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Basic fundamentals of Credit risk estimation

Basic fundamentals of Credit risk estimation



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    Credit+risk+estimation(2) Credit+risk+estimation(2) Presentation Transcript

    • Overview
      • Credit Risk Definition
      • Estimation of Credit Risk : Standardised Approach of Basel-II
      • Estimation of default probabilities
      • Reducing credit exposure
      • Credit Ratings Migration
      • Credit Default Correlation
      • Credit Value-at-Risk Models
    • Credit Risk Definition
      • Credit risk is defined as the potential that a bank borrower or counterparty fail to meet its obligations in accordance with agreed terms.
      • Objective of Credit Risk
        • Maximising Bank‘s Risk-adjusted Return
        • Keeping the overall credit risk exposure within acceptable level.
        • Provision of Capital for Credit Risk
        • Regulators require banks to keep capital reflecting the credit risk as per Basel II.
    • Capital Estimation for Credit Risk
      • Basel-II : Standardized Approach
      • Asset Category
      • Sovereign
      • PSEs
      • Banks
      • Corporates
      • Commercial Real Estate
      • Residential Property
      • Regulatory Retail Portfolio
      • Consumer credit
      • HTM
      • Equity Investment  
    • Capital Estimation for Credit Risk
      • As per RBI’s guidelines all credit with minimum disbursement of Rs.2 lakh need to be rated.
      • Rating Agency
        • Internal Process by the bank itself
        • External agencies
      • On the basis of rating risk-weight, as given by the RBI, need to be factored with the loan amount for estimation of RWAs.
      • Risk Weighted Assets
      • (Loan Amount- Market value of Collateral) * RW
    • Capital Estimation for Credit Risk
      • Capital Structure of Bank A
      • Paid Up Capital : 400 IPDI :25
      • Reserves & Surplus :5000 Investment in Subsidiary : 80
      • Revaluation Reserves :26 General Provisions :10
      • Sub- ordinated Term Debt :900
      • Estimate the CRAR for Credit Risk.
    • Capital Estimation for Credit Risk
    • Credit Risk : Default Probability
      • Expected Versus Unexpected Losses
      • It is the long-run average losses to each category of loan portfolio.
      • Expected loss does not by itself constitute the risk.
      • The cost of expected losses are generally factored in the loan pricing policy.
      • It is the unexpected losses for which capital is provided.
      • Unexpected losses are the variations in the actual losses over time.
      • To estimate the Unexpected Losses Basel-II has designed Probability of Default.
    • Credit Risk : Default Probability
      • Probability of default is the likelihood that a particular category of loan would not be re-paid over a specified time horizon.
      • As per the Basel-II Nomrs:
        • Expected Losses = PD * EAD*LGD
        • It is generally estimated through Rating Migration Matrix
    • Credit Ratings Migration
    • Estimation of Default Probability: Rating Approach
      • From the Migration of each rating Category to Default category need to know from the rating migration matrix
      • Each year PD need to be estimated
      • Avg. PD need to be estimated from the yearly PD from 5 to 10 years of annual probability for each rating category.
    • Estimation of Default Probability: Rating Approach
      • Annual Probability of Default:
        • No. of Firms default in each rating category
        • Total firms in that rating category
      • Average Probability of Default:
        • PD = Weighted average annual rating in each category over the
    • Estimation of Default Probability
    • Estimation of Default Probability
    • EAD & LGD
      • EAD= outstanding loans + Accrued Interest
      • LGD is the portion of EAD which cannot be recovered.
      • LGD = EAD – Recovery Amount
      • LGD= 100% - Recovery Rate
      • Recovery Rate = (Recovery amount)/EAD
      • Recovery : Collateral Sell + Cash Recovery
      • - Cost of legal process
    • Loss Given Default
    • Loss Given Default
    • Altman’s Z-Score
      • Altman’s Z-Score came as a response to the need for identifying the financial health of any business based on observable accounting and market ratios.
      • This original measure was developed in 1968 by Edward Altman, whose Z-Score is available in various forms.
    • Banks’ Health using Logit Model
      • Dependent Variable : Health: H
          • H=0 if Net NPA > 6% ( Bad)
          • H=1 if Net NPA< 6% ( Good)
      • Independent Variables
      • x 1 = Return on Assets
      • x 2 = Income to Assets
      • x 3 = Networth to Assets
      • x 4 = Operating Expenditure to Assets
      • x 5 = Operating Profit to Assets
      • x 6 = Regulatory Capital to Risk Weighted Assets
    • Banks’ Health using Logit Model
          • Log (Health) = 1.87+3.45*x1-2.26*x2-0.27*x3-4.39*x4+1.26*x5 +0.17*x6
          • Ratios considered are
          • Return on Assets (x1)
          • Total Income to Total Assets(x2)
          • Networth to Total Assets (x3)
          • Operating Expenditure to Total Assets(x4)
          • Operating Profit to Total Assets(x5)
          • Regulatory Capital to Risk Weighted Assets(x6)