Book description
Risk control, capital allocation, and realistic derivative pricing and hedging are critical concerns for major financial institutions and individual traders alike. Events from the collapse of Lehman Brothers to the Greek sovereign debt crisis demonstrate the urgent and abiding need for statistical tools adequate to measure and anticipate the amplitude of potential swings in the financial market's from ordinary stock price and interest rate moves, to defaults, to those increasingly frequent "rare events" fashionably called black swan events. Yet many on Wall Street continue to rely on standard models based on artificially simplified assumptions that can lead to systematic (and sometimes catastrophic) underestimation of real risks.
In Practical Methods of Financial Engineering and Risk Management, Dr. Rupak Chatterjee former director of the multi-asset quantitative research group at Citiintroduces finance professionals and advanced students to the latest concepts, tools, valuation techniques, and analytic measures being deployed by the more discerning and responsive Wall Street practitioners, on all operational scales from day trading to institutional strategy, to model and analyze more faithfully the real behavior and risk exposure of financial markets in the cold light of the post-2008 realities. Until one masters this modern skill set, one cannot allocate risk capital properly, price and hedge derivative securities realistically, or risk-manage positions from the multiple perspectives of market risk, credit risk, counterparty risk, and systemic risk.
The book assumes a working knowledge of calculus, statistics, and Excel, but it teaches techniques from statistical analysis, probability, and stochastic processes sufficient to enable the reader to calibrate probability distributions and create the simulations that are used on Wall Street to valuate various financial instruments correctly, model the risk dimensions of trading strategies, and perform the numerically intensive analysis of risk measures required by various regulatory agencies.
Table of contents
- Cover
- Halftitle1
- Halftitle2
- Title
- Copyright
- Dedication
- Contents at a Glance
- Contents
- Series Editors’ Foreword
- About the Author
- About the Technical Reviewer
- Acknowledgments
- Introduction
- Chapter 1: Financial Instruments
- Chapter 2: Building a Yield Curve
-
Chapter 3: Statistical Analysis of Financial Data
- Tools in Probability Theory
- Creating Random Variables and Distributions
- Calibrating Distributions through Moment Matching
- Basic Risk Measures
- The Term Structure of Statistics
- Dynamic Portfolio Allocation
- Appendix. Joint Distributions and Correlation
-
Problems
- Problem 3-1. Create a Gaussian Random Number Generator in Excel
- Problem 3-2. Create a Mixture of Gaussians in Excel
- Problem 3-3. Calibrate S&P 500 Returns to a Mixed Normal in Excel
- Problem 3-4. Calibrate SX5E Returns to a Student’s-t distribution in Excel
- Problem 3-5. Create a Skew Normal Distribution in Excel
- Problem 3-6. VaR and CVaR
- Problem 3-7. Term Structure of Statistics
- References
-
Chapter 4: Stochastic Processes
- Stochastic Calculus
- Geometric Brownian Motion and Monte Carlo Simulations
- GARCH Process for Stock Returns
- Statistical Modeling of Trading Strategies
- Appendix A. Black-Scholes with Holes
- Appendix B. Moment Matching and Binomial Trees
-
Problems
- Problem 4-1. Create a Brownian Motion Process for Stock Returns Using Monte Carlo Simulations in Excel
- Problem 4-2. Ito’s Lemma
- Problem 4-3. Calibrate a GARCH(1,1) Process for SX5E
- Problem 4-4. Create a GARCH(1,1) Simulator in Excel
- Problem 4-5. Volume Adjustment for Pairs Trading for MCD versus XLY
- References
-
Chapter 5: Optimal Hedging Monte Carlo Methods
- Dynamic Hedging and Replication
- Wealth Change Equations: Spot, Forwards, and Options
- The OHMC Optimization Problem and Solution Methodology
- Risk Capital
-
The OHMC Examples
- Hedge Fund Index: GARCH Calibration to Daily Returns
- Option Pricing: Hedge Fund Index: 1.20Yr 110% Strike Call, 2 Day Liquidity
- Option Pricing: Hedge Fund Index: 1.20Yr 99% Strike Put, 2 Day Liquidity
- Dynamic Portfolio Allocation Index: GARCH Calibration to Daily Returns
- Option Pricing: Dynamic Portfolio Allocation: 2.00Yr 110% Strike Call, 5 Day Liquidity
- Option Pricing: Dynamic Portfolio Allocation: 2.00Yr 95% Strike Put, 5 Day Liquidity
- Hedge Fund Index: GARCH Calibration to Monthly Returns
- Option Pricing: Hedge Fund Index: 3.00Yr 100% Strike Put, 3-Month Liquidity
- Option Pricing: Hedge Fund Index: 3.00-Yr 110% Strike Call, 3-Month Liquidity
- Cliquet Contracts
- Knockout Cliquet Sellers Wealth Change Equation
- Problems
- References and Further Reading
-
Chapter 6: Introduction to Credit Derivatives
- The CDS Contract: Overview
- The CDS Contract: Pricing
- Intensity-Based Reduced-Form Default Models
- Bootstrapping a Survival Curve with Piecewise Constant Hazard Rates
- Credit Triangle
- Quotation Conventions for Standard Contracts
- Par Asset Swaps
- Collateralization
- Correlation and Copulas
- Stochastic Hazard Rates
- OHMC and the Static Hedging of a Risky Bond with a CDS
- OHMC and CDS Swaptions
- Appendix. Bloomberg Functionality
- Problems
- References
- Chapter 7: Risk Types, CVA, Basel III, and OIS Discounting
- Chapter 8: Power Laws and Extreme Value Theory
- Chapter 9: Hedge Fund Replication
- Index
Product information
- Title: Practical Methods of Financial Engineering and Risk Management
- Author(s):
- Release date: August 2014
- Publisher(s): Apress
- ISBN: 9781430261346
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