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| Introduction. | |
| The Theoretical Set-Up. | |
| The LIBOR Market Model. | |
| Definitions | |
| The Volatility Functions | |
| Separating the Correlation from the Volatility Term | |
| The Caplet-Pricing Condition Again | |
| The Forward-Rate/Forward-Rate Correlation | |
| Possible Shapes of the Doust Correlation Function | |
| The Covariance Integral... MORE | |
| The SABR Model. | |
| The SABR Model (and Why It Is a Good Model | |
| Description of the Model | |
| The Option Prices Given by the SABR Model | |
| Special Cases | |
| Qualitative Behaviour of the SABR Model | |
| The Link Between the Exponent, _, and the Volatility of Volatility, _ | |
| Volatility Clustering in the (LMM)-SABR Model | |
| The Market | |
| How Do We Know that the Market Has Chosen _ = 0:5? | |
| The Problems with the SABR Model | |
| The LMM-SABR Model. | |
| The Equations of Motion | |
| The Nature of the Stochasticity Introduced by Our Model | |
| A Simple Correlation Structure | |
| A More General Correlation Structure | |
| Observations on the Correlation Structure | |
| The Volatility Structure | |
| What We Mean by Time Homogeneity | |
| The Volatility Structure in Periods of Market Stress | |
| A More General Stochastic Volatility Dynamics | |
| Calculating the No-Arbitrage Drifts | |
| IMPLEMENTATION AND CALIBRATION. | |
| Calibrating the LMM-SABR model to Market Caplet Prices. | |
| The Caplet-Calibration Problem | |
| Choosing the Parameters of the Function, g (_), and the Initial Values, kT 0 | |
| Choosing the Parameters of the Function h(_ | |
| Choosing the Exponent, _, and the Correlation, _SABR | |
| Results | |
| Calibration in Practice: Implications for the SABR Model | |
| Implications for Model Choice | |
| Calibrating the LMM-SABR model to Market Swaption Prices. | |
| The Swaption Calibration Problem | |
| Swap Rate and Forward Rate Dynamics | |
| Approximating the Instantaneous Swap Rate Volatility, St | |
| Approximating the Initial Value of the Swap Rate Volatility, _0 (First Route | |
| Approximating _0 | |
| Approximating the Swap-Rate/Swap-Rate-Volatility Correlation, RSABR | |
| Approximating the Swap Rate Exponent, B | |
| Results | |
| Conclusions and Suggestions for Future Work | |
| Appendix: Derivation of Approximate Swap Rate Volatility | |
| Appendix: Derivation of Swap-Rate/Swap-Rate-Volatility Correlation, RSABR | |
| Appendix: Approximation of | |
| Calibrating the Correlation Structure. | |
| Statement of the Problem | |
| Creating a Valid Model Matrix | |
| A Case Study: Calibration Using the Hypersphere Method | |
| Which Method Should One Choose? | |
| Appendix1 | |
| EMPIRICAL EVIDENCE. | |
| The Empirical Problem. | |
| Statement of the Empirical Problem | |
| What Do We know from the Literature? | |
| Data Description | |
| Distributional Analysis and Its Limitations | |
| What Is the True Exponent _? | |
| Appendix: Some Analytic Results | |
| Estimating the Volatility of the Forward Rates. | |
| Expiry-Dependence of Volatility of Forward Rates | |
| Direct Estimation | |
| Looking at the Normality of the Residuals | |
| Maximum-Likelihood and Variations on the Theme | |
| Information About the Volatility from the Options Market | |
| Overall Conclusions | |
| Estimating the Correlation Structure. | |
| What We Are Trying To Do | |
| Some Results from Random Matrix Theory | |
| Empirical Estimation | |
| Descriptive Statistics | |
| Signal and Noise in the Empirical Correlation Blocks | |
| What Does Random Matrix Theory Really Tell Us? | |
| Calibrating the Correlation Matrices | |
| How Much Information Do the Proposed Models Retain? | |
| HEDGING. | |
| Various Types of Hedging. | |
| Statement of the Problem | |
| Three Types of Hedging | |
| Definitions | |
| First-Order Derivatives with Respect to the Underlyings | |
| Second-Order Derivatives with Respect to the Underlyings | |
| Generalizing Functional-Dependence Hedging | |
| How Does the Model Know about Volga and Vanna? | |
| Choice of Hedging Instrument | |
| Hedging Against Moves in the Forward Rate and in the Volatility. | |
| Delta Hedging in the SABR-(LMM) Model | |
| Vega Hedging in the SABR-(LMM) Model | |
| (LMM)-SABR Hedging in Practice: Evidence from Market Data. | |
| Purpose of this Chapter | |
| Notation | |
| Hedging Results for the SABR Model | |
| Hedging Results for the LMM-SABR Model | |
| Conclusions | |
| Hedging the Correlation Structure. | |
| The Intuition Behind the Problem | |
| Hedging the Forward-Rate Block | |
| Hedging the Volatility-Rate Block | |
| Hedging the Forward-Rate/Volatility Block | |
| Final Considerations | |
| Hedging in Conditions of Market Stress. | |
| Statement of the Problem | |
| The Volatility Function | |
| The Case Study | |
| Hedging | |
| Results | |
| Are We Getting Something for Nothing? | |
| Table of Contents provided by Publisher. All Rights Reserved. |
Kenneth McKay is a PhD student at the London School of Economics following a first class honours degree in Mathematics and Economics from the LSE and an MPhil in Finance from Cambridge University. He has been working on interest rate derivative-related research with Riccardo Rebonato for the past year.
Richard White holds a doctorate in Particle Physics from Imperial College London, and a first class honours degree in Physics from Oxford University. He held a Research Associate position at Imperial College before joining RBS in 2004 as a Quantitative Analyst. His research interests include option pricing with Levy Processes, Genetic Algorithms for portfolio optimisation, and Libor Market Models with stochastic volatility. He is currently taking a fortuitously timed sabbatical to pursue his joint passion for travel and scuba diving.