The document discusses capital requirements for small and medium-sized entities (SMEs) and other retail asset classes under Basel II. It provides formulas for calculating capital requirements based on probability of default, loss given default, maturity, and firm size. It also examines methods for determining probability of default using migration matrices and cohort or hazard rate approaches based on historical credit rating data. Transition probabilities are presented for one, five, and ten year time periods based on these methods.
The document discusses propensity modeling using logistic regression for various applications such as insurance, banking, and consumer purchases. It describes how propensity models use logistic regression to predict the probability of a binary outcome based on multiple independent variables. The document then provides a specific example of building a propensity model to predict the likelihood of a customer obtaining a new mortgage within 3 months using logistic regression on customer database variables. It evaluates the economic impact of the model by estimating costs, revenues, and 5-year net present value when targeting customer segments identified by the model.
The document discusses different types of predictive modeling and analytics models, including time series models, regression models, statistical models, machine learning models, physical models, mathematical models, and propensity models. It provides examples and descriptions of each type of model. The document also includes an economic evaluation of a propensity model for predicting home mortgages that estimates a 5-year net present value of $1,500,000 by targeting the top 35% of customers.
This document contains a conversion worksheet that shows the relationship between Z-value, DPMO (defects per million opportunities), percentage of defects, and yield. As the Z-value increases from 0 to 2, the DPMO decreases from 500,000 to 22,750, the percentage of defects decreases from 50% to 2.3%, and the yield increases from 50% to 97.7%.
The document appears to be a spreadsheet containing historical monthly inflation data from March 1991 to November 1999 based on the INPC index, with columns calculating interest, new indices, and credit values. It includes notes explaining that differences can be calculated from March 1991 and that the spreadsheet is specifically for FGTS accounts remunerated at 3% annual interest. The yellow cells in the spreadsheet can be filled in by the user to perform calculations.
The document provides financial and operational data for four companies - Andrews, Baldwin, Chester, and Digby. It includes key metrics like return on sales, assets, and equity as well as leverage, sales, profits, stock prices, and bond yields. It also provides production details for 16 products across three market segments (Traditional, Low End, High End). The top performing products in each segment are identified based on market share, sales, and customer survey results.
This document contains the solutions to four questions regarding bond valuation and analysis for a financial modeling course. It includes the valuations of four bonds using various inputs like maturity, coupon rate, yield to maturity, etc. It also includes the calculations of convexity for each of the four bonds.
Reliance Industries ltd. data & analysisPankaj Sharma
- The financial analysis examines key financial ratios for Reliance Industries Ltd. from 2008 to 2017 including current ratio, debt to assets ratio, total assets turnover, quick ratio, cash ratio, gross profit margin, net profit margin, debt to equity ratio, and financial leverage.
- Many of the ratios fluctuated over the years analyzed, with some ratios like current ratio and quick ratio decreasing initially and then increasing in later years. Other ratios such as debt to equity peaked in 2009 before declining.
- The document provides a detailed analysis of each financial ratio and trends over the 10-year period to evaluate the company's financial performance and health.
This document contains financial ratios and statements for a company from March 2004 to March 2008. It shows that over this period gross profit, net profit, and sales increased substantially while costs also increased. Most ratios such as debt-equity, current, and return on net worth also improved over time indicating stronger financial performance.
The document discusses propensity modeling using logistic regression for various applications such as insurance, banking, and consumer purchases. It describes how propensity models use logistic regression to predict the probability of a binary outcome based on multiple independent variables. The document then provides a specific example of building a propensity model to predict the likelihood of a customer obtaining a new mortgage within 3 months using logistic regression on customer database variables. It evaluates the economic impact of the model by estimating costs, revenues, and 5-year net present value when targeting customer segments identified by the model.
The document discusses different types of predictive modeling and analytics models, including time series models, regression models, statistical models, machine learning models, physical models, mathematical models, and propensity models. It provides examples and descriptions of each type of model. The document also includes an economic evaluation of a propensity model for predicting home mortgages that estimates a 5-year net present value of $1,500,000 by targeting the top 35% of customers.
This document contains a conversion worksheet that shows the relationship between Z-value, DPMO (defects per million opportunities), percentage of defects, and yield. As the Z-value increases from 0 to 2, the DPMO decreases from 500,000 to 22,750, the percentage of defects decreases from 50% to 2.3%, and the yield increases from 50% to 97.7%.
The document appears to be a spreadsheet containing historical monthly inflation data from March 1991 to November 1999 based on the INPC index, with columns calculating interest, new indices, and credit values. It includes notes explaining that differences can be calculated from March 1991 and that the spreadsheet is specifically for FGTS accounts remunerated at 3% annual interest. The yellow cells in the spreadsheet can be filled in by the user to perform calculations.
The document provides financial and operational data for four companies - Andrews, Baldwin, Chester, and Digby. It includes key metrics like return on sales, assets, and equity as well as leverage, sales, profits, stock prices, and bond yields. It also provides production details for 16 products across three market segments (Traditional, Low End, High End). The top performing products in each segment are identified based on market share, sales, and customer survey results.
This document contains the solutions to four questions regarding bond valuation and analysis for a financial modeling course. It includes the valuations of four bonds using various inputs like maturity, coupon rate, yield to maturity, etc. It also includes the calculations of convexity for each of the four bonds.
Reliance Industries ltd. data & analysisPankaj Sharma
- The financial analysis examines key financial ratios for Reliance Industries Ltd. from 2008 to 2017 including current ratio, debt to assets ratio, total assets turnover, quick ratio, cash ratio, gross profit margin, net profit margin, debt to equity ratio, and financial leverage.
- Many of the ratios fluctuated over the years analyzed, with some ratios like current ratio and quick ratio decreasing initially and then increasing in later years. Other ratios such as debt to equity peaked in 2009 before declining.
- The document provides a detailed analysis of each financial ratio and trends over the 10-year period to evaluate the company's financial performance and health.
This document contains financial ratios and statements for a company from March 2004 to March 2008. It shows that over this period gross profit, net profit, and sales increased substantially while costs also increased. Most ratios such as debt-equity, current, and return on net worth also improved over time indicating stronger financial performance.
Financial Conduct Authority_Developing our approach to implementing MiFID II ...Oliver Blower
This document discusses the Financial Conduct Authority's approach to implementing certain conduct of business and organizational requirements from the revised Markets in Financial Instruments Directive (MiFID II) in the UK. It seeks early feedback from firms and stakeholders on topics like applying MiFID II rules to insurance products and pensions, treating structured deposits, and recording telephone conversations. It aims to gather input to help develop policy options for later consultation on transposing MiFID II, which strengthens investor protections and must be implemented by July 2016.
Cfa level 1 quantitative analysis e book part 1parmanandiskool
The given e-book discusses Quantitative Analysis module for CFA L-1 .It is part-1 of the series and for other parts visit our site https://www.educorporatebridge.com/freebies3.php
The document discusses Value at Risk (VaR), a metric used to measure and manage financial risk. It provides an introduction to VaR and outlines several key concepts, including: reasons for VaR's widespread adoption; calculating VaR for single and multiple assets; assumptions underlying VaR calculations; and approaches to estimating VaR for linear and non-linear derivatives. It also covers converting daily VaR to other time periods, factors affecting portfolio risk, and stress testing as a complement to VaR analysis.
Credit risk modeling helps estimate potential credit losses and determine how much capital is needed to protect against such risks. It is more complex than market risk modeling due to factors like limited data on defaults, illiquidity in credit markets, non-normal distributions of losses, and correlations between obligors that increase in downturns. The main approaches are default mode, which considers losses from defaults, and mark-to-market, which also incorporates losses from credit quality deterioration. Structural models link default to a firm's asset value while reduced form models view default as a random event. Correlations between probability of default, exposure at default, and loss given default are also important to consider.
This presentation is the one stop point to learn about Basel Norms in the Banking
This is the most comprehensive presentation on Risk Management in Banks and Basel Norms. It presents in details the evolution of Basel Norms right form Pre Basel area till implementation of Basel III in 2019 along with factors and reason for shifting of Basel I to II and finally to III.
Links to Video's in the presentation
Risk Management in Banks
https://www.youtube.com/watch?v=fZ5_V4RW5pE
Tier 1 Capital
http://www.investopedia.com/terms/t/tier1capital.asp
Tier 2 Capital
http://www.investopedia.com/terms/t/tier2capital.asp
Basel I
http://www.investopedia.com/terms/b/basel_i.asp
Capital Adequacy Ratio
http://www.investopedia.com/terms/c/capitaladequacyratio.asp
Basel II
http://www.investopedia.com/video/play/what-basel-ii/?header_alt=c
Basel III
http://www.investopedia.com/terms/b/basell-iii.asp
RBI Governor - Raghuram G Rajan on the importance if Basel III regulations
https://youtu.be/EN27ZRe_28A
The document discusses risk and return in investments. It defines key concepts such as realized and expected return, ex-ante and ex-post returns, sources and measurements of risk including standard deviation and coefficient of variation. It also discusses the risk-return tradeoff and how higher risk investments require higher potential returns to compensate for additional risk.
Meeting the data management challenges of MiFID IILeigh Hill
The compliance deadline for Markets in Financial Instruments Directive II (MiFID II) has been pushed back a year to January 2018, giving financial institutions within its scope an opportunity to take a strategic rather than tactical approach to implementation. But whatever the approach, the scale of the regulation is large and the data management challenge complex, requiring firms to work on compliance solutions well ahead of the deadline.
Join the webinar to find out more about:
-Regulatory guidance
-Progress on data management
-Outstanding challenges
-Best practice approaches
-Meeting the deadline
This module discusses risk management and insurance. It covers topics such as risks and risk management, different types of risks, methods of handling risks including avoiding, controlling, accepting and transferring risks. It also discusses the basic concepts of insurance including risk pooling, law of large numbers, requirements of insurable risks, advantages and disadvantages of insurance. Additionally, it covers personal risk management process, objectives of risk management pre-loss and post-loss, insurance market dynamics and underwriting cycle. Finally, it discusses some key legal principles of insurance contracts such as offer and acceptance, consideration, insurable interest, subrogation and utmost good faith.
WTF - Why the Future Is Up to Us - pptx versionTim O'Reilly
This is the talk I gave January 12, 2017 at the G20/OECD Conference on the Digital Future in Berlin. I talk about fitness landscapes as applied to technology and business, the role of unchecked financialization in the state of our politics and economy, and why technology really wants to create jobs, not destroy them. (There is a separate PDF version, but some readers said the notes were too fuzzy to read.)
This document summarizes default rate data from Moody's and Standard & Poor's (S&P) credit rating agencies from 1987 to 2016. It includes default rates for various credit rating categories (e.g. AAA, AA, A, BBB) according to Moody's and S&P scales as well as overall averages. The document is from a textbook on credit risk management and Basel III regulations authored by Dr. Lam Yat-fai from CapitaLogic Limited.
This document provides examples of various credit risk modeling techniques in Excel including: single name CDS pricing; static and dynamic hedging; lower and upper bounds of transition matrices; first-to-default, basket, and portfolio CDS pricing; portfolio defaults simulation; and portfolio CDS pricing. The examples are from a textbook on managing credit risk under Basel III authored by Dr. Lam Yat-fai from CapitaLogic Limited.
The document discusses using wavelet analysis techniques to identify hidden patterns in financial time series data. It presents the wavelet transform approach for filtering time series data and decomposing it into different time scales. Several examples are given showing how wavelet analysis can be used to study structural patterns in datasets like NASDAQ and for momentum-based trading strategies.
Multi Objective Optimization of PMEDM Process Parameter by Topsis Methodijtsrd
In this study, MRR, SR, and HV in powder mixed electrical discharge machining PMEDM were multi criteria decision making MCDM by TOPSIS method. The process parameters used included work piece materials, electrode materials, electrode polarity, pulse on time, pulse off time, current, and titanium powder concentration. Some interaction pairs among the process parameters were also used to evaluate. The results showed that optimal process parameters, including ton = 20 µs, I= 6 A, tof = 57 µs, and 10 g l. The optimum characteristics were MRR = 38.79 mm3 min, SR = 2.71 m, and HV = 771.0 HV. Nguyen Duc Luan | Nguyen Duc Minh | Le Thi Phuong Thanh ""Multi-Objective Optimization of PMEDM Process Parameter by Topsis Method"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23169.pdf
Paper URL: https://www.ijtsrd.com/engineering/manufacturing-engineering/23169/multi-objective-optimization-of-pmedm-process-parameter-by-topsis-method/nguyen-duc-luan
This document describes a regression analysis conducted on data containing 97 observations of PSA levels and 7 predictor variables. Initially, a full regression model was fit using the first 65 observations. Diagnostic plots of the residuals showed some lack of randomness, indicating a need for transformation. A Box-Cox transformation with lambda=0.5 was applied to the response variable before refitting the model. The transformed model will be validated using the remaining 32 observations to select the best regression model for predicting PSA levels from this data.
Quantitative Analysis of Retail Sector Companiesprashantbhati354
In-depth quantitative analysis of the UK's three big retail sector companies, ASOS Plc, M&S, and WH Smith. In this, I have done descriptive statistics analysis, performance comparisons, stock price estimation with the help of time series analysis, beta estimation, and many more to find the future growth and profitability of the companies.
ALLL Webinar | CECL Methodologies Series Kick OffLibby Bierman
In this session Sageworks' Brandon Russell and Neekis Hammond explain prepayments, attrition rates, the use of FICO and data requirements for the CECL model to be used for financial institutions' ALLL or allowance for loan and lease losses.
Linear regression an 80 year study of the dow jones industrial averageTehyaSingleton
Linear regression was used to model the relationship between the Dow Jones Industrial Average (DJIA) price and years since 1930 over an 80 year period. The results showed a strong positive linear relationship where DJIA price increases by about 125 points for each additional year. The slope of the linear model indicates that DJIA price rises as years since 1930 increases. The y-intercept of the model, which is the hypothetical DJIA price at year 0 (1930), provides meaningful context about the starting price over the 80 years analyzed.
Linear regression an 80 year study of the dow jones industrial averageTehyaSingleton
Linear regression was used to model the relationship between the Dow Jones Industrial Average (DJIA) price and years since 1930 over an 80 year period. The results showed a strong positive linear relationship where DJIA price increases by about 125 points for each additional year. The regression equation determined that DJIA price equals 125.3 times the number of years since 1930 minus 2.4425. While DJIA price has generally increased over the eight decades, the model suggests it would have been negative in 1930 based on the y-intercept value.
This document provides examples of credit risk modeling techniques using an Excel workbook. It includes examples of calculating expected loss for individual and multiple borrowers, modeling default correlation using copulas, and simulating defaults in a homogeneous loan portfolio. The workbook is intended to demonstrate concepts from the textbook "Managing Credit Risk Under The Basel III Framework" by Dr. Lam Yat-fai.
10 yrs competitive analysis of lg balakrishnanKarmveer Singh
This document analyzes the financial performance of LG Balakrishnan & Brothers over a 10-year period from 2002-2003 to 2011-2012. It provides details on sales, variable costs, contribution, fixed costs, profit before tax, and profit after tax. Key findings include a decline in sales and profits during the recession period from 2006-2007 to 2008-2009, with profits turning negative. The document also calculates metrics like P/V ratio, BEP, and margin of safety to evaluate the company's performance over time. Correlation analysis found a strong negative relationship between sales and profit.
This document provides references and supporting materials for a project management course. It includes a list of 6 main readings and 5 supporting readings on topics like project management, quantitative methods, and construction project management. It also outlines the course schedule covering areas such as project planning, risk, scheduling, and project control across 12 meetings. Tables show examples of cash flow analyses for different project scenarios calculating NPV and IRR.
Financial Conduct Authority_Developing our approach to implementing MiFID II ...Oliver Blower
This document discusses the Financial Conduct Authority's approach to implementing certain conduct of business and organizational requirements from the revised Markets in Financial Instruments Directive (MiFID II) in the UK. It seeks early feedback from firms and stakeholders on topics like applying MiFID II rules to insurance products and pensions, treating structured deposits, and recording telephone conversations. It aims to gather input to help develop policy options for later consultation on transposing MiFID II, which strengthens investor protections and must be implemented by July 2016.
Cfa level 1 quantitative analysis e book part 1parmanandiskool
The given e-book discusses Quantitative Analysis module for CFA L-1 .It is part-1 of the series and for other parts visit our site https://www.educorporatebridge.com/freebies3.php
The document discusses Value at Risk (VaR), a metric used to measure and manage financial risk. It provides an introduction to VaR and outlines several key concepts, including: reasons for VaR's widespread adoption; calculating VaR for single and multiple assets; assumptions underlying VaR calculations; and approaches to estimating VaR for linear and non-linear derivatives. It also covers converting daily VaR to other time periods, factors affecting portfolio risk, and stress testing as a complement to VaR analysis.
Credit risk modeling helps estimate potential credit losses and determine how much capital is needed to protect against such risks. It is more complex than market risk modeling due to factors like limited data on defaults, illiquidity in credit markets, non-normal distributions of losses, and correlations between obligors that increase in downturns. The main approaches are default mode, which considers losses from defaults, and mark-to-market, which also incorporates losses from credit quality deterioration. Structural models link default to a firm's asset value while reduced form models view default as a random event. Correlations between probability of default, exposure at default, and loss given default are also important to consider.
This presentation is the one stop point to learn about Basel Norms in the Banking
This is the most comprehensive presentation on Risk Management in Banks and Basel Norms. It presents in details the evolution of Basel Norms right form Pre Basel area till implementation of Basel III in 2019 along with factors and reason for shifting of Basel I to II and finally to III.
Links to Video's in the presentation
Risk Management in Banks
https://www.youtube.com/watch?v=fZ5_V4RW5pE
Tier 1 Capital
http://www.investopedia.com/terms/t/tier1capital.asp
Tier 2 Capital
http://www.investopedia.com/terms/t/tier2capital.asp
Basel I
http://www.investopedia.com/terms/b/basel_i.asp
Capital Adequacy Ratio
http://www.investopedia.com/terms/c/capitaladequacyratio.asp
Basel II
http://www.investopedia.com/video/play/what-basel-ii/?header_alt=c
Basel III
http://www.investopedia.com/terms/b/basell-iii.asp
RBI Governor - Raghuram G Rajan on the importance if Basel III regulations
https://youtu.be/EN27ZRe_28A
The document discusses risk and return in investments. It defines key concepts such as realized and expected return, ex-ante and ex-post returns, sources and measurements of risk including standard deviation and coefficient of variation. It also discusses the risk-return tradeoff and how higher risk investments require higher potential returns to compensate for additional risk.
Meeting the data management challenges of MiFID IILeigh Hill
The compliance deadline for Markets in Financial Instruments Directive II (MiFID II) has been pushed back a year to January 2018, giving financial institutions within its scope an opportunity to take a strategic rather than tactical approach to implementation. But whatever the approach, the scale of the regulation is large and the data management challenge complex, requiring firms to work on compliance solutions well ahead of the deadline.
Join the webinar to find out more about:
-Regulatory guidance
-Progress on data management
-Outstanding challenges
-Best practice approaches
-Meeting the deadline
This module discusses risk management and insurance. It covers topics such as risks and risk management, different types of risks, methods of handling risks including avoiding, controlling, accepting and transferring risks. It also discusses the basic concepts of insurance including risk pooling, law of large numbers, requirements of insurable risks, advantages and disadvantages of insurance. Additionally, it covers personal risk management process, objectives of risk management pre-loss and post-loss, insurance market dynamics and underwriting cycle. Finally, it discusses some key legal principles of insurance contracts such as offer and acceptance, consideration, insurable interest, subrogation and utmost good faith.
WTF - Why the Future Is Up to Us - pptx versionTim O'Reilly
This is the talk I gave January 12, 2017 at the G20/OECD Conference on the Digital Future in Berlin. I talk about fitness landscapes as applied to technology and business, the role of unchecked financialization in the state of our politics and economy, and why technology really wants to create jobs, not destroy them. (There is a separate PDF version, but some readers said the notes were too fuzzy to read.)
This document summarizes default rate data from Moody's and Standard & Poor's (S&P) credit rating agencies from 1987 to 2016. It includes default rates for various credit rating categories (e.g. AAA, AA, A, BBB) according to Moody's and S&P scales as well as overall averages. The document is from a textbook on credit risk management and Basel III regulations authored by Dr. Lam Yat-fai from CapitaLogic Limited.
This document provides examples of various credit risk modeling techniques in Excel including: single name CDS pricing; static and dynamic hedging; lower and upper bounds of transition matrices; first-to-default, basket, and portfolio CDS pricing; portfolio defaults simulation; and portfolio CDS pricing. The examples are from a textbook on managing credit risk under Basel III authored by Dr. Lam Yat-fai from CapitaLogic Limited.
The document discusses using wavelet analysis techniques to identify hidden patterns in financial time series data. It presents the wavelet transform approach for filtering time series data and decomposing it into different time scales. Several examples are given showing how wavelet analysis can be used to study structural patterns in datasets like NASDAQ and for momentum-based trading strategies.
Multi Objective Optimization of PMEDM Process Parameter by Topsis Methodijtsrd
In this study, MRR, SR, and HV in powder mixed electrical discharge machining PMEDM were multi criteria decision making MCDM by TOPSIS method. The process parameters used included work piece materials, electrode materials, electrode polarity, pulse on time, pulse off time, current, and titanium powder concentration. Some interaction pairs among the process parameters were also used to evaluate. The results showed that optimal process parameters, including ton = 20 µs, I= 6 A, tof = 57 µs, and 10 g l. The optimum characteristics were MRR = 38.79 mm3 min, SR = 2.71 m, and HV = 771.0 HV. Nguyen Duc Luan | Nguyen Duc Minh | Le Thi Phuong Thanh ""Multi-Objective Optimization of PMEDM Process Parameter by Topsis Method"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23169.pdf
Paper URL: https://www.ijtsrd.com/engineering/manufacturing-engineering/23169/multi-objective-optimization-of-pmedm-process-parameter-by-topsis-method/nguyen-duc-luan
This document describes a regression analysis conducted on data containing 97 observations of PSA levels and 7 predictor variables. Initially, a full regression model was fit using the first 65 observations. Diagnostic plots of the residuals showed some lack of randomness, indicating a need for transformation. A Box-Cox transformation with lambda=0.5 was applied to the response variable before refitting the model. The transformed model will be validated using the remaining 32 observations to select the best regression model for predicting PSA levels from this data.
Quantitative Analysis of Retail Sector Companiesprashantbhati354
In-depth quantitative analysis of the UK's three big retail sector companies, ASOS Plc, M&S, and WH Smith. In this, I have done descriptive statistics analysis, performance comparisons, stock price estimation with the help of time series analysis, beta estimation, and many more to find the future growth and profitability of the companies.
ALLL Webinar | CECL Methodologies Series Kick OffLibby Bierman
In this session Sageworks' Brandon Russell and Neekis Hammond explain prepayments, attrition rates, the use of FICO and data requirements for the CECL model to be used for financial institutions' ALLL or allowance for loan and lease losses.
Linear regression an 80 year study of the dow jones industrial averageTehyaSingleton
Linear regression was used to model the relationship between the Dow Jones Industrial Average (DJIA) price and years since 1930 over an 80 year period. The results showed a strong positive linear relationship where DJIA price increases by about 125 points for each additional year. The slope of the linear model indicates that DJIA price rises as years since 1930 increases. The y-intercept of the model, which is the hypothetical DJIA price at year 0 (1930), provides meaningful context about the starting price over the 80 years analyzed.
Linear regression an 80 year study of the dow jones industrial averageTehyaSingleton
Linear regression was used to model the relationship between the Dow Jones Industrial Average (DJIA) price and years since 1930 over an 80 year period. The results showed a strong positive linear relationship where DJIA price increases by about 125 points for each additional year. The regression equation determined that DJIA price equals 125.3 times the number of years since 1930 minus 2.4425. While DJIA price has generally increased over the eight decades, the model suggests it would have been negative in 1930 based on the y-intercept value.
This document provides examples of credit risk modeling techniques using an Excel workbook. It includes examples of calculating expected loss for individual and multiple borrowers, modeling default correlation using copulas, and simulating defaults in a homogeneous loan portfolio. The workbook is intended to demonstrate concepts from the textbook "Managing Credit Risk Under The Basel III Framework" by Dr. Lam Yat-fai.
10 yrs competitive analysis of lg balakrishnanKarmveer Singh
This document analyzes the financial performance of LG Balakrishnan & Brothers over a 10-year period from 2002-2003 to 2011-2012. It provides details on sales, variable costs, contribution, fixed costs, profit before tax, and profit after tax. Key findings include a decline in sales and profits during the recession period from 2006-2007 to 2008-2009, with profits turning negative. The document also calculates metrics like P/V ratio, BEP, and margin of safety to evaluate the company's performance over time. Correlation analysis found a strong negative relationship between sales and profit.
This document provides references and supporting materials for a project management course. It includes a list of 6 main readings and 5 supporting readings on topics like project management, quantitative methods, and construction project management. It also outlines the course schedule covering areas such as project planning, risk, scheduling, and project control across 12 meetings. Tables show examples of cash flow analyses for different project scenarios calculating NPV and IRR.
Isu tentang monopoli perusahaan PBM swasta di wilayah Pelindo khususnya Pelindo III cabang tanjung perak
disini kami meneliti dampak kebijakan tersebut
This is a Construction Cost Estimate for a construction project Case study taking few parameters as assumptions.like Steel per m cube is taken as 80 kg.
1. The document presents information on an insurance policy called Elite Builder, including projected cashback amounts, guaranteed sums, total premiums paid, and returns on investment.
2. It shows the cashback and sum assured amounts projected to be received each year over the 30 year term of the policy. The total cashback is projected to be Rs. 66,750 and total guaranteed sum is Rs. 52,000 over the term.
3. Two payment options are presented - Option 1 with a 12 year payment term and Option 3 with an 8 year payment term. The total cash received is projected to be Rs. 309,293 under Option 1 and Rs. 429,051 under Option 3.
1) Triple exponential smoothing was used to analyze monthly bean supply price data for Mbeya and demand data for Kinondoni in Tanzania from 2004-2014.
2) The multiplicative seasonal model best fit the increasing seasonal variations in Mbeya's data. Smoothing parameters of 0.2, 0.1, and 0.1 were applied.
3) Forecasts for 2015 were generated for both locations based on the exponential smoothing models. Accuracy metrics and residual analysis were also calculated.
This document contains multiple budget and forecast summaries for different periods and categories. It includes forecasts for revenues and expenses broken down by project and category for January through December. It also includes receivables and payables balances by customer and supplier, with amounts due and overdue. The data is presented in tables with column and row labels to organize the financial information by period, category, and customer/supplier.
Miroslav iz NPS je imao vrlo dinamično i živahno predavanje na temu “Microsoft Dynamics & Business intelligence rešenje”. Ukratko, kako Microsoft BI (Business intelligence) alati mogu da pomognu controllerima?
In this analysis, I was tasked to calculate the growth rate of different energy production methods in the United States, and find the two fastest growing renewable energy methods.
Similar to Basel II Risk Weighted Assets 2011 (20)
1. Capital Requirements Under
Basel II:
Evaluation Of Capital Requirement to Default
Probability, Loss Given Default and Maturity for
SME and Other Retail Asset Classes
Maggie Kriebelt
October 2011
2. Minimum Requirements for Internal
Rating Systems for Basel II
Minimum 7 rating classes for non-defaulted borrowers
No excessive concentrations in single rating classes
Differentiation of risk amongst the rating classes
Plausible, intuitive and current input data
All relevant information must be taken into account
No preference for model methods
Maggie Kriebelt October 2011
3. Risk-Weighted Assets for Small &
Medium Sized Entities: Evaluating
Exposures Not In Default
Based on the Section 273 of the Basel II Committee
Notes - Rules for Risk Weighted Assets in this Class Are
Determined As Follows:
Correlation = 0.12 * (1-e(-50*pd))/(1-e(-50))+ 0.24 * [1-(1-
e(-50*pd)) / (1-e(-50))] – 0.04*(1-(S-5)/45)
Maturity Adjustment = (0.11852 – 0.05478*ln(pd))^2
Capital Requirement = [LGD * N(1-R)^-0.5 * G(PD) +
(R/(1-R))^0.5 * G(0.999)] – PD * LGD] *(1-1.5*b)^-1
*(1+(M-2.5)*b)
Risk Weighted Assets = 12.5 *Capital Requirement*EAD
Maggie Kriebelt October 2011
4. Risk-Weighted Assets for SME Exposures:
Evaluating Exposures Not In Default
Risk Weighted Assets for entities with sales under 50
million Euros are likewise stressed at the alpha 0.1%
level to obtain a 99.9% confidence that realizations of
default on the portfolio for each exposure is better
than the stress scenario
Maturity adjustment remains the same
Correlation is dependent on Probability of Default and
adjustment for firm size is incorporated
Required Capital increases for larger SME’s and is more
pronounced at higher maturities as can be seen from
Required Capital surfaces at 1Yr, 5Yr, 10Yr and 20Yr:
response surface and grid illustrations follow
Maggie Kriebelt October 2011
5. Determining Probability of Default
◦ Migration Matrices to calculate PD using
Cohort Method or Hazard Rate Method
from historical credit ratings
Sample Rating Data
Obligor Date Rating
12550 17-Nov-84 AA
12550 1-Feb-00 BBB
12550 21-Mar-04 BB
14770 2-Sep-82 CCC
14770 30-Dec-94 B
14770 14-Apr-96 BB
13426 30-Sep-82 B
13426 3-May-96 BB
Maggie Kriebelt October 2011
6. Cohort Method
Uses obligors that have a rating at
beginning of evaluation period creating a
transition matrix with frequencies of
rating at end of period
◦ Let Ni,t = # of obligors in category i at
beginning period t
◦ Let Nij,t = # of obligors in cohort i that moved
to grade j at end of period t
◦ Transition frequency in period t
pˆij,t = Nij,t / Ni,t
Maggie Kriebelt October 2011
7. Cohort Method
◦ Transition frequency averaged over all periods
pˆij,t= Ni,t * pˆij,t / Ni,t
t t
◦ Combining equations and reducing yields the
obligor weighted average as the aggregate of
the transitions from i to j divided by the total
obligors in cohort i at beginning of period
pˆij,t= Ni,t * [Nij,t / Ni,t] / Ni,t
t t
pˆij,t= Nij,t / Ni,t
t t
pˆij,t= Nij / Ni
Maggie Kriebelt October 2011
8. Cohort Method Portfolio Results
on Hypothetical Credit Data
One Year Transition Probabilities Based On Cohort Approach
Using Historical Credit Ratings Data From 1986 to 2005
From/To AAA AA A BBB BB B CCC D
AAA 93.70606 5.523937 0.602611 0.133914 0.033478 0 0 0
AA 1.778222 93.12687 4.355644 0.579421 0.11988 0 0 0.03996
A 0.110274 2.940636 92.17056 4.208785 0.459474 0.055137 0 0.055137
BBB 0.021418 0.257014 5.225958 90.27629 3.512529 0.471193 0.042836 0.192761
BB 0.023137 0.161962 0.624711 6.362795 88.59324 3.378066 0.254512 0.601573
B 0 0 0.041632 0.749376 7.993339 86.5945 2.622814 1.998335
CCC 0 0 0.095057 0.285171 1.996198 5.038023 82.12928 10.45627
D 0 0 0 0 0 0 0 100
One Year Transition Probabilities Based On Cohort Approach
Using Historical Credit Ratings Data From 1996 to 2005
From/To AAA AA A BBB BB B CCC D
AAA 91.27219 7.544379 0.887574 0.221893 0.073964 0 0 0
AA 2.573808 91.06737 5.412566 0.719152 0.189251 0 0 0.03785
A 0.167729 3.555854 90.00335 5.73633 0.469641 0.033546 0 0.033546
BBB 0.037566 0.413223 6.423742 88.12923 3.869271 0.713749 0.075131 0.338092
BB 0.054915 0.384404 1.043383 9.2257 84.40417 3.953871 0.10983 0.823723
B 0 0 0.120773 1.449275 9.903382 82.72947 2.777778 3.019324
CCC 0 0 0.429185 0.858369 3.433476 6.437768 79.39914 9.44206
D 0 0 0 0 0 0 0 100
Maggie Kriebelt October 2011
9. Cohort Method Portfolio Results
5Yr & 10 Yr Migration
Five Year Transition Probabilities Based On Cohort Approach
Using Historical Credit Ratings Data From 1986 to 2005
From/To AAA AA A BBB BB B CCC D
AAA 73.06543 21.29285 4.284603 1.046794 0.254769 0.023495 0.002075 0.029982
AA 6.847254 71.90163 16.54954 3.600975 0.786187 0.087365 0.007538 0.219519
A 0.847467 11.09027 69.29591 15.13993 2.739804 0.478313 0.040981 0.367323
BBB 0.189815 2.181287 18.61027 63.33793 11.89928 2.346053 0.285521 1.149843
BB 0.128347 0.919461 4.680541 21.11996 58.13473 10.40889 1.276009 3.332054
B 0.019635 0.154885 1.018553 5.993631 24.44986 51.382 6.864434 10.11701
CCC 0.006867 0.064796 0.57572 2.121639 8.0462 13.37377 38.19502 37.61598
D 0 0 0 0 0 0 0 100
Ten Year Transition Probabilities Based On Cohort Approach
Using Historical Credit Ratings Data From 1986 to 2005
From/To AAA AA A BBB BB B CCC D
AAA 54.88218 31.36801 9.830473 2.898558 0.749521 0.119689 0.013522 0.138051
AA 10.07438 55.07772 24.3688 7.618646 1.943656 0.355796 0.041533 0.519475
A 1.998195 16.19584 52.84186 21.09709 5.502486 1.23296 0.155925 0.975646
BBB 0.58174 5.167537 25.63513 45.67485 15.57847 4.059124 0.610572 2.692578
BB 0.313235 2.219682 10.16596 27.04901 39.09302 12.08884 2.00598 7.064282
B 0.086901 0.668082 3.554979 12.34532 28.07138 30.00972 6.47848 18.78514
CCC 0.033936 0.277624 1.537531 4.74466 11.28951 12.86993 15.6156 53.6312
D 0 0 0 0 0 0 0 100
Maggie Kriebelt October 2011
10. Hazard Rate Method
Incorporates within period transitions
◦ Calculate generator matrix
◦ Let Nij = # of obligors in cohort i that moved to
grade j
◦ Let Yi (s) = # of obligors rated i at time s
t
◦ Let ij= Nij / ∫ Yi(s) ds for i ≠j
t0
◦ Let ii= - ij
i≠j
◦ T-year migration P(T)
k k
P(T) = exp( T) = [ T ] / k!
k=0
Maggie Kriebelt October 2011
11. Hazard Rate Portfolio Results on
Hypothetical Credit Data
One Year Transition Probabilities Based On Hazard Rate Approach
Using Historical Credit Ratings Data From 1986 to 2005
From/To AAA AA A BBB BB B CCC D
AAA 91.48377 7.143391 1.025835 0.261321 0.080607 0.002547 0.00015 0.002381
AA 2.39916 91.58535 4.966863 0.80533 0.196159 0.007226 0.000435 0.039473
A 0.196455 3.394196 90.40769 5.318108 0.577465 0.058286 0.002858 0.044939
BBB 0.043742 0.357067 6.251844 88.62991 3.613714 0.683256 0.073595 0.346873
BB 0.055308 0.27961 1.156373 8.904006 84.94655 3.559837 0.14485 0.953468
B 0.003317 0.020408 0.310894 1.655158 9.166305 83.65436 2.321998 2.867557
CCC 0.0012 0.006633 0.05653 0.98391 3.376531 6.418202 80.43506 8.72193
D 0 0 0 0 0 0 0 100
One Year Transition Probabilities Based On Hazard Rate Approach
Using Historical Credit Ratings Data From 1996 to 2005
From/To AAA AA A BBB BB B CCC D
AAA 91.48377 7.143391 1.025835 0.261321 0.080607 0.002547 0.00015 0.002381
AA 2.39916 91.58535 4.966863 0.80533 0.196159 0.007226 0.000435 0.039473
A 0.196455 3.394196 90.40769 5.318108 0.577465 0.058286 0.002858 0.044939
BBB 0.043742 0.357067 6.251844 88.62991 3.613714 0.683256 0.073595 0.346873
BB 0.055308 0.27961 1.156373 8.904006 84.94655 3.559837 0.14485 0.953468
B 0.003317 0.020408 0.310894 1.655158 9.166305 83.65436 2.321998 2.867557
CCC 0.0012 0.006633 0.05653 0.98391 3.376531 6.418202 80.43506 8.72193
D 0 0 0 0 0 0 0 100
Maggie Kriebelt October 2011
12. Hazard Rate Portfolio Results
5Yr & 10 Yr Migration
Five Year Transition Probabilities Based On Hazard Rate Approach
Using Historical Credit Ratings Data From 1986 to 2005
From/To AAA AA A BBB BB B CCC D
AAA 73.38483 20.65001 4.454501 1.149504 0.28702 0.032235 0.003147 0.03875
AA 6.550769 72.95027 15.67757 3.639123 0.836047 0.107379 0.010417 0.228424
A 0.881035 10.75132 70.0775 14.47197 2.857795 0.516373 0.049304 0.3947
BBB 0.20045 2.032838 18.31537 64.33316 11.35115 2.289411 0.285357 1.192264
BB 0.126529 0.77568 4.776965 20.6913 58.97102 9.928518 1.223181 3.506811
B 0.025236 0.170192 1.319722 6.304139 23.7213 52.30527 6.173526 9.980614
CCC 0.006923 0.046348 0.376048 2.167411 7.2824 13.67692 39.42811 37.01584
D 0 0 0 0 0 0 0 100
Ten Year Transition Probabilities Based On Hazard Rate Approach
Using Historical Credit Ratings Data From 1986 to 2005
From/To AAA AA A BBB BB B CCC D
AAA 55.24799 30.72278 9.852635 3.040695 0.81819 0.140936 0.016679 0.160091
AA 9.73258 56.33635 23.423 7.520052 2.009072 0.385311 0.046882 0.546757
A 2.001007 15.87657 53.62746 20.47819 5.549173 1.265583 0.163269 1.038746
BBB 0.585547 4.893365 25.51896 46.61364 15.10129 3.933185 0.585521 2.768485
BB 0.304434 2.001088 10.21716 26.88666 39.71213 11.71463 1.878023 7.285881
B 0.097573 0.675289 3.953918 12.59257 27.60057 30.70927 5.971997 18.39881
CCC 0.03117 0.21777 1.344712 4.674156 10.66734 13.32095 16.48556 53.25834
D 0 0 0 0 0 0 0 100
Maggie Kriebelt October 2011
13. Default Probability from Empirical Data
for Cohort & Hazard Rate Methods
Default Probabilities Based On Cohort Approach
Using Historical Credit Ratings Data From 1986 to 2005
From/To 1Yr 2Yr 3Yr 4Yr 5Yr 6Yr 7Yr 8Yr 9Yr 10Yr
AAA 0.001498 0.005996 0.013517 0.024101 0.037807 0.054708 0.074885 0.098427 0.12543 0.155991
AA 0.040788 0.083544 0.128718 0.176724 0.227944 0.282727 0.341385 0.404199 0.471415 0.543248
A 0.059614 0.128384 0.206552 0.294276 0.391638 0.498653 0.615271 0.741389 0.876854 1.021472
BBB 0.202292 0.423177 0.661822 0.917261 1.188434 1.474216 1.773451 2.084972 2.407621 2.740265
BB 0.619709 1.271857 1.950739 2.650864 3.367084 4.09468 4.829397 5.567462 6.305568 7.040864
B 2.018484 4.050252 6.063012 8.033479 9.945363 11.78777 13.55391 15.24014 16.84515 18.36934
CCC 9.629118 17.78827 24.71842 30.61973 35.65858 39.97344 43.67962 46.87324 49.63454 52.03053
Default Probabilities Based On Hazard Rate Approach
Using Historical Credit Ratings Data From 1986 to 2005
From/To 1Yr 2Yr 3Yr 4Yr 5Yr 6Yr 7Yr 8Yr 9Yr 10Yr
AAA 0.001535 0.006144 0.01385 0.024698 0.03875 0.056084 0.076787 0.100955 0.128688 0.160091
AA 0.040797 0.083582 0.128829 0.176975 0.228424 0.283542 0.342658 0.406067 0.474026 0.546757
A 0.059362 0.128286 0.207046 0.295821 0.3947 0.503696 0.622747 0.751731 0.890472 1.038746
BBB 0.200529 0.421073 0.660641 0.91811 1.192264 1.481835 1.785528 2.102046 2.430113 2.768485
BB 0.649443 1.330547 2.03753 2.764734 3.506811 4.258847 5.016422 5.775631 6.533084 7.285881
B 2.014358 4.052683 6.076586 8.058813 9.980614 11.8297 13.59863 15.28358 16.88339 18.39881
CCC 10.17111 18.6966 25.86297 31.90512 37.01584 41.35351 45.04838 48.20775 50.92006 53.25834
Maggie Kriebelt October 2011
16. Required Capital Levels for SME Exposures
Required Capital By SME Size Factor Level 10YR Maturity
5MM Euro
0.30
0.30
0.25
0.25
0.20
0.20
Req'd Capital
0.15
0.10 0.15
0.05
0.10
0.5
0.4
0.3
0.05
0.2 0.1
Probability Default 0.2
0.1 0.3
0.4
0.5
0.6
Loss Given Default
0.00
Kriebelt 2011
Maggie Kriebelt October 2011
17. Required Capital Levels for SME Exposures
Required Capital By SME Size Factor Level 10YR Maturity
20MM Euro
0.35
0.30
0.30
0.25
0.25
0.20
Req'd Capital 0.20
0.15
0.10
0.15
0.05
0.10
0.5
0.4
0.3
0.2 0.1 0.05
Probability Default 0.2
0.1 0.3
0.4
0.5
0.6
Loss Given Default
0.00
Kriebelt 2011
Maggie Kriebelt October 2011
18. Required Capital Levels for SME Exposures
Required Capital By SME Size Factor Level 10YR Maturity
50MM Euro
0.40
0.35
0.30
0.3
0.25
Req'd Capital
0.2
0.20
0.1
0.15
0.5 0.10
0.4
0.3
0.2 0.1
Probability Default 0.2 0.05
0.1 0.3
0.4
0.5
0.6
Loss Given Default
0.00
Kriebelt 2011
Maggie Kriebelt October 2011
20. Required Capital Levels for SME Exposures
Required Capital SME 40MM Euros Maturity 10Yrs
lgd_0.1 lgd_0.2 lgd_0.3 lgd_0.4 lgd_0.5 lgd_0.6 lgd_0.7
pd_0.1 0.047 0.054 0.053 0.050 0.045 0.038 0.094
pd_0.2 0.107 0.107 0.100 0.089 0.075 0.142 0.161
pd_0.3 0.160 0.150 0.134 0.113 0.189 0.214 0.214
pd_0.4 0.200 0.178 0.150 0.236 0.268 0.267 0.250
pd_0.5 0.223 0.188 0.283 0.321 0.321 0.300 0.267
pd_0.6 0.225 0.330 0.375 0.374 0.350 0.312 0.263
Required Capital SME 50MM Euros Maturity 10Yrs
lgd_0.1 lgd_0.2 lgd_0.3 lgd_0.4 lgd_0.5 lgd_0.6 lgd_0.7
pd_0.1 0.050 0.056 0.056 0.052 0.046 0.038 0.099
pd_0.2 0.112 0.111 0.103 0.092 0.077 0.149 0.168
pd_0.3 0.167 0.155 0.138 0.115 0.199 0.224 0.222
pd_0.4 0.207 0.183 0.154 0.249 0.280 0.278 0.259
pd_0.5 0.229 0.192 0.298 0.336 0.333 0.310 0.275
pd_0.6 0.231 0.348 0.391 0.389 0.362 0.321 0.269
Generally, required capital increases over higher SME size ,with
larger default probabilities and greater loss given default, however
the parabolic functions determining the required capital
incorporates higher probability of default lowers possibility of
upgrade: result is a relatively lower required capital when pd and
lgd closer to 1.0 with the pattern continuing over higher
maturities as well.
Maggie Kriebelt October 2011
21. Required Capital Levels for SME Exposures
Required Capital By SME Size Factor Level 1YR Maturity
30MM Euro 35MM Euro 40MM Euro 45MM Euro 50MM Euro
0.30
0.25 0.25 0.25 0.25 0.25
0.20 0.20 0.20 0.20 0.20
0.25
Req'd Capital
0.15 Req'd Capital
0.15 Req'd Capital
0.15 Req'd Capital
0.15 Req'd Capital
0.15
0.10 0.10 0.10 0.10 0.10
0.05 0.05 0.05 0.05 0.05
0.20
0.5 0.5 0.5 0.5 0.5
0.4 0.4 0.4 0.4 0.4
0.3 0.3 0.3 0.3 0.3
0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1
Probability Default 0.3 0.2 Probability Default 0.3 0.2 Probability Default 0.3 0.2 Probability Default 0.3 0.2 Probability Default 0.3 0.2
0.1 0.4 0.1 0.4 0.1 0.4 0.1 0.4 0.1 0.4
0.5 0.5 0.5 0.5 0.5
0.6 0.6 0.6 0.6 0.6
Loss Given Default Loss Given Default Loss Given Default Loss Given Default Loss Given Default
0.15
5MM Euro 10MM Euro 15MM Euro 20MM Euro 25MM Euro
0.25 0.25 0.25 0.25 0.25 0.10
0.20 0.20 0.20 0.20 0.20
Req'd Capital
0.15 Req'd Capital
0.15 Req'd Capital
0.15 Req'd Capital
0.15 Req'd Capital
0.15
0.10 0.10 0.10 0.10 0.10
0.05 0.05 0.05 0.05 0.05 0.05
0.5 0.5 0.5 0.5 0.5
0.4 0.4 0.4 0.4 0.4
0.3 0.3 0.3 0.3 0.3
0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1
Probability Default 0.2 Probability Default 0.2 Probability Default 0.2 Probability Default 0.2 Probability Default 0.2
0.1 0.4 0.3 0.1 0.4 0.3 0.1 0.4 0.3 0.1 0.4 0.3 0.1 0.4 0.3
0.5 0.5 0.5 0.5 0.5
0.6 0.6 0.6 0.6 0.6 0.00
Loss Given Default Loss Given Default Loss Given Default Loss Given Default Loss Given Default
Kriebelt 2011
Maggie Kriebelt October 2011
22. Required Capital Levels for SME Exposures
Required Capital By SME Size Factor Level 5YR Maturity
30MM Euro 35MM Euro 40MM Euro 45MM Euro 50MM Euro 0.35
0.30 0.30 0.30 0.30 0.30
0.25 0.25 0.25 0.25 0.25
0.30
0.20 0.20 0.20 0.20 0.20
Req'd Capital Req'd Capital Req'd Capital Req'd Capital Req'd Capital
0.15 0.15 0.15 0.15 0.15
0.10 0.10 0.10 0.10 0.10
0.25
0.05 0.05 0.05 0.05 0.05
0.5 0.5 0.5 0.5 0.5
0.4 0.4 0.4 0.4 0.4
0.3 0.3 0.3 0.3 0.3
0.2 0.2 0.2 0.2 0.2
Probability Default
0.1 0.4
0.3 0.2 0.1
Probability Default
0.1 0.4
0.3 0.2 0.1
Probability Default
0.1 0.4
0.3 0.2 0.1
Probability Default
0.1 0.4
0.3 0.2 0.1
Probability Default
0.1 0.4
0.3 0.2 0.1
0.20
0.5 0.5 0.5 0.5 0.5
0.6 0.6 0.6 0.6 0.6
Loss Given Default Loss Given Default Loss Given Default Loss Given Default Loss Given Default
5MM Euro 10MM Euro 15MM Euro 20MM Euro 25MM Euro
0.15
0.30 0.30 0.30 0.30 0.30
0.25 0.25 0.25 0.25 0.25
0.10
0.20 0.20 0.20 0.20 0.20
Req'd Capital Req'd Capital Req'd Capital Req'd Capital Req'd Capital
0.15 0.15 0.15 0.15 0.15
0.10 0.10 0.10 0.10 0.10
0.05 0.05 0.05 0.05 0.05
0.05
0.5 0.5 0.5 0.5 0.5
0.4 0.4 0.4 0.4 0.4
0.3 0.3 0.3 0.3 0.3
0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1
Probability Default 0.2 Probability Default 0.2 Probability Default 0.2 Probability Default 0.2 Probability Default 0.2
0.1 0.4 0.3 0.1 0.4 0.3 0.1 0.4 0.3 0.1 0.4 0.3 0.1 0.4 0.3
0.5 0.5 0.5 0.5 0.5
0.6 0.6 0.6 0.6 0.6 0.00
Loss Given Default Loss Given Default Loss Given Default Loss Given Default Loss Given Default
Kriebelt 2011
Maggie Kriebelt October 2011
23. Required Capital Levels for SME Exposures
Required Capital By SME Size Factor Level 10YR Maturity
30MM Euro 35MM Euro 40MM Euro 45MM Euro 50MM Euro
0.40
0.3 0.3 0.3 0.3 0.3 0.35
Req'd Capital
0.2 Req'd Capital
0.2 Req'd Capital
0.2 Req'd Capital
0.2 Req'd Capital
0.2
0.1 0.1 0.1 0.1 0.1 0.30
0.5 0.5 0.5 0.5 0.5
0.4 0.4 0.4 0.4 0.4
0.3 0.3 0.3 0.3 0.3
0.25
0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1
Probability Default 0.3 0.2 Probability Default 0.3 0.2 Probability Default 0.3 0.2 Probability Default 0.3 0.2 Probability Default 0.3 0.2
0.1 0.4 0.1 0.4 0.1 0.4 0.1 0.4 0.1 0.4
0.5 0.5 0.5 0.5 0.5
0.6 0.6 0.6 0.6 0.6
Loss Given Default Loss Given Default Loss Given Default Loss Given Default Loss Given Default
0.20
5MM Euro 10MM Euro 15MM Euro 20MM Euro 25MM Euro
0.15
0.3 0.3 0.3 0.3 0.3
Req'd Capital
0.2 Req'd Capital
0.2 Req'd Capital
0.2 Req'd Capital
0.2 Req'd Capital
0.2 0.10
0.1 0.1 0.1 0.1 0.1
0.05
0.5 0.5 0.5 0.5 0.5
0.4 0.4 0.4 0.4 0.4
0.3 0.3 0.3 0.3 0.3
0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1
Probability Default 0.2 Probability Default 0.2 Probability Default 0.2 Probability Default 0.2 Probability Default 0.2
0.1 0.4 0.3 0.1 0.4 0.3 0.1 0.4 0.3 0.1 0.4 0.3 0.1 0.4 0.3
0.5 0.5 0.5 0.5 0.5
0.6 0.6 0.6 0.6 0.6 0.00
Loss Given Default Loss Given Default Loss Given Default Loss Given Default Loss Given Default
Kriebelt 2011
Maggie Kriebelt October 2011
24. Required Capital Levels for SME Exposures
Required Capital By SME Size Factor Level 20YR Maturity
30MM Euro 35MM Euro 40MM Euro 45MM Euro 50MM Euro
0.5
0.5 0.5 0.5 0.5 0.5
0.4 0.4 0.4 0.4 0.4
0.3 0.3 0.3 0.3 0.3
Req'd Capital Req'd Capital Req'd Capital Req'd Capital Req'd Capital
0.2 0.2 0.2 0.2 0.2
0.4
0.1 0.1 0.1 0.1 0.1
0.5 0.5 0.5 0.5 0.5
0.4 0.4 0.4 0.4 0.4
0.3 0.3 0.3 0.3 0.3
0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1
Probability Default 0.3 0.2 Probability Default 0.3 0.2 Probability Default 0.3 0.2 Probability Default 0.3 0.2 Probability Default 0.3 0.2
0.1 0.1 0.1 0.1 0.1
0.6
0.5 0.4
0.6
0.5 0.4
0.6
0.5 0.4
0.6
0.5 0.4
0.6
0.5 0.4 0.3
Loss Given Default Loss Given Default Loss Given Default Loss Given Default Loss Given Default
5MM Euro 10MM Euro 15MM Euro 20MM Euro 25MM Euro
0.5 0.5 0.5 0.5 0.5
0.2
0.4 0.4 0.4 0.4 0.4
0.3 0.3 0.3 0.3 0.3
Req'd Capital Req'd Capital Req'd Capital Req'd Capital Req'd Capital
0.2 0.2 0.2 0.2 0.2
0.1
0.1 0.1 0.1 0.1 0.1
0.5 0.5 0.5 0.5 0.5
0.4 0.4 0.4 0.4 0.4
0.3 0.3 0.3 0.3 0.3
0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1
Probability Default 0.2 Probability Default 0.2 Probability Default 0.2 Probability Default 0.2 Probability Default 0.2
0.1 0.4 0.3 0.1 0.4 0.3 0.1 0.4 0.3 0.1 0.4 0.3 0.1 0.4 0.3
0.5 0.5 0.5 0.5 0.5
0.6 0.6 0.6 0.6 0.6
0.0
Loss Given Default Loss Given Default Loss Given Default Loss Given Default Loss Given Default
Kriebelt 2011
Maggie Kriebelt October 2011
25. Risk-Weighted Assets of Other Retail Exposures:
Evaluating Exposures Not In Default
Based on the Section 330 of the Basel II Committee
Notes - Rules for Risk Weighted Assets in this Class Are
Determined As Follows:
Correlation = Correlation = 0.03 * (1-e(-35*pd))/(1-e(-35))+
0.16 * [1-(1-e(-35*pd)) / (1-e(-35))]
Capital Requirement = [LGD * N(1-R)^-0.5 * G(PD) +
(R/(1-R))^0.5 * G(0.999)] – PD * LGD]
Risk Weighted Assets = 12.5 *Capital Requirement*EAD
Maggie Kriebelt October 2011
26. Risk-Weighted Assets of Other Retail Exposures :
Required Capital increases for with LGD and is
parabolic with respect to PD, but is smaller than for
both mortgage and revolving retail lines exposures
No maturity adjustment
Correlation is dependent on default probability
Response surface and grid illustrations compare the
required capital requirements
Maggie Kriebelt October 2011
29. Risk-Weighted Assets of Other Retail Exposures
Required Capital For Other Retail Exposures
0.20
0.15
0.15
0.10
Req'd Capital
0.10
0.05
0.8 0.05
0.6
0.4
Probability Default
0.2 0.2
0.4
0.6
0.8
Loss Given Default
0.00
Kriebelt 2011
Maggie Kriebelt October 2011