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Dunya Institute for Higher Education

Dunya Institute for Higher Education

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Dunya Institute for Higher Education

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  1. Dunya Institute for Higher Education

  2. Risk Management By Zulfiqar Ali Khan

  3. What is risk ? • The possibility of incurring unexpected loss or uncertainty that can result in losses. • Financial risk is the variability of expected future return. In some cases it may be worst than expected. R=∑(pi*xi) Pi= probability of loss Xi= loss amount

  4. Risk Management • Risk management is the identification, assessment and prioritization of risk and taking appropriate actions to reduce, control or supervise the probability of unfortunate events. • Identification includes identifying its sources i.e. saving dominated in domestic currency • Assessment includes the probability of occurrence into the impact of risk event

  5. Risk Management (Cont.) • Plans to address the risk • Out-sourcing or sharing • Mitigating • Retention and prepare budget • Financial risk management uses different measures to mitigate risk specifically credit and market risks

  6. Types of Risks in Banking Sector • Financial Risk • Credit Risk • Market Risk • Non-financial Risk • Operational Risk • Strategic Risk • Political Risk • Legal Risk

  7. Weight of Various Risk • In order to make a profit, every bank must take risks. These are: • Credit Risk (50%+) • Operational Risk (15-30%) • Market Risk (5-10%) • Liquidity Risk • Risk is an uncertainty that could result in losses or adverse fluctuations in profitability.

  8. Financial Risk • Uncertainty of financial returns faced by banks or probability of expected future losses resulting from market price movement and default on the borrower side. • Financial Risk includes: • Credit Risk • Market Risk

  9. Credit Risk • The inability of borrower to repay principle and interest premium or the inability of borrower to meets its contractual agreement. • For example: A company or person is unable to pay its loan. • Credit Risk includes: • Concentration Risk • Default Risk

  10. Credit Risk • Potential that a bank's borrower or counterparty will fail to meet its obligations. • Single largest factor threatening the soundness of financial institutions and the financial system as a whole. • Arises any time bank funds are extended, committed, invested, or otherwise exposed.

  11. Credit Risk • Sources include: • weak or non-existent credit standards for borrowers • lax loan portfolio management • poor economic conditions • Loans are the largest source. • Also includes the risk that the credit standing or rating of the obligor will decline.

  12. Credit Risk Example • Afghan National Bank has a loan portfolio of Afs 10,000, consisting of 10 loans with a principal amount of Afs 1,000 each and a maturity of 1 year. The annual interest rate of each loan is 10%. Calculate the total interest revenue on the loan portfolio?

  13. Discussion Question • One of the borrowers is declared bankrupt and defaults on the loan. What impact will this have on the bank’s operating results?

  14. Quiz • Short-term lending generally involves a greater degree of risk than long-term lending. – True or False? • On average, long-term lending involves a greater risk because of the length of time the credit is outstanding. Due to this long-term loans are often priced higher than short-term loans. They are also often secured and generally require regular amortization.

  15. Credit Default Risk • Credit default risk is one of the most common risks in banking sector. • The inability of borrower to pay its debt and the interest charged on the debt. • Banks use different instruments and tools for avoiding, reducing and transferring risk. They also use third party provided intelligence.

  16. Credit Scoring Models • Credit Scoring systems apply statistical techniques to empirically determine • which variables (quantitative and qualitative) characterize “good” firms from “bad” • What weight should be assigned to these variables to achieve the highest rate of discriminatory and predictive power • The final score can be used • To discriminate between good (low probability of default) and bad (high probability of default) credits • To act as an early warning signal • To derive the implied PD

  17. Approaches for Estimation of Default Probability (PD) • Historical time series data of rating transitions and defaults derived from Banks’ internal Rating Models • Credit Scoring Models - Empirical analysis of factors predicting default using statistical techniques • Structural Models/Option Theoretic Models/ Market based Models

  18. Lending Spectrum and PD Estimation Impact of Credit Risk of Individual Obligors Retail SME Corporate • Retail scoring models • Pooled PD, LGD • Rich data, large samples for statistical methodologies • Judgmental Internal Ratings • Pooled PD • Data quality • Internal Rating Models • Mapping to External Rating Models • Lumpy and small data

  19. Estimation of PD through Rating Transitions • Basel II requirements for use of Internal Ratings for PD estimation • Historically validated two-dimensional risk-ratings • More than seven non-default grades and one default grade • Active use of the system in credit approval, pricing, credit limit and provisioning • Stable populations within scorecard rating bands • Ratings that make use of all available quantitative and qualitative information about the obligor

  20. Estimation of PD through Rating Transitions • PD can be estimated by analyzing rating transitions over time • Methodology - mortality rate analysis of one-year cohorts of companies to find the number of firms in each rating class (or industry) in each cohort moving towards default (D) • Data - Banks’ firm wise internal rating transition data covering at least one economic cycle (Minimum 5 years) • The year-wise default rate for different rating grades can be estimated by counting the number of defaulting companies in a yearly transition for each grade and dividing by the total number of firms in that grade at the beginning of the year

  21. Estimation of PD through Rating Transitions • Average one-year default probabilities are obtained by weighted average where the weight represents the relative importance of each year as given by the cohort size for each rating grade at the beginning of each year • Stressed PD • Ensure that the rating process has considered stressed macroeconomic scenarios • Point-in-time versus Through-the-cycle ratings • Calculated PD over a downturn sample period • Time horizon in PD estimation versus time horizon for rating assignment

  22. Transition and Default Probabilities for Indian Bond Ratings Transition Matrix: Probabilities of credit rating migrating from one rating quality to another, within one year

  23. Industry PDs for Different Bond Grades

  24. Limitations of Rating Based Approach to PD Estimation • Uniform PDs across parties in the same rating class • Default may be a continuous process, which is not addressed by rating migration approach where only a discrete number of rating grades are assigned • Indian Banks largely use point-in-time ratings which depend upon current macroeconomic conditions and will result in unstressed one-year PDs which may not relevant for capital estimation, even if a long run average of these one-years PDs is computed • Definition of Default has to be consistently applied • Validation requires testing the performance of cumulative defaults across rating grades

  25. Z-score for Indian Firms: NIBM Model Z= Function {WK_TA, CASHP_TA, SOLVR, OPPROF_TA, SALES_TA} • If Z > 0, then firm is classified as non-defaulting firm • If Z < 0, then firm is classified as a defaulting firm • If Z = 0, grey area • Holdout Sample Validation - The model has been tested on a 2004 holdout sample of 50 firms and it has 92% correct prediction for defaulted firms (otherwise type 1 error) & 96% correct prediction for solvent firms (otherwise type 2 error).

  26. Z-score for Indian Firms: A Comparison

  27. Z-score for Indian Firms: NIBM Model Logit Score = Function {WK_TA, CASHP_TA, SOLVR, MVE_BVL, SALES_TA, ISO Cert., Age of the firm, Industry Category} PD =[1 / {1+EXP(-Logit Score)}] * 100 • If PD > 50%, then firm is classified as a defaulting firm • If PD < 50%, then firm is classified as a non-defaulting firm • Overall, within sample discriminatory power is 97.2%

  28. Option Theory Approach • Academic belief is that default is driven by • market value of firm’s assets • level of firm’s obligations (or liabilities) • variability in future market value of assets • As the market value of firm’s assets approaches book value of liabilities, the default risk of firm increases • Default Point: The threshold value of firm’s assets (somewhere between total liabilities & current liabilities) at which the firm defaults • Relevant Networth = Mkt. Value of Assets - Default Pt. • Default: Relevant Networth = 0

  29. Steps in Estimating EDFs • Estimation of current market value of firm’s assets and volatility of asset return by using market capitalization, stock return volatility and book value of liabilities • Calculation of Distance to Default (DD) • Mapping Expected Default Probability (EDF) from DD using normal distribution

  30. Modeling LGD • The purpose of developing models for predicting LGD is to produce estimates of loss rates for accounts that would default in a future horizon • Any modeling effort will depend on the availability of historical data reflecting the bank’s lending experience • The factors (drivers / explanatory variables) included in any LGD model will likely come from the set of factors we find to be important determinants for explaining variations in LGD • Factors such as: collateral, facility, firm, industry, country, and the macro economy

  31. Modeling LGD • Look Up Table: • Average historical LGD for certain pre-defined factors. For eg. Average LGD for Senior Unsecured Loans to Automotive Industry during a Recession • Easy to build and use • Very data intensive • Basic Statistical Models (Regression Analysis) • LGDit regressed on dummies for Senior / Subordinate, Collateral Type, Collateral Value, Industry group, Macroeconomic Expansion/Recession etc. • Given that LGD typically varies from 0 to 1, one can use two statistical techniques-linear regression and tobit regression • If there are many cases, where the recovery rate is 0, one may get biased or less accurate results if linear regression technique is used • Therefore, tobit regression, which is designed to handle cases where the dependent variable is clustered around limit 0, is more appropriate.

  32. An RMA Paper Study

  33. Asian LGDs Vary a Lot by Country, but are High Relative to the US (45%) Source: S & P Asia-Pacific Banking Outlook 2004

  34. LGD Model for an Indian Bank

  35. Estimation of EAD • A Bank’s best estimate of the amount drawn down upon on a revolving credit upon default in a year • Banks must empirically support assumptions on expected draw downs given default

  36. Estimation of EAD • Histogram of the usage ratio of all credit lines: • 14% of credit line-year observations are zero • 11% of observations are at 100% usage • Remaining 75% of observations are roughly symmetric around the 50% usage mark

  37. Estimation of EAD • EAD depends on both the loan type and the borrower • Covenants • Restructuring • Obligor-specific characteristics – such as the history with the institution and customer profitability • Macroeconomic factors

  38. Modeling EAD Where i denotes the credit line, j the firm, k the bankand t the time. • δitis the time to default for credit lines that default at t+τ, τ>0 • two specifications • discrete, dummy variables; i.e.,δit(-5) = 1 & rest = 0 • continuous variable; i.e.,δit(-5) = -5 • Firmjt is variables that controls for firm characteristics • Bankkt is variables that control for bank characteristics • GDPGt is real, annual growth rate in GDP • RIRt is 3m real interbank interest rate (measure of funding) • ηiis a fixed effect for the credit line including collateral

  39. ModellingEAD