating agencies, qualified institutional buyers and certified
investors.
Our professionals have published and spoken extensively on valuation
topics as they pertain to the investment community.
27. Trusted Advisor
Service
A combination of…..
… resulting in a “Trusted Advisor” relationship creating
valuation as a competitive advantage.
Flexibility
Collaboration
Accuracy
Thoroughness
Timeliness
Integrity
Expertise
28. “Excellent communication”
“Very responsive from a process and analysis standpoint”
“Exceeds expectations in response time “
“Investors have reached out to Navigant on methods and assumptions and have only
provided positive feedback”
“Meets all schedules and deadlines”
“Provides creative ideas and suggestions”
References available upon request
What our clients say about us….
29. Navigant vs. Big 4
• We have a diverse skill set; not just CPAs,
• We have corporate industry experience, they do not,
• QF is a discipline based on Applied Math, Economics
and Finance, not on Accounting, and
• We are not limited by rules-based thinking and
checklists.
30. Navigant vs. Wall Street
• We do not have trading and marketing desks, so we
can be more objective,
• We understand regulatory and statutory valuation,
bankers sometimes do not, and
• We understand all of the accounting and tax rules
that are unique to the Energy Complex.
31. Navigant vs. Boutique Firms
• Our balance sheet and brand stand behind our work,
• We can deploy a wide variety of resources globally,
• We do not have to outsource or sub-contract
anything, we are a one-stop shop,
• Small firms cannot be counted on to be there with
you through all of your regulatory challenges, and
• Energy boutiques do not possess the quantitative
finance horsepower needed to be effective.
33. Thomas J. McNulty
Director
Navigant Consulting
909 Fannin St. Suite 1900
Houston, TX 7010
Tel: 713.646.5078
Cell: 832.472.3717
thomas.mcnulty@navigant.com
Professional History
Navigant Consulting, Inc.
Sirius Solutions LLP
Plains All American
Duke Energy
Enron International
US Foreign Service
Brown Brothers Harriman
Education
M.B.A. (Accounting & Finance),
Kellogg, Northwestern University
B.A. (History), Yale University
Tom McNulty is a Director in the Valuation & Financial Risk Management practice of Navigant Consulting,
Inc. His specialties are transaction and commercial analytics, valuation, and risk management. Specifically, his
practice offers clients a unique combination of strategic, financial, and transactional advisory services. Mr.
McNulty is based out of the Houston office, and he has more than 20 years of financial advisory and related
experience work.
As a consultant, Mr. McNulty has advised clients drawing on his valuation, transactions, risk management,
treasury and international credentials. Specifically, he has assisted with the valuation of securities, business
units, acquisition targets, derivatives, and equity incentive plans. In industry, and as an advisor, he has
worked on more than $38 billion in MD&A deals and has valued more than $8.5 billion notional in futures,
options, and other derivatives.
In his current role at Navigant Consulting, Mr. McNulty directs engagements governing acquisitions and
divestitures throughout the energy complex, including upstream oil & gas, midstream, merchant power,
renewable energy, energy services, trading and marketing, as well as LNG. He also performs valuation work
for clients with complex equity, fixed income, and commodity derivative instruments.
Recent assignments Mr. McNulty has led include:
• Provided ASC 815 assistance to several energy companies related to their derivatives portfolios.
• Rendered a comfort opinion to a energy hedge offering a new midstream oil & gas fund.
• Rendered an opinion to an oilfield services company for a related party transaction.
• Directed ASC 805 project for the reverse merger of two private drilling companies.
• Provided ASC 718 valuation assistance to a publicly traded oilfield services company for its restricted stock
units and stock options.
• Delivered ASC 718 valuation assistance to a privately held upstream oil & gas company for its equity
incentive plan.
• Led the valuation work on the GP interest in a privately-held midstream company for a large litigation
engagement.
Resumes