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 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.
The concept of the Security Market Line is very popular for portfolio management. It helps to derive the pricing of risky securities by plotting their expected returns.
To know more about it, click on the link given below:
https://efinancemanagement.com/investment-decisions/security-market-line
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.
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.
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.
This document provides an overview of asset-liability management (ALM) systems for banks and financial institutions. It discusses why ALM is important due to factors like globalization, deregulation, and integration of markets. The key objectives of ALM are to manage liquidity risk, interest rate risk, currency risk, and to aid in profit planning and growth projections. Specific risks like credit, market and operational risks are also discussed. The document outlines the ALM process, including generating statements to measure liquidity mismatches and interest rate sensitivity over different time periods. Tools for analyzing liquidity and interest rate risk are also presented. Overall organizational structure for effective ALM implementation is emphasized.
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.
The concept of the Security Market Line is very popular for portfolio management. It helps to derive the pricing of risky securities by plotting their expected returns.
To know more about it, click on the link given below:
https://efinancemanagement.com/investment-decisions/security-market-line
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.
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.
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.
This document provides an overview of asset-liability management (ALM) systems for banks and financial institutions. It discusses why ALM is important due to factors like globalization, deregulation, and integration of markets. The key objectives of ALM are to manage liquidity risk, interest rate risk, currency risk, and to aid in profit planning and growth projections. Specific risks like credit, market and operational risks are also discussed. The document outlines the ALM process, including generating statements to measure liquidity mismatches and interest rate sensitivity over different time periods. Tools for analyzing liquidity and interest rate risk are also presented. Overall organizational structure for effective ALM implementation is emphasized.
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.
This document discusses relative strength investment strategies. It finds that relative strength portfolios outperform benchmarks in 70% of years and returns are persistent over time. Adding a trend following parameter to dynamically hedge the portfolio decreases both volatility and drawdown. Momentum strategies have been used for over a century and relative strength is one of the most researched strategies. The document tests relative strength models on US equity sector portfolios and global asset classes.
Technical analysis, market efficiency, and behavioral financeBabasab Patil
Technical analysis uses patterns in stock prices and trading volume to predict future market movements and identify trading opportunities. The efficient market hypothesis states that stock prices instantly reflect all available information, making technical analysis ineffective. However, behavioral finance suggests psychological factors influence investor decisions and market anomalies exist, challenging the notion of complete market efficiency.
The beta coefficient is a form of measurement for volatile movement in an individual stock, as well as systematic risk in comparison to comparable stocks or the wider market.
Beta is a representation of the trajectory output of the slop calculated through regression analysis of a particular stock vs sector vs wider market. http://blugm.com
This document discusses Value at Risk (VaR) and how it can be used by client advisors, sales/brokerage teams, and senior management to assess portfolio risks. VaR measures the maximum potential loss of a portfolio over a time period, given a probability. It allows risks across different asset types to be measured together. The document outlines how VaR is calculated using historical volatility and correlation data to project a range of possible future portfolio values. It also discusses how options are incorporated into VaR using measures like delta, gamma, and theta to account for non-normal return distributions. The overall aim is to inform readers about risk measurement and how VaR can help mitigate risks for clients.
Outlook and market survey on the fresh Standards for Minimum capital requirements for market risk, published January 14th, 2016.
FRTB will deeply impact banks on IT, process, organization and human aspects.
CH&Co can help banks cope with these changes.
Fundamental analysis involves analyzing macroeconomic conditions, industries, and individual companies. At the macroeconomic level, factors like GDP growth, inflation, interest rates, and fiscal/monetary policies are examined. Industry analysis evaluates the attractiveness of industries based on their growth stage, competitive environment, and sensitivity to economic cycles. Finally, company analysis assesses the financial statements, management quality, and competitive positioning of specific firms. Together, this three-tiered fundamental analysis helps investors evaluate investment opportunities.
Algorithmic strategy with adoptable trading frequency, effectively works with relatively inefficient markets. To the attention of potential investors/partners.
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.
The document analyzes the bank performance of Wells Fargo using the CAMELS framework. It provides a history of Wells Fargo and a SWOT analysis. The CAMELS analysis finds that Wells Fargo is well capitalized with strong capital ratios exceeding minimum requirements. Asset quality has improved in the last quarter with reductions in nonperforming loans and charge-offs. Management quality is assessed as high based on financial performance and risk management.
This document discusses financial information markets and the efficient market hypothesis. It introduces the efficient market hypothesis, which states that financial markets are informationally efficient and prices instantly reflect all available information. The document then discusses the debate between the efficient market view and the asymmetric information view. The asymmetric information perspective is that some market players have better information than others, which can lead to pockets of market inefficiency. Problems that can arise from informational asymmetries, like adverse selection and moral hazard, are also summarized.
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 market efficiency and the efficient market hypothesis (EMH). It defines market efficiency as when market prices impartially estimate true investment value. For a market to be efficient, price deviations from true value must be random and uncorrelated with other value factors. The document also outlines the implications of EMH, such as no consistent strategies beating the market, and discusses criticisms like the internal contradiction of investors seeking inefficiencies. It concludes that perfect efficiency is unlikely due to human emotions and differing investor valuations.
Corporations forecast exchange rates for several reasons: to decide on financing in foreign currencies, hedging foreign cash flows, investing in foreign projects, and having foreign subsidiaries remit earnings. There are four main types of forecasting techniques: technical analysis uses historical data, fundamental analysis uses economic factors, market-based analysis uses current spot and forward rates, and mixed forecasting combines approaches. Corporations evaluate forecasts over time by measuring absolute forecast errors to assess bias and accuracy. Exchange rate volatility is also forecast to specify a confidence interval around point estimates.
This document provides information about an upcoming conference on the Fundamental Review of the Trading Book. It lists the speakers and panelists that will be participating. It also provides an overview of the conference topics, which include regulatory timelines, the sensitivities based approach, incremental default risk modelling, model risk management, VaR vs expected shortfall approaches, and challenges around non-modellable risk factors, profit and loss attribution, and desk eligibility. The document provides logistical information about the conference including dates, location, sponsors, and discounts.
Technical analysis is a method of evaluating securities using historical price and volume data rather than fundamental factors like intrinsic value. It relies on the assumptions that markets discount all known information, prices move in trends, and history tends to repeat itself. Technicians analyze charts looking for patterns and trends to predict future price movements. Key concepts include identifying uptrends and downtrends using higher highs/lows and lower highs/lows, analyzing different trend lengths, and identifying support and resistance levels where prices tend not to fall or rise beyond. Understanding trends and support/resistance allows technicians to trade in the direction of the prevailing trend.
This document discusses mortgage pipeline risk management. It begins by defining different types of risk associated with hedging mortgage pipelines, including interest-rate risk and fallout risk. It then provides an example of a secondary marketing report card that reviews secondary marketing operations on an ongoing basis. The report card covers areas like hedge position management, fallout analysis, and trading. It emphasizes the importance of accurately measuring performance in these areas through techniques like back-testing hedge ratios and fallout models.
Retail banking refers to banking services offered directly to consumers rather than other banks or corporations. It is characterized by multiple products, distribution channels, and customer groups. Common retail banking products in India include loans for housing, vehicles, education, and consumption. Growth in the Indian retail banking sector is supported by factors such as rising incomes, changing demographics, and technological advancement.
This document discusses several mega trends impacting retail banking, including increasing regulatory requirements, legacy technology issues, customer behavior changes, and industry consolidation. It notes these trends are driving up costs and changing customer expectations. The document provides examples of these trends, such as new banking regulations and their high costs of implementation. It suggests banks need to transform by adopting new approaches, technologies, mindsets and paradigms to address these challenges, improve customer experience, and adapt to new competitors.
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.
This document discusses relative strength investment strategies. It finds that relative strength portfolios outperform benchmarks in 70% of years and returns are persistent over time. Adding a trend following parameter to dynamically hedge the portfolio decreases both volatility and drawdown. Momentum strategies have been used for over a century and relative strength is one of the most researched strategies. The document tests relative strength models on US equity sector portfolios and global asset classes.
Technical analysis, market efficiency, and behavioral financeBabasab Patil
Technical analysis uses patterns in stock prices and trading volume to predict future market movements and identify trading opportunities. The efficient market hypothesis states that stock prices instantly reflect all available information, making technical analysis ineffective. However, behavioral finance suggests psychological factors influence investor decisions and market anomalies exist, challenging the notion of complete market efficiency.
The beta coefficient is a form of measurement for volatile movement in an individual stock, as well as systematic risk in comparison to comparable stocks or the wider market.
Beta is a representation of the trajectory output of the slop calculated through regression analysis of a particular stock vs sector vs wider market. http://blugm.com
This document discusses Value at Risk (VaR) and how it can be used by client advisors, sales/brokerage teams, and senior management to assess portfolio risks. VaR measures the maximum potential loss of a portfolio over a time period, given a probability. It allows risks across different asset types to be measured together. The document outlines how VaR is calculated using historical volatility and correlation data to project a range of possible future portfolio values. It also discusses how options are incorporated into VaR using measures like delta, gamma, and theta to account for non-normal return distributions. The overall aim is to inform readers about risk measurement and how VaR can help mitigate risks for clients.
Outlook and market survey on the fresh Standards for Minimum capital requirements for market risk, published January 14th, 2016.
FRTB will deeply impact banks on IT, process, organization and human aspects.
CH&Co can help banks cope with these changes.
Fundamental analysis involves analyzing macroeconomic conditions, industries, and individual companies. At the macroeconomic level, factors like GDP growth, inflation, interest rates, and fiscal/monetary policies are examined. Industry analysis evaluates the attractiveness of industries based on their growth stage, competitive environment, and sensitivity to economic cycles. Finally, company analysis assesses the financial statements, management quality, and competitive positioning of specific firms. Together, this three-tiered fundamental analysis helps investors evaluate investment opportunities.
Algorithmic strategy with adoptable trading frequency, effectively works with relatively inefficient markets. To the attention of potential investors/partners.
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.
The document analyzes the bank performance of Wells Fargo using the CAMELS framework. It provides a history of Wells Fargo and a SWOT analysis. The CAMELS analysis finds that Wells Fargo is well capitalized with strong capital ratios exceeding minimum requirements. Asset quality has improved in the last quarter with reductions in nonperforming loans and charge-offs. Management quality is assessed as high based on financial performance and risk management.
This document discusses financial information markets and the efficient market hypothesis. It introduces the efficient market hypothesis, which states that financial markets are informationally efficient and prices instantly reflect all available information. The document then discusses the debate between the efficient market view and the asymmetric information view. The asymmetric information perspective is that some market players have better information than others, which can lead to pockets of market inefficiency. Problems that can arise from informational asymmetries, like adverse selection and moral hazard, are also summarized.
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 market efficiency and the efficient market hypothesis (EMH). It defines market efficiency as when market prices impartially estimate true investment value. For a market to be efficient, price deviations from true value must be random and uncorrelated with other value factors. The document also outlines the implications of EMH, such as no consistent strategies beating the market, and discusses criticisms like the internal contradiction of investors seeking inefficiencies. It concludes that perfect efficiency is unlikely due to human emotions and differing investor valuations.
Corporations forecast exchange rates for several reasons: to decide on financing in foreign currencies, hedging foreign cash flows, investing in foreign projects, and having foreign subsidiaries remit earnings. There are four main types of forecasting techniques: technical analysis uses historical data, fundamental analysis uses economic factors, market-based analysis uses current spot and forward rates, and mixed forecasting combines approaches. Corporations evaluate forecasts over time by measuring absolute forecast errors to assess bias and accuracy. Exchange rate volatility is also forecast to specify a confidence interval around point estimates.
This document provides information about an upcoming conference on the Fundamental Review of the Trading Book. It lists the speakers and panelists that will be participating. It also provides an overview of the conference topics, which include regulatory timelines, the sensitivities based approach, incremental default risk modelling, model risk management, VaR vs expected shortfall approaches, and challenges around non-modellable risk factors, profit and loss attribution, and desk eligibility. The document provides logistical information about the conference including dates, location, sponsors, and discounts.
Technical analysis is a method of evaluating securities using historical price and volume data rather than fundamental factors like intrinsic value. It relies on the assumptions that markets discount all known information, prices move in trends, and history tends to repeat itself. Technicians analyze charts looking for patterns and trends to predict future price movements. Key concepts include identifying uptrends and downtrends using higher highs/lows and lower highs/lows, analyzing different trend lengths, and identifying support and resistance levels where prices tend not to fall or rise beyond. Understanding trends and support/resistance allows technicians to trade in the direction of the prevailing trend.
This document discusses mortgage pipeline risk management. It begins by defining different types of risk associated with hedging mortgage pipelines, including interest-rate risk and fallout risk. It then provides an example of a secondary marketing report card that reviews secondary marketing operations on an ongoing basis. The report card covers areas like hedge position management, fallout analysis, and trading. It emphasizes the importance of accurately measuring performance in these areas through techniques like back-testing hedge ratios and fallout models.
Retail banking refers to banking services offered directly to consumers rather than other banks or corporations. It is characterized by multiple products, distribution channels, and customer groups. Common retail banking products in India include loans for housing, vehicles, education, and consumption. Growth in the Indian retail banking sector is supported by factors such as rising incomes, changing demographics, and technological advancement.
This document discusses several mega trends impacting retail banking, including increasing regulatory requirements, legacy technology issues, customer behavior changes, and industry consolidation. It notes these trends are driving up costs and changing customer expectations. The document provides examples of these trends, such as new banking regulations and their high costs of implementation. It suggests banks need to transform by adopting new approaches, technologies, mindsets and paradigms to address these challenges, improve customer experience, and adapt to new competitors.
Retail banking provides basic banking services like checking and savings accounts, CDs, mortgages, and loans directly to consumers rather than large corporations. Today, retail banking is characterized by offering multiple products through multiple channels to serve various customer groups. While retail banking deals with individual customers through branches, corporate banking serves business clients and investment banking handles complex financial deals between large entities.
This document discusses retail and wholesale banking in India. It defines retail banking as conducting business with individuals through segmented products, channels, and customer groups. Key drivers of retail banking include economic prosperity, changing demographics, and convenience banking. Opportunities in retail banking lie in housing, consumer finance, and wealth management. Wholesale banking refers to conducting business with corporations and includes various fund-based and non-fund-based products and services. Opportunities in wholesale banking include commercial lending, small businesses, investment banking, and structured finance.
Wholesale banking refers to providing banking services to large corporate clients, multinational firms, and other financial institutions rather than individual consumers. It involves borrowing and lending large sums of money. Services offered include savings and checking accounts, loans, underwriting, market making, and mergers and acquisitions advice. Wholesale banks deal primarily with large businesses, real estate developers, mortgage brokers, and other institutional customers.
Monzo is a mobile-only bank that provides an excellent user experience and sets a new standard for product excellence in retail banking apps. Key aspects of Monzo's approach include making everyday banking tasks simple, fast and available anywhere through the mobile app. Transactions appear instantly, payments are sent immediately, and cards can be frozen or unfrozen with a single tap. Monzo also offers strong customer service directly within the app and uses design, notifications and messaging to make banking feel more friendly and engaging for users.
Retail banking provides financial services to individual customers through local bank branches. It aims to offer multiple products through multiple distribution channels to multiple customer groups. Retail banking has grown due to changing customer demographics, increased technology penetration, cost reduction pressures, and the need to meet individual customer needs. It provides stable deposits and increases subsidiary business, but banks cannot exploit customers as much as in wholesale banking and designing new products is costly. Challenges include money laundering, maintaining customer trust, meeting evolving customer needs through technology, and retaining talent and customers to remain competitive.
The document summarizes the retail banking system in India as presented by Varsha Golekar. It discusses how retail banking has shifted from being credit and risk focused to being more customer centric. It provides an overview of the products, services, and processes involved in retail banking. Specifically, it describes TJSB Sahakari Bank Ltd's centralized retail banking cell and its processing of retail loans. It discusses the key risks in retail banking and the general documents required for loan applications and recovery processes.
The document discusses retail banking in India. It notes that retail banking refers to banking services offered directly to individual consumers rather than corporations, including savings accounts, loans, credit/debit cards. Indian retail banking is growing rapidly due to increasing consumerism, internet usage, and new banking companies/technology. Banks are adopting strategies from retail stores, such as improved customer service, customized products, and using data to better understand customer needs.
Retail banking provides banking services to individual customers through local branches. It offers savings and checking accounts, mortgages, loans, debit/credit cards. Retail banking started in 15th century Europe and expanded through branch networks in the 19th century. Today it is characterized by multiple products and distribution channels for different customer groups. In India, retail banking has grown over 35% in the last 5 years and offers potential in rural areas. It provides secure money management and access to accounts/services through various channels like ATMs, internet and mobile banking.
Asset liability management (ALM) aims to match assets and liabilities to control sensitivity to interest rate changes and limit losses. Key concepts discussed include liquidity risk, interest rate risk, gap analysis, duration gap analysis, and the role of the ALCO in managing risks. Liquidity and interest rate risks can arise from mismatches between asset and liability cash flows and interest rate sensitivities. ALM techniques assess risks and seek to balance risks from both sides of the balance sheet.
Risk & Market Data Providers - U 3 - IBOmeenakshik38
Investment banks rely on market data providers for timely, accurate financial information and data to support decision-making. Major providers include Bloomberg, Refinitiv, and FactSet. They collect, process, and distribute real-time market quotes, historical price movements, trading volumes, economic indicators, news, and other data used by investment professionals for research, analysis, and informed investing decisions. Access to high-quality market data from reliable sources is crucial for investment banks' trading, risk management, and overall operations.
This document discusses various methodologies for credit risk modeling and risk aggregation. It describes both unconditional models that use limited borrower information and conditional models that incorporate economic factors. Top-down and bottom-up approaches to credit risk aggregation are also outlined. Linear risk aggregation simply sums individual risks while copula models allow for more complex dependence structures. The document also notes that risk is not fully fungible due to legal and tax considerations.
here we are trying to explain how firms can benefit from forecasting exchange rate, to describe common technique that used to forecast, how to evaluate forecasting performance
Risk management in banking involves four main steps: identifying risks, measuring them both qualitatively and quantitatively, managing the risks, and monitoring and reviewing risks on an ongoing basis. There are three main categories of risk for banks: credit risk, market risk, and operational risk. Basel II aimed to make capital requirements more risk-sensitive by directly linking capital to the risk levels of counterparties and businesses. It introduced three pillars: minimum capital requirements, supervisory review, and market discipline through disclosure.
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.
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.
Long horizon simulations for counterparty risk Alexandre Bon
The Challenges of Long Horizon Simulations in the context of Counterparty Risk modeling : CVA, PFE and Regulatory reporting.
This joint presentation reviews the key decisions that need making regarding the choice of risk factor evolution models and calibration methods. In particular, we will analyse the performance of classical historical calibration methods (such as Maximum Likelihood and the Efficient Method of Moments) in estimating the volatility and drift terms of the Hull & White class of Interest Rate models ; both in terms of convergence and stability.
As most methods perform satisfactorily for volatility but disappoint on the mean reversion estimation, we propose a new modified Variance Estimation method that significantly outperform the classical approaches.
Lastly, after reviewing historical economic evidence of mean-reversion dynmics in high interest rate regime, we propose modifying classical models by making mean reversion non-linear and accelerating for high rates - that can be referred as "+R" models.
This model address unrealistically large and persistent interest rates values often observed at high quantile in PFE and CVA simulations.
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.
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.
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%
Managing Credit Risk
• A major part of the business of financial institutions is making loans,
and the major risk with loans is that the borrow will not repay.
• Credit risk is the risk that a borrower will not repay a loan according
to the terms of the loan, either defaulting entirely or making late
payments of interest or principal.
• Concepts of adverse selection and moral hazard provides framework
to understand the principles that is used to minimize credit risk, yet
make successful loans.
The document discusses various models for analyzing portfolio risk and return, including the Capital Market Line (CML) and different types of return-generating models. It also covers the Capital Asset Pricing Model (CAPM) and its assumptions, the Security Market Line (SML), and techniques for evaluating portfolio performance such as the Sharpe Ratio and Treynor Ratio. The Fama-French three-factor model and Carhart four-factor model, which extend the CAPM, are also summarized.
Asset liability management (ALM) addresses risks from asset-liability mismatches due to liquidity or interest rate changes. ALM systematically manages these risks through targeting metrics like net interest margin and net economic value, while maintaining balance sheet constraints. Key ALM concepts include liquidity, maturity, interest rate sensitivity, and default risk. ALM measures interest rate risk through methods like gap analysis and aims to optimize returns while controlling risk.
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.
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.
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
Similar to Risk Europe 2002 Retail Bank Va R Pdf Min (20)
This document summarizes a presentation on analyzing and valuing core deposits given at an annual banking conference. It describes the experiences of two banks: a community bank that previously used outdated regulatory assumptions about core deposit durations and a large bank upgrading its systems. For the community bank, using a more accurate survival analysis model based on the Weibull distribution revealed that core deposits, especially money market deposits, had much longer average lives than assumed. This led the bank to change its transfer pricing methodology, incentivize core deposit growth, improve profitability, and increase franchise value through a more stable funding mix. The presentation advocated this approach as a more mathematically valid way to project deposit retention under changing rate environments compared to prior methods.
Model Risk guidance was provided by the OCC and FED in 2011. We comment on their proposal, review other relevant literature, and conclude with an user friendly checklist.
This document provides an overview and summary of a presentation on core deposit modeling. The presentation covers topics such as rate sensitivities in a rising rate environment, valuation of core deposits, sensitivity analysis from regulatory and best practice perspectives, liquidity concerns, and approaches to core deposit studies. The presentation was given by Bank Risk Advisors and covered current hot topics and best practices in core deposit modeling.
This document summarizes best practices for measuring and reporting liquidity risk. It discusses how the ALM Network is an independent consulting firm that provides customized ALM services to financial institutions. It also covers key topics examined in liquidity risk exams, including meaningful management information systems and risk limits. Additional topics include measuring prospective and projected liquidity using cash flow projections across multiple scenarios and time periods.
This document discusses core deposit theory and valuation. It begins by introducing the ALM Network consulting firm and their services. It then covers the key aspects of core deposit theory: rates, liquidity, valuation, and duration. Various levels of sophistication for modeling each aspect are presented. Examples are provided to demonstrate basic valuation of different deposit products, such as passbook accounts and money market deposit accounts, using common valuation techniques like discounted cash flow analysis and regression models. The use of these techniques to determine fair value premiums or discounts is illustrated.
The document discusses best practices for loan fair value analytics at banks. It outlines objectives like financial reporting, ALM analysis, loan pricing, and M&A analysis. The approach uses ALM models to produce loan fair values at the loan level benchmarked to market values. Loans are mapped to external factors for Level 2 disclosure. Mapping examples provided for commercial loans include rating internal loans and considering internal and external factors. Issues around data availability, mapping processes, and third party spreads are addressed.
This document summarizes a presentation on core deposit modeling best practices. It discusses topics like rate sensitivities in a rising rate environment, valuation of core deposits, sensitivity analysis, liquidity concerns, core deposit studies and behavioral inputs. Key points covered include using historical data to model rate sensitivities, the GAAP definition of valuing core deposits as the present value of average balances discounted by alternative funding costs, and the importance of sensitivity analysis and considering different scenarios in modeling.
This presentation discusses liquidity risk management for financial institutions. It begins with definitions of liquidity risk and introduces the concept of a liquidity gap as a measurement of liquidity. New regulations focusing on liquidity are discussed, including both qualitative and quantitative approaches. The presentation recommends two new ratios - the Liquidity Coverage Ratio and Net Stable Funding Ratio proposed by the Basel Committee. Best practices for liquidity risk measurement and management are then outlined, including policies, stress testing, contingency funding plans, risk limits, and oversight.
1. The Methodology of Value-at-Risk
for the Retail Banking Sector
Fred Poorman, Jr., CFA
email: fpoorman@almnetwork.com
2. Disclaimer & Reminder
• The opinions are solely those of the author, as such, they do not represent
the views of Deutsche Bank. Disclaimer:
This information has been prepared solely for information purposes. It is not an offer, recommendation, or solicitation to
buy or sell, nor is it an official confirmation of terms. It is based on information generally available to the public from sources
believed to be reliable. No representation is made that it is accurate or complete or that any returns will be achieved.
Changes to assumptions may have made a material impact on any returns detailed. Past performance is not indicative of
future returns. Price and availability are subject to change without notice. Additional information is available upon request.
• Portions of this publication are used with the express permission of the
copyright owners (the author and his publishers):
• Bank Accounting and Finance, published by Institutional Investor, Inc. and Aspen Publishers,
Inc.
• Bank Asset/Liability Management, published by A.S. Pratt/Thomson Financial
• see Appendix A for additional information
• No part of this presentation may be reproduced in any form without the
written permission of the copyright owner.
3. Presentation outline
• Applicability of VaR methodologies in the retail
banking sector
• Stochastic rate generation processes in retail
bank A/LM
• Efficient selection of portfolio-specific stress tests
• Utility of Earnings- and Value-at-Risk approaches
• Tradeoffs of EaR and VaR in retail banking
• Approach for management and disclosure of
market risk for retail banks
4. Presentation considerations
• Questions:
– Who uses VAR at their bank?
– For the trading book or structural or retail bank?
– Why do banks use this methodology?
– Why don’t banks use this methodology?
• Questions are welcome!
• Please note:
– Based, in part, on data from U.S. retail banks
– Endnotes are available in attached article
5. U.S. SEC market risk disclosure formats
(also retail bank market risk approaches)
1. Cash flow table, or Liquidity GAP, with Fair Value disclosures
• Circa 1980s
2. Sensitivity analysis of earnings, cash flow, or values based on
hypothetical rate changes
• Usually +/- 100, 200, 300 bp or some variant
• Circa 1990s
• Still standard retail banking approach
3. Probabilistic analysis disclosing earning, cash flow, or value (Value at
Risk) changes emanating from market movements
• Circa 2000s
6. Obiligatory VaR benefits slide
• Standard line - The VaR approach has
benefits that surpass regulatory compliance:
1. A way to describe the magnitude of likely losses
in a portfolio.
2. The likelihood of those losses.
3. A method to monitor, manage and control risk.
4. Efficient selection of portfolio- (or bank-)
specific risk scenarios. This is an elusive goal of
stress-test analysis.
7. Additional VaR benefits slide
• Also consider these benefits:
1. Efficient selection of portfolio-specific “worst
case” stress test.
1. This benefit deserves additional attention
2. Determination of directionality in interest
rate risk management
1. Useful for active risk management
2. Clarify investor expectations
8. Additional VaR benefits slide
• EaR is equally important
– A way to describe the magnitude of
fluctuations in earnings.
– What are chances of realising budgeted
income given market conditions
– In what scenarios do you make budget?
– What scenarios should be hedged?
9. Requisite VaR methodologies slides
1) Parametric methods variously referred to
as the correlation-covariance method, or
the delta-normal or delta–gamma
approaches. J.P. Morgan standardized this
as RiskMetrics in 1994. This is typically a
closed-form process and is used by some
financial firms to analyze and disclose
market risk.
10. Requisite VaR methodologies slides
2) Historical simulation, or extreme event stress tests. This
methodology replicates market risk factors.
• The 1987 U.S. equities market crash.
• The 1998 Russian, Asian, and Long Term Capital Management
crises.
• Rapid interest rate increases, in the following years:
– 1994, when the Fed Funds rate increased 250 bps.
– 1977-1981, when the Fed Funds rate increased over 1500 bps in forty-
eight months.
• Rapid interest rate decreases, in the following years:
– 2001, when the Feds Fund rate decreased by 475 bps.
– 1991-93, when the Fed Funds rate decreased 500 bps in twenty-four
months.
11. Requisite VaR methodologies slides
3) Monte Carlo or Quasi- Monte Carlo
methods, perhaps more correctly a
stochastic process. At its simplest, Monte
Carlo simulation is the procedure by which
random future rate paths are generated and
used to derive path dependent cash flow
schedules. It uses stochastically generated
rate paths and associates cash flows to value
interest rate contingent financial
instruments (Linsmeier, 2000 and Rahl,
2000).
12. Applicability of VaR for retail banks
• Trading portfolio assets tend to have well-defined cash flow
characteristics, with standardized cash flow mapping, and readily
available correlations and cross-correlations
• Retail bank balance sheets, in comparison, are replete with financial
instruments with either indeterminate and/or interest rate contingent
cash flows
• More intricate examples include credit card and line of credit loan types,
also seen in investment portfolios in securitized equivalents and non-
maturity deposits
• Due to the predominance of option-laden instruments on bank balance
sheets, closed-form solutions are not typically used for the structural
bank, except in stylized examples (Ho, 1999).
13. Applicability of VaR for retail banks
• Extensive discussions of instrument-level modeling specifics are outside
the scope of this paper, but a brief explication of the approach rendered
is germane.
• Modeling and valuing structural balance sheets can be problematic, as
one class of financial instruments, non-maturity deposits (e.g. demand,
savings, and money market deposit types) comprise up to fifty percent
of bank liabilities.
• Lacking a public market, there is no general consensus regarding
appropriate modeling and valuation methodologies among marketplace
participants, regulators and academia for non-maturity deposits.
• See appendix for VaR for Core deposits slides
14. A/LM & VaR
• Within the banking sector, the primary method for analyzing and
managing market risk is usually referred to as Asset/Liability
Management (A/LM).
• The goal of successful A/LM is seen as “ensuring that net interest
income and the net economic value of the balance sheet remain
positive and stable under all probable scenarios (Essert, 1997)”
• For retail banks, use risk to economic capital, referred to as Economic
Value of Equity (EVE)
• Advanced vendor-built A/LM models used by the banking sector are
capable of producing VaR analyses utilizing historical and/or stochastic
process approaches
15. A/LM & VaR
• A necessary and integral component of a
VaR-based bank A/LM approach is a suitable
interest rate model.
• Minimum requirements for utilization of a
stochastic interest rate model include:
– Creation of arbitrage-free forward term
structures of interest rates.
– Capability of utilizing historical or implied market
volatilities.
16. Stochastic rate generation processes in retail bank A/LM
• Numerous interest rate models have been
proposed for evaluating rate-contingent
financial instruments
• The model used and discussed herein is the
well-known continuous single factor Black-
Derman-Toy (B-D-T) model (Black, 1990)
• In the following analysis, the B-D-T model is
implemented with user-defined selection of:
– Short volatility
– Speed of the reversion process, via selection of the
long and short periods.
17. Stochastic rate generation processes in retail bank A/LM
• The selection of a stochastic rate component is
important in generating and valuing rate-
contingent cash flows, primary choices include:
• A Monte Carlo simulator
– Random
– Structured
• A lattice based model, primarily:
– Binomial (rates go up or down)
– Trinomial (rates go up, down, or remain stationary).
18. Other rate considerations
• Historical vs. Implied volatility
• Which volatility?
– Treasury
– Agency / Corporate
– Mortgage
– Swaps / Swaption (current vol. choice of many)
• What type of volatility model?
– Normal / Lognormal
– Curve / Mean reversion
19. Interest rates change
• Interest rates change
• Not all rates move
together
– Short-term rates and
long-term rates may
move in different
directions
– Key rate durations from
the swap curve serve as
a good bank risk proxy
20. Rate scenario generation
• Linear Path Space (LPS) is a
sampled binomial tree imposed
on a trinomial lattice
– Key rate duration approach, with
seven points on yield curve
– Seven sources of IRR, based on
key rate durations, may be bank
specific
– All points may have unique
volatility
– Compare to sensitivity analysis,
usually one IRR source, parallel
shift
21. Rate scenario considerations
• For retail bank A/LM, prefer sampled lattice utlising market-based
volatilities (can use vol shocks)
• Computational time is non-trivial
– 360 month binomial lattice has 2360 interest rate paths
– LPS is sample of all possible paths
– 269 scenarios covers 89.9% of these possibilities
– These are ordered in terms of likelihood
• Management time is valuable
– 101 scenarios covers 91.3% of 269 scenarios
– 89.9% * 91.3% > 80%
– 80/20 rule applies
22. Short rate scenarios
• Averages of 3 month
rates generated
(Spring 2000
displayed)
• Non-parallel yield
curve shifts are the
rule, not the
exception
• Rate rate changes of
100 bps, over time,
correspond to one
std. dev.,but only for
a single yield curve
point
23. Short rate examples
Projected 3 month LIBOR, Mar 2000, 20% vol
• 3 month rate
examples, 14%
– Base 12%
– Up likely
10%
– Down likely
8%
– Up extreme
6%
– Down
extreme 4%
– From last year, 2%
good for %
backtesting
Ap
Ju
Jan
Ap
Ju
Jan
Oc
Oc
l-0
l-0
t-0
t-0
r-0
r-0
-01
-02
0
1
0
1
0
1
Base Case Up likely Down likely Up extreme Down extreme
24. Short rate examples
• 3 month rate Projected 3 month LIBOR, Mar 2000, 40% vol
examples,
14%
– Base
12%
– Up likely
– Down likely 10%
– Up extreme 8%
– Down 6%
extreme
4%
– From 2000,
good for 2%
backtesting %
Ap
Ju
Jan
Ap
Ju
Jan
Oc
Oc
l-0
l-0
t-0
t-0
r-0
r-0
-01
-02
0
1
0
1
0
1
Base Case Up likely Down likely Up extreme Down extreme
25. Sample yield curves
April ‘01 base rolled to March ‘04, 20% vol.
• Projected
rates, 3 years
forward
– Base
– Up likely
– Down
likely
– Up
extreme
– Down
extreme
– From April
2001
26. Two sample banks
• Banks scaled to $20 billion in assets
• March 2001 balance sheets, rates, volatilities
• Bank 1
– Earnings at Risk exposure is to high level of rates, especially at the short end
of the curve
– Core deposits < 50% of funding
• Bank 2
– Earnings at Risk exposure is to low level of rates, especially at the short end
of the curve
– Core deposits > 50% of funding
27. Bank 1 VaR disclosure
A sample market risk disclosure should read: Our lifetime VaR limit for the Economic
Value of Equity is 25% … we calculate lifetime VaR… at the 99% confidence level (two
tailed).
Table 2
VaR Profile: March, 2001
Lifetime VaR% Quarter-end
Interest Rate Risk 16.8%
28. Bank 1 VaR (EVE) profile
100%
75%
50%
Probability
Cumulative Probability
25%
0%
2,323,329 2,479,811 2,636,294 2,792,776 2,949,258 3,105,740 3,262,223 3,418,705
Standard deviations <-3 -3 to -2 -2 to -1 -1 to mean mean to +1 +1 to +2 +2 to +3 +3 to +4
M Value
arket 2,323,329 2,479,811 2,636,294 2,792,776 2,949,258 3,105,740 3,262,223 3,418,705
Probability 1% 4% 13% 18% 46% 18% 0% 0%
Cum ulative Probability 100% 96% 83% 65% 18% 0% 0% 0%
29. Measuring risk
• Disclosure
– Trend is toward ever-increasing transparency
• Basle Principle 13
– U.S. GSEs have agreed to greater disclosure
– Equity analysts need disclosure due to Reg FD
– Enron and Global fiascos suggest more transparent
risk disclosures are appropriate
• Supplemental disclosure and analysis
– A measure of directionality
– A valuation benchmark
– The goodness of fit, or R2, of the measure.
30. Bank 1 VaR disclosure
Benchmark is 5 year swap rate
Fwd.5 year rate
Economic Value of Equity
10.00
9.00
8.00
7.00
6.00
5.00
4.00 y = -7E-06x + 25.931
R 2 = 0.9354
3.00
2.00
2,000,000 2,250,000 2,500,000 2,750,000 3,000,000 3,250,000
EVE, data points sized based on probability
31. Bank 1 EaR disclosure
Our EaR analysis and sample disclosures use the same format and bank previously used
for the VaR analysis. A sample EaR disclosure should read: Our first year EaR limit for
Net Interest Income (NII) is 10% … we calculate first year EaR … at the 99% confidence
level.
Table 3
EaR Profile: March, 2001
First year EaR % Quarter-end
Interest Rate Risk 5.5%
32. Bank 1 EaR (NII) profile
100%
75% P ro babi l i ty
Cum ul ati ve
50%
P ro babi l i ty
25%
0%
9
8
7
6
5
4
3
2
40
75
10
45
80
15
50
85
3,
4,
6,
7,
8,
0,
1,
2,
58
59
60
61
62
64
65
66
Standard deviations <-3 -3 to -2 -2 to -1 -1 to mean mean to +1 +1 to +2 +2 to +3 +3 to +4
Net Interest Income 583,409 594,758 606,107 617,456 628,805 640,154 651,503 662,852
Probability 1% 1% 18% 31% 28% 21% 0% 0%
Cumulative Probability 100% 99% 81% 50% 21% 0% 0% 0%
33. Bank 1 EaR disclosure
Benchmark is 12 month LIBOR
1 year rate
Net Interest Income
10.00
9.00
8.00
7.00
6.00
5.00
4.00
y = -9E-05x + 63.274
3.00 R 2 = 0.9416
2.00
550,000 600,000 650,000 700,000 750,000
N II, data points sized based on probability
34. Risk highlights
• A short-term rate decrease is favorable from an
earnings and a valuation standpoint. Note that
this is not necessarily always the case.
• The selection of risk mitigation strategies,
including off-balance sheet hedging may be
dependent on income/value tradeoffs.
• Different benchmarks, or key rates, may be
significant for value and earnings measures, and
for different banks.
• Bank A in isolation is a useful case study. Utility
preferences are established within a comparative
framework.
35. EaR & VaR profiles
Comparative EaR Directional EaR R 2 VaR (99%) Directional VaR R2
Analysis (99%) risk: EaR risk: VaR
Bank A 5.5% Increasing 0.94 16.8% Increasing 0.94
short rates intermediate
over 1-year rates over
horizon long-term
horizon
Bank B 10.5% Decreasing 0.99 2.6% Uncertain 0.14
short rates
over 1-year
horizon
36. Utility preferences and optimal
frontier
• Bank A would be favored over Bank B by those
investors preferring less EaR volatility.
• Investors preferring less VaR volatility would
prefer Bank B to Bank A.
• Short-term, earnings-focused investors with a
bias towards continued decreases in short rates
would, ceteris paribus, prefer Bank A to Bank B.
• Alternatively, short-term, earnings-focused
investors with a bias towards increases in short
rates would, ceteris paribus, prefer Bank B to
Bank A.
37. VaR approach: conclusion
• EaR-VaR Risk benefits:
– Metrics assist in identifying risk tolerances
– “Best practices” approach quantifies risk
– More realistic methodology for modeling interest rate
changes
– Effective risk management = potentially greater earnings
for given level of risk
– Enhanced disclosures
– Better, more stable earnings with better risk management
practices and disclosure should, ceteris paribus, result in
increased valuations
• Understand limitations of this approach
38.
39. Appendix A
• See attached article for references
• This paper was the winning entry in the Glenmede
Investment Insight Award (2001) of the Financial
Analysts of Philadelphia, an AIMR chapter and is
available at www.faphil.org
• Thanks to:
– Tom Ho for comments and insights on an earlier version
of this paper
– Glenmede Trust and the Philadelphia Chapter of AIMR
• e-mail your comments on this topic to:
fred.a.poorman@db.com
40. Account-level VAR Applications
• MBS & Mortgage Accounts
– Essential prepay factor is refi advantage
– Benchmark SVAL MBS valuation to Bloomberg
– Bloomberg OAS considerations
– LPS OAV/OAS considerations
– Compare calculation of option costs
• Core & Time Deposit Accounts
– Balances driven by spread to market rates (like refi)
– Benchmark to market transactions
– Less sophisticated analytical approach
41. Account-level VAR Applications
Average 3 month LIBOR
• MBS example
– Consider
value
distribution
and negatively
convex profile
– Compare to
bank and
derivative
profiles
42. Account-level VAR Applications
LIBOR CMO floater
• MBS example
– Consider
value
distribution
and negatively
convex profile
– Compare to
bank and
derivative
profiles
43. VaR analytics for Transaction Deposits:
Industry Models:Premium Estimates for 101 Scenarios
• Valuation is consistent
with market and model
results
• Extreme value scenarios
are an efficient,
portfolio-specific manner
for stress testing
• Review zero vol
scenario,capability to
review full scenario
detail is imperative
• Risk profile suggests
hedging strategies
44. VaR analytics for Transaction Deposits:
Industry Models: Probability
Distributions
• Transaction premium
Retail Deposit Price Distribution
is primarily comprised
of non-time deposit 100%
components
• Compare to: 75% Probability
Cum. Prob.
– MBS distributions
50%
– Loan portfolio
profile
25%
– Institution VaR
0%
104.7 105.5 106.3 107.2 108.0 108.8 109.6 110.4
Std dev. <-3 -3 to -2 -2 to -1 -1 to meanmean to +1 +1 to +2 +2 to +3 +3 to +4
$ Price 104.7 105.5 106.3 107.2 108.0 108.8 109.6 110.4
Probability 1% 4% 12% 19% 46% 18% 0% 0%
Cum. Prob. 99% 95% 83% 65% 18% 0% 0% 0%