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Loan Portfolio Selection and Risk Measurement. Chapters 10 and 11. The Paradox of Credit. Lending is not a “buy and hold”process. To move to the efficient frontier, maximize return for any given level of risk or equivalently, minimize risk for any given level of return.

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Loan Portfolio Selection and Risk Measurement

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Loan portfolio selection and risk measurement

Loan Portfolio Selection and Risk Measurement

Chapters 10 and 11


The paradox of credit

The Paradox of Credit

  • Lending is not a “buy and hold”process.

  • To move to the efficient frontier, maximize return for any given level of risk or equivalently, minimize risk for any given level of return.

  • This may entail the selling of loans from the portfolio. “Paradox of Credit” – Fig. 10.1.

Saunders & Allen Chapters 10 & 11


Loan portfolio selection and risk measurement

Saunders & Allen Chapters 10 & 11


Managing the loan portfolio according to the tenets of modern portfolio theory

Managing the Loan Portfolio According to the Tenets of Modern Portfolio Theory

  • Improve the risk-return tradeoff by:

    • Calculating default correlations across assets.

    • Trade the loans in the portfolio (as conditions change) rather than hold the loans to maturity.

    • This requires the existence of a low transaction cost, liquid loan market.

    • Inputs to MPT model: Expected return, Risk (standard deviation) and correlations

Saunders & Allen Chapters 10 & 11


The optimum risky loan portfolio fig 10 2

The Optimum Risky Loan Portfolio – Fig. 10.2

  • Choose the point on the efficient frontier with the highest Sharpe ratio:

    • The Sharpe ratio is the excess return to risk ratio calculated as:

Saunders & Allen Chapters 10 & 11


Loan portfolio selection and risk measurement

Saunders & Allen Chapters 10 & 11


Problems in applying mpt to untraded loan portfolios

Problems in Applying MPT to Untraded Loan Portfolios

  • Mean-variance world only relevant if security returns are normal or if investors have quadratic utility functions.

    • Need 3rd moment (skewness) and 4th moment (kurtosis) to represent loan return distributions.

  • Unobservable returns

    • No historical price data.

  • Unobservable correlations

Saunders & Allen Chapters 10 & 11


Kmv s portfolio manager

KMV’s Portfolio Manager

  • Returns for each loan I:

    • Rit = Spreadi + Feesi – (EDFi x LGDi) – rf

  • Loan Risks=variability around EL=EGF x LGD = UL

    • LGD assumed fixed: ULi =

    • LGD variable, but independent across borrowers: ULi =

    • VOL is the standard deviation of LGD. VVOL is valuation volatility of loan value under MTM model.

    • MTM model with variable, indep LGD (mean LGD): ULi =

Saunders & Allen Chapters 10 & 11


Valuation under kmv pm

Valuation Under KMV PM

  • Depends on the relationship between the loan’s maturity and the credit horizon date:

  • Figure 11.1: DM if loan’s maturity is less than or equal to the credit horizon date (maturities M1 or M2).

  • MTM if loan’s maturity is greater than credit horizon date (maturity M3). See Appendix 11.1 for valuation.

Saunders & Allen Chapters 10 & 11


Loan portfolio selection and risk measurement

Saunders & Allen Chapters 10 & 11


Correlations

Correlations

  • Figure 11.2 – joint PD is the shaded area.

  • GF = GF/GF

  • GF =

  • Correlations higher (lower) if isocircles are more elliptical (circular).

  • If JDFGF = EDFGEDFF then correlation=0.

Saunders & Allen Chapters 10 & 11


Loan portfolio selection and risk measurement

Saunders & Allen Chapters 10 & 11


Role of correlations

Role of Correlations

  • Barnhill & Maxwell (2001): diversification can reduce bond portfolio’s standard deviation from $23,433 to $8,102.

  • KMV diversifies 54% of risk using 5 different BBB rated bonds.

  • KMV uses asset (de-levered equity) correlations, CreditMetrics uses equity correlations.

  • Correlation ranges:

    • KMV: .002 to .15

    • Credit Risk Plus: .01 to .05

    • CreditMetrics: .0013 to .033

Saunders & Allen Chapters 10 & 11


Calculating correlations using kmv pm

Calculating Correlations using KMV PM

  • Construct asset returns using OPM.

  • Estimate 3-level multifactor model. Estimate coefficients and then evaluated asset variance and correlation coefficients using:

  • First level decomposition:

    • Single index model – composite market factor constructed for each firm.

  • Second level decomposition:

    • Two factors: country and industry indices.

  • Third level decomposition:

    • Three sets of factors: (1) 2 global factors (market-weighted index of returns for all firms and return index weighted by the log of MV); (2) 5 regional factors (Europe, No. America, Japan, SE Asia, Australia/NZ); (3) 7 sector factors (interest sensitive, extraction, consumer durables, consumer nondurables, technology, medical services, other).

Saunders & Allen Chapters 10 & 11


Creditmetrics portfolio var

CreditMetrics Portfolio VAR

  • Two approaches:

    • Assuming normally distributed asset values.

    • Using actual (fat-tailed and negatively skewed) asset distributions.

  • For the 2 Loan Case, Calculate:

    • Joint migration probabilities

    • Joint payoffs or loan values

    • To obtain portfolio value distribution.

Saunders & Allen Chapters 10 & 11


The 2 loan case under the normal distribution

The 2-Loan Case Under the Normal Distribution

  • Joint Migration Probabilities = the product of each loan’s migration probability only if the correlation coefficient=0.

    • From Table 10.1, the probability that obligor 1 retains its BBB rating and obligor 2 retains it’s a rating would be 0.8693 x 0.9105 = 79.15% if the loans were uncorrelated. The entry of 79.69% suggests a positive correlation of 0.3.

Saunders & Allen Chapters 10 & 11


Mapping ratings transitions to asset value distributions

Mapping Ratings Transitions to Asset Value Distributions

  • Assume that assets are normally distributed.

  • Compute historic transition matrix. Figure 11.3 uses the matrix for a BB rated loan.

  • Suppose that historically, there is a 1.06% probability of transition to default. This corresponds to 2.3 standard deviations below the mean on the standard normal distribution.

  • Similarly, if there is a 8.84% probability of downgrade from BB to B, this corresponds to 1.23 standard deviations below the mean.

Saunders & Allen Chapters 10 & 11


Joint transition matrix

Joint Transition Matrix

  • Can draw a figure like Fig. 11.3 for the A rated obligor. There is a 0.06% PD, corresponding to 3.24 standard deviations below the mean; a 5.52% probability of downgrade from A to BBB, corresponding to 1.51 std dev below the mean.

  • The joint probability of both borrowers retaining their BBB and A ratings is: the probability that obligor 1’s assets fluctuate between –1.23 to +1.37 and obligor 2’s assets between –1.51 to +1.98 with a correlation coefficient=0.2. Calculated to equal 73.65%.

Saunders & Allen Chapters 10 & 11


Loan portfolio selection and risk measurement

Saunders & Allen Chapters 10 & 11


Calculating correlation coefficients

Calculating Correlation Coefficients

  • Estimate systematic risk of each loan – the relationship between equity returns and returns on market/industry indices.

  • Estimate the correlation between each pair of market/industry indices.

  • Calculate the correlation coefficient as the weighted average of the systematic risk factors x the index correlations.

Saunders & Allen Chapters 10 & 11


Two loan example of correlation calculation

Two Loan Example of Correlation Calculation

  • Estimate the systematic risk of each company by regressing the stock returns for each company on the relevant market/industry indices.

  • RA = .9RCHEM + UA

  • RZ = .74RINS + .15RBANK + UZ

  • A,Z=(.9)(.74)CHEM,INS + (.9)(.15)CHEM,BANK

  • Estimate the correlation between the indices.

  • If CHEM,INS =.16 and CHEM,BANK =.08, then AZ=0.1174.

Saunders & Allen Chapters 10 & 11


Joint loan values

Joint Loan Values

  • Table 11.1 shows the joint migration probabilities.

  • Calculate the portfolio’s value under each of the 64 possible credit migration possibilities (using methodology in Chap.6) to obtain the values in Table 11.3.

  • Can draw the portfolio value distribution using the probabilities in Table 11.1 and the values in Table 11.3.

Saunders & Allen Chapters 10 & 11


Credit var measures

Credit VAR Measures

  • Calculate the mean using the values in Table 11.3 and the probabilities in Tab 11.1.

    • Mean =

    • Variance =

    • Mean=$213.63 million

    • Standard deviation= $3.35 million

Saunders & Allen Chapters 10 & 11


Calculating the 99 th percentile credit var under normal distribution

Calculating the 99th percentile credit VAR under normal distribution

  • 2.33 x $3.35 = $7.81 million

  • Benefits of diversification. The BBB loan’s credit VAR (alone) was $6.97million. Combining 2 loans with correlations=0.3, reduces portfolio risk considerably.

Saunders & Allen Chapters 10 & 11


Calculating the credit var under the actual distribution

Calculating the Credit VAR Under the Actual Distribution

  • Adding up the probabilities (from Table 11.1) in the lowest valuation region in Table 11.3, the 99th percentile credit VAR using the actual (not normal) distribution is $204.4 million.

  • Unexpected Losses=$213.63m - $204.4m = $9.23 million (>$7.81m).

  • If the current value of the portfolio = $215m, then Expected Losses=$215m - $213.63m = $1.37m.

Saunders & Allen Chapters 10 & 11


Creditmetrics with more than 2 loans in the portfolio

CreditMetrics with More Than 2 Loans in the Portfolio

  • Cannot calculate joint transition matrices for more than 2 loans because of computational difficulties: A 5 loan portfolio has over 32,000 joint transitions.

  • Instead, calculate risk of each pair of loans, as well as standalone risk of each loan.

  • Use Monte Carlo simulation to obtain 20,000 (or more) possible asset values.

Saunders & Allen Chapters 10 & 11


Monte carlo simulation

Monte Carlo Simulation

  • First obtain correlation matrix (for each pair of loans) using the systematic risk component of equity prices. Table 11.5

  • Randomly draw a rating for each loan from that loan’s distribution (historic rating migration) using the asset correlations.

  • Value the portfolio for each draw.

  • Repeat 20,000 times! New algorithms reduce some of the computational requirements.

  • The 99th% VAR based on the actual distribution is the 200th worst value out of the 20,000 portfolio values.

Saunders & Allen Chapters 10 & 11


Mpt using creditmetrics

MPT Using CreditMetrics

  • Calculate each loan’s marginal risk contribution = the change in the portfolio’s standard deviation due to the addition of the asset into the portfolio.

  • Table 11.6 shows the marginal risk contribution of 20 loans – quite different from standalone risk.

  • Calculate the total risk of a loan using the marginal contribution to risk = Marginal standard deviation x Credit Exposure. Shown in column (5) of Table 11.6.

Saunders & Allen Chapters 10 & 11


Figure 11 4

Figure 11.4

  • Plot total risk exposure using marginal risk contributions (column 6 of Table 11.6) against the credit exposure (column 5 of Table 11.4).

  • Draw total risk isoquants using column 5 of Table 11.6.

  • Find risk outliers such as asset 15 which have too much portfolio risk ($270,000) for the loan’s size ($3.3 million).

  • This analysis is not a risk-return tradeoff. No returns.

Saunders & Allen Chapters 10 & 11


Loan portfolio selection and risk measurement

Saunders & Allen Chapters 10 & 11


Default correlations using reduced form models

Default Correlations Using Reduced Form Models

  • Events induce simultaneous jumps in default intensities.

  • Duffie & Singleton (1998): Mean reverting correlated Poisson arrivals of randomly sized jumps in default intensities.

  • Each asset’s conditional PD is a function of 4 parameters: h (intensity of default process);  (constant arrival prob.); k (mean reversion rate);  (steady state constant default intensity).

  • The jumps in intensity follow an exponential distribution with mean size of jump=J.

  • So: probability of survival from time t to s:

Saunders & Allen Chapters 10 & 11


Numerical example

Numerical Example

  • Suppose that =.002, k=.5, =.001, J=5, h(0)=.001 (corresponds to an initial rating of AA).

  • Correlations across loan default probabilities:

  • Vc=common factor; V=idiosyncratic factor. As v0, corr0 As v1, corr1.

  • If v=.02, V=.001, Vc=.05: the probability that loani intensity jumps given that loanj has experienced a jump is = vVc/(Vc+V) = 2%. If v= .05 (instead of .02), then the probability increases to 5%.

  • Figure 11.5 shows correlated jumps in default intensities.

  • Figure 11.6 shows the impact of correlations on the portfolio’s risk.

Saunders & Allen Chapters 10 & 11


Loan portfolio selection and risk measurement

Saunders & Allen Chapters 10 & 11


Loan portfolio selection and risk measurement

Saunders & Allen Chapters 10 & 11


Appendix 11 1 valuing a loan that matures after the credit horizon kmv pm

Appendix 11.1: Valuing a Loan that Matures after the Credit Horizon – KMV PM

  • Maturity=M3 in Figure 11.1. Use MTM to value loans.

  • Four Step Process:

    • 1. Valuation of an individual firm’s assets using random sampling of risk factors.

    • 2. Loan valuation based on the EDFs implied by the firm’s asset valuation.

    • 3. Aggregation of individual loan values to construct portfolio value.

    • 4. Calculation of excess returns and losses for portfolio.

  • Yields a single estimate for expected returns (losses) for each loan in the portfolio. Use Monte Carlo simulation (repeated 50,000 to 200,000 times) to trace out distribution

Saunders & Allen Chapters 10 & 11


Step 1 valuation of firm assets at 3 time horizons fig 11 7

Step 1: Valuation of Firm Assets at 3 Time Horizons – Fig. 11.7

  • A0 , AH , AMvaluations. Stochastic process generating AH, AM:

  • The random component = systematic portion f + firm-specific portion u. Each simulation draws another risk factor.

  • Using AH andAMcan calculate EDFH and EDFM

Saunders & Allen Chapters 10 & 11


Step 2 loan valuation using term structure of edfs

Step 2: Loan Valuation Using Term Structure of EDFs

  • Convert EDF into QDF by removing risk-adjusted ROR.

  • Also value loan as of credit horizon date H:

Saunders & Allen Chapters 10 & 11


Step 3 aggregation to construct portfolio

Step 3: Aggregation to Construct Portfolio

  • Sum the expected values VHfor all loans in the portfolio.

Saunders & Allen Chapters 10 & 11


Step 4 calculation of excess returns losses

Step 4: Calculation of Excess Returns/Losses

  • Excess Returns on the Portfolio:

  • Expected Loss on the Portfolio:

  • Repeat steps 1 through 4 from 50,000 to 200,000 times.

Saunders & Allen Chapters 10 & 11


A case study kmv pm valuation of 5 yr maturity 1 loan paying a fixed rate of 10 p a

A Case Study: KMV PM valuation of 5 yr maturity $1 loan paying a fixed rate of 10% p.a.

  • Using Table 11.8:

Saunders & Allen Chapters 10 & 11


Valuing the loan at the credit horizon date 1

Valuing the Loan at the Credit Horizon Date =1

  • Using Table 11.9:

Saunders & Allen Chapters 10 & 11


Kmv s private firm model

KMV’s Private Firm Model

  • Calculate EBITDA for private firm j in industryj.

  • Calculate the average equity mulitple for industryi by dividing the industry average MV of equity by the industry average EBITDA.

  • Obtain an estimate of the MV of equity for firm j by multiplying the industry equity multiple by firm j’s EBITDA.

  • Firm j’s assets = MV of equity + BV of debt

  • Then use valuation steps as in public firm model.

Saunders & Allen Chapters 10 & 11


Credit risk plus model 2 incorporating systematic linkages in mean default rates

Credit Risk Plus Model 2 - Incorporating Systematic Linkages in Mean Default rates

  • Mean default rate is a function of factor sensitivities to different independent sectors (industries or countries).

  • Table 11.7 shows as example of 2 loans sensitive to a single factor (parameters reflect US national default rates). As credit quality declines (m gets larger), correlations get larger.

Saunders & Allen Chapters 10 & 11


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