Κλειστό

•Develop a model of tail dependent asset returns for measuring portfolio credit risk, utilizing nested Archimedean copulas to allow for stronger dependence within sub-portfolios •Modify the nesting procedures to ensure compatibility of copula generator

2. Key outputs

• Construct an easy-to-use/modify model using Matlab (or any other recommended software which simplify scope for future modifications e.g. Wolfram Mathematica) –

i. Important Note: Please provide high degree of details for model set-up, design and in particular, line by line explanation of coding for ease of application by junior researchers

• Provide details of a particular specification based on a gamma mixture of powers

• Demonstrate extent of model risk when calculating VaR or expected shortfall for a credit portfolio

• 3000 word write-up (good mix of non-technical summaries, theoretical explanation, step-by-step guide for direct application and technical details)

• Rough guide of paper outline

i. Abstract

ii. Introduction and stimulus

1. Major challenges for banks’ assessment of credit risk in lending portfolios (e.g. interdependent default events), thus the need for capturing dependence between rare events

2. Tail dependence of properties of joint distribution of asset values/returns would determine how frequently low probability events (e.g. simultaneous defaults of several large obligors) occurs, thus affecting amount of unexpected loss and capital buffers

3. Explain how Gaussian model may therefore lead to systematic underestimation of portfolio credit risk/capital buffers

4. Include brief discussion of features/present applications/pros/cons of different forms of copulas, incl. of course nested Archimedean copulas)

iii. Credit portfolio setting

iv. Model set-up

1. Derivation of the copula

2. Properties of copula

3. Sampling algorithm

v. Application to test portfolios

1. Hypothetical test portfolios to examine model risk compared with 1-2 benchmark specification (Gaussian included)

a. Try publically available credit exposures by ratings/categories (S&P, Moody’s or Rating Agency Malaysia)

b. In the meantime, I will find other sdata ource of credit exposures

2. Parameter calibration

3. Results of simulation

vi. Conclusions (technical and non-technical)

vii. Good graphical representations of results (with clear explanation for each) and tables

1. Contour plots of independence and maximum copulas

2. Contour and scatter plots of Clayton and CG copulas

3. Pairwise scatter plots of HAC

4. Tail dependence for Clayton and CG copulas

5. Tail function of portfolio loss

6. Comparison of VaR for Gaussian and HAC models

7. Structure of small test portfolio

8. Model parameters used in simulation

9. Comparison of VaR and ES for different settings

10. Parameter sensitivity

• Conclusion (theoretical, practical applications e.g. stress testing, scope for future enhancements)

• References

Ικανότητες: Matlab and Mathematica

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Σχετικά με τον Εργοδότη:
( 0 αξιολογήσεις ) Malaysia

Ταυτότητα Εργασίας: #6871642

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