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 multiasset 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 post2008 realities. Until one masters this modern skill set, one cannot allocate risk capital properly, price and hedge derivative securities realistically, or riskmanage 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
 Title
 Copyright
 Dedication
 Contents at a Glance
 Contents
 Series Editor’s Foreword
 About the Author
 About the Technical Reviewers
 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 31. Create a Gaussian Random Number Generator in Excel
 Problem 32. Create a Mixture of Gaussians in Excel
 Problem 33. Calibrate S&P 500 Returns to a Mixed Normal in Excel
 Problem 34. Calibrate SX5E Returns to a Student’st distribution in Excel
 Problem 35. Create a Skew Normal Distribution in Excel
 Problem 36. VaR and CVaR
 Problem 37. 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. BlackScholes with Holes
 Appendix B. Moment Matching and Binomial Trees

Problems
 Problem 41. Create a Brownian Motion Process for Stock Returns Using Monte Carlo Simulations in Excel
 Problem 42. Ito’s Lemma
 Problem 43. Calibrate a GARCH(1,1) Process for SX5E
 Problem 44. Create a GARCH(1,1) Simulator in Excel
 Problem 45. 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, 3Month Liquidity
 Option Pricing: Hedge Fund Index: 3.00Yr 110% Strike Call, 3Month 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
 IntensityBased ReducedForm 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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