Perturbation Methods in Credit Derivatives

Perturbation Methods in Credit Derivatives
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Stress-test financial models and price credit instruments with confidence and efficiency using the perturbation approach taught in this expert volume   Perturbation Methods in Credit Derivatives: Strategies for Efficient Risk Management  offers an incisive examination of a new approach to pricing credit-contingent financial instruments. Author and experienced financial engineer Dr. Colin Turfus has created an approach that allows model validators to perform rapid benchmarking of risk and pricing models while making the most efficient use possible of computing resources.  The book provides innumerable benefits to a wide range of quantitative financial experts attempting to comply with increasingly burdensome regulatory stress-testing requirements, including:  Replacing time-consuming Monte Carlo simulations with faster, simpler pricing algorithms for front-office quants Allowing CVA quants to quantify the impact of counterparty risk, including wrong-way correlation risk, more efficiently Developing more efficient algorithms for generating stress scenarios for market risk quants Obtaining more intuitive analytic pricing formulae which offer a clearer intuition of the important relationships among market parameters, modelling assumptions and trade/portfolio characteristics for traders The methods comprehensively taught in  Perturbation Methods   in Credit Derivatives  also apply to CVA/DVA calculations and contingent credit default swap pricing.

Оглавление

Colin Turfus. Perturbation Methods in Credit Derivatives

Table of Contents

List of Tables

List of Illustrations

Guide

Pages

Perturbation Methods in Credit Derivatives. Strategies for Efficient Risk Management

Preface

Note

Acknowledgments

Acronyms

CHAPTER 1 Why Perturbation Methods? 1.1 ANALYTIC PRICING OF DERIVATIVES

1.2 IN DEFENCE OF PERTURBATION METHODS

NOTE

CHAPTER 2 Some Representative Case Studies

2.1 QUANTO CDS PRICING

2.2 WRONG‐WAY INTEREST RATE RISK

2.3 CONTINGENT CDS PRICING AND CVA

2.4 ANALYTIC INTEREST RATE OPTION PRICING

2.5 EXPOSURE SCENARIO GENERATION

2.6 MODEL RISK

2.7 MACHINE LEARNING

2.8 INCORPORATING INTEREST RATE SKEW AND SMILE

NOTE

CHAPTER 3 The Mathematical Foundations. 3.1 THE PRICING EQUATION

3.2 PRICING KERNELS. 3.2.1 What Is a Kernel?

3.2.2 Kernels in Financial Engineering

3.2.3 Why Use Pricing Kernels?

3.3 EVOLUTION OPERATORS

3.3.1 Time‐Ordered Exponential

3.3.2 Magnus Expansion

3.4 OBTAINING THE PRICING KERNEL

3.4.1 Duhamel–Dyson Expansion Formula

3.4.2 Baker–Campbell–Hausdorff Expansion Formula

3.4.3 Exponential Expansion Formula

3.4.4 Exponentials of Derivatives

3.4.5 Example – The Black–Scholes Pricing Kernel

3.4.6 Example – Mean‐Reverting Diffusion

3.5 CONVOLUTIONS WITH GAUSSIAN PRICING KERNELS

3.6 PROOFS FOR CHAPTER 3

3.6.1 Proof of Theorem 3.2

3.6.2 Proof of Lemma 3.1

NOTES

CHAPTER 4 Hull–White Short‐Rate Model

4.1 BACKGROUND OF HULL–WHITE MODEL

4.2 THE PRICING KERNEL

4.3 APPLICATIONS. 4.3.1 Zero Coupon Bond Pricing

4.3.2 LIBOR Pricing

4.3.3 Caplet Pricing

4.3.4 European Swaption Pricing

4.4 PROOF OF THEOREM 4.1. 4.4.1 Preliminary Results

4.4.2 Turn the Handle!

NOTES

CHAPTER 5 Black–Karasinski Short‐Rate Model

5.1 BACKGROUND OF BLACK–KARASINSKI MODEL

5.2 THE PRICING KERNEL

5.3 APPLICATIONS

5.3.1 Zero Coupon Bond Pricing

5.3.2 Caplet Pricing

5.3.3 European Swaption Pricing

5.4 COMPARISON OF RESULTS

5.5 PROOF OF THEOREM 5.1. 5.5.1 Preliminary Result

5.5.2 Turn the Handle!

5.6 EXACT BLACK–KARASINSKI PRICING KERNEL

NOTES

CHAPTER 6 Extension to Multi‐Factor Modelling. 6.1 MULTI‐FACTOR PRICING EQUATION

6.2 DERIVATION OF PRICING KERNEL. 6.2.1 Preliminaries

6.2.2 Full Solution Using Operator Expansion

6.3 EXACT EXPRESSION FOR HULL–WHITE MODEL

6.4 ASYMPTOTIC EXPANSION FOR BLACK–KARASINSKI MODEL

6.5 FORMAL SOLUTION FOR RATES‐CREDIT HYBRID MODEL

NOTE

CHAPTER 7 Rates‐Equity Hybrid Modelling. 7.1 STATEMENT OF PROBLEM

7.2 PREVIOUS WORK

7.3 THE PRICING KERNEL. 7.3.1 Main Result

7.4 VANILLA OPTION PRICING

CHAPTER 8 Rates‐Credit Hybrid Modelling. 8.1 BACKGROUND. 8.1.1 Black–Karasinski as a Credit Model

8.1.2 Analytic Pricing of Rates‐Credit Hybrid Products

8.1.3 Mathematical Definition of the Model

8.1.4 Pricing Credit‐Contingent Cash Flows

8.2 THE PRICING KERNEL

8.3 CDS PRICING

8.3.1 Risky Cash Flow Pricing

8.3.2 Protection Leg Pricing

8.3.3 Defaultable LIBOR Pricing

8.3.4 Defaultable Capped LIBOR Pricing

8.3.5 Contingent CDS with IR Swap Underlying

NOTES

CHAPTER 9 Credit‐Equity Hybrid Modelling. 9.1 BACKGROUND

9.2 DERIVATION OF CREDIT‐EQUITY PRICING KERNEL. 9.2.1 Pricing Equation

9.2.2 Pricing Kernel

9.2.3 Asymptotic Expansion

9.3 CONVERTIBLE BONDS

9.4 CONTINGENT CDS ON EQUITY OPTION

NOTES

CHAPTER 10 Credit‐FX Hybrid Modelling. 10.1 BACKGROUND

10.2 CREDIT‐FX PRICING KERNEL

10.3 QUANTO CDS

10.3.1 Domestic Currency Fixed Flow

10.3.2 Foreign Currency Fixed Flow

10.3.3 Foreign Currency LIBOR Flow

10.3.4 Foreign Currency Notional Protection

10.4 CONTINGENT CDS ON CROSS‐CURRENCY SWAPS

CHAPTER 11 Multi‐Currency Modelling

11.1 PREVIOUS WORK

11.2 STATEMENT OF PROBLEM

11.3 THE PRICING KERNEL. 11.3.1 Main Result

11.3.2 Derivation of Multi‐Currency Pricing Kernel

11.4 INFLATION AND FX OPTIONS

NOTE

CHAPTER 12 Rates‐Credit‐FX Hybrid Modelling. 12.1 PREVIOUS WORK

12.2 DERIVATION OF RATES‐CREDIT‐FX PRICING KERNEL. 12.2.1 Pricing Equation

12.2.2 Pricing Kernel

12.3 QUANTO CDS REVISITED

12.3.1 Domestic Currency Fixed Flow

12.3.2 Foreign Currency Fixed Flow

12.3.3 Foreign Currency Notional Protection

12.4 CCDS ON CROSS‐CURRENCY SWAPS REVISITED

CHAPTER 13 Risk‐Free Rates. 13.1 BACKGROUND

13.2 HULL–WHITE KERNEL EXTENSION

13.3 APPLICATIONS. 13.3.1 Compounded Rates Payment

13.3.2 Caplet Pricing

13.3.3 European Swaption Pricing

13.3.4 Average Rate Options

13.4 BLACK–KARASINSKI KERNEL EXTENSION

13.5 APPLICATIONS. 13.5.1 Compounded Rates Payment

13.5.2 Caplet Pricing

13.6 A NOTE ON TERM RATES

NOTES

CHAPTER 14 Multi‐Curve Framework. 14.1 BACKGROUND

14.2 STOCHASTIC SPREADS

14.3 APPLICATIONS. 14.3.1 LIBOR Pricing

14.3.2 LIBOR Caplet Pricing

14.3.3 European Swaption Pricing

CHAPTER 15 Scenario Generation. 15.1 OVERVIEW

15.2 PREVIOUS WORK

15.3 PRICING EQUATION

15.4 HULL–WHITE RATES. 15.4.1 Two‐Factor Pricing Kernel

15.4.2 m‐Factor Extension

15.5 BLACK–KARASINSKI RATES. 15.5.1 Two‐Factor Pricing Kernel

15.5.2 Asymptotic Expansion

15.5.3 m‐Factor Extension

15.5.4 Representative Calculations

15.6 JOINT RATES‐CREDIT SCENARIOS

NOTES

CHAPTER 16 Model Risk Management Strategies. 16.1 INTRODUCTION

16.2 MODEL RISK METHODOLOGY. 16.2.1 Previous Work

16.2.2 Proposed Framework

16.2.3 Calibration to CDS Market

16.3 APPLICATIONS

16.3.1 Interest Rate Swap Extinguisher

16.3.2 Contingent CDS

16.4 CONCLUSIONS

NOTES

CHAPTER 17 Machine Learning. 17.1 TRENDS IN QUANTITATIVE FINANCE RESEARCH. 17.1.1 Some Recent Trends

17.1.2 The Arrival of Machine Learning

17.2 FROM PRICING MODELS TO MARKET GENERATORS

17.3 SYNERGIES WITH PERTURBATION METHODS

17.3.1 Asymptotics as Control Variates

17.3.2 Data Representation

NOTES

Bibliography

Index

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Another desk meanwhile trading hybrid products into emerging markets notices that the bank's pricing library now provides production‐quality analytic methods for option pricing under the Black–Karasinski model. They frequently use this model in preference to Hull–White as an interest rate model, as they find it performs better in market conditions with high and volatile interest rates. They are interested in what analytic functions are available and see that in Chapter 5 there are explicit formulae for caplets, swaptions and zero coupon bonds (stochastic discount factors) which they consider could be useful, particularly in the process of model calibration, where pricing of calibration instruments must otherwise be done by repeated Monte Carlo simulation.

They note in addition that results in Chapter 14 allow calibration of the Black–Karasinski model in a multi‐curve framework where the LIBOR spread(s) over the risk‐free rate can be stochastic and potentially correlated with the risk‐free rate. Furthermore, they note that results in Chapter 13 facilitate the extension of Black–Karasinski option pricing formulae enabling the model to be conveniently calibrated to caps referencing backward‐looking risk‐free rates, as and when a market in these inevitably appears in the post‐IBOR world to which the finance industry is currently headed.

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