Margin in Derivatives Trading

Margin in Derivatives Trading

Global Macro

Global Macro

Market Risk Modelling (2nd Edition)


This fully updated and revised second edition of Market Risk Modelling expands to incorporate the vast developments in the risk management landscape since the first edition, both in terms of advances in statistical techniques and their application. With new material focusing on key topics such as tail risk modelling and stochastic forecasting, Market Risk Modelling describes easily implementable tools and approaches for use by the time-starved risk manager.

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The second edition of Market Risk Modelling examines the latest developments and updates in statistical methods used to solve the day-to-day problems faced by a risk manager. After almost a decade since the publication of the first edition, this book considers new risk management methodologies, approaches and packages.

Bringing together a wide variety of statistical methods and models that have proven their worth in risk management, Market Risk Modelling provides practical examples and easily implementable approaches, whereby readers can integrate the underlying quantitative concepts into their pre-existing risk management systems.

Written by market risk expert, Nigel Da Costa Lewis, this second edition gives concise and applied explanations of approaches to market risk modelling, demonstrated using relevant, applicable examples. Designed for the time-starved risk manager as both a working manual and a compact reference guide, this book provides rapid and succinct access to what can be an intimidating and complex subject.

The value of market risk statistical analysis in resource and performance evaluation and setting trading limits is long-established. Statistical methods provide an objective assessment of the risks facing a financial institution and, as importantly, offer their potential clients a fully transparent risk profile of products and services. Market Risk Modelling, Second Edition covers the topics key to risk modelling and management, such as EVT, principle components and fitting probability distributions.

A quickly digestible reference to this rapidly evolving field, Market Risk Modelling, Second Edition is a must read for all risk management professionals and quants who need practical and applicable insight into this vitally important subject.

More Information
ISBN 9781906348779
Navision code MMR2
Publication date 21 Nov 2012
Size 155mm x 235mm
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Nigel Da Costa Lewis

Dr Nigel D. Costa Lewis has been a “quant” on a trading floor, a Chief Risk Officer, Managing Director on a $100bn portfolio, and taught economics and statistics at university. He has written five books on investment risk and published numerous journal articles. His most recent work has appeared in the Journal of Investing, Journal of Wealth Management and the Journal of Pensions.   An international speaker, his innovative, original and insightful keynote talks on investment risk have been presented at the Pension Review Board, Financial Planning Association, the Financial Services Professionals Association, Texas Association of Public Employee Retirement Systems and numerous other business and financial organizations. A great advocate of servant leadership he remains very active in the investment industry. Amongst his many roles, Dr. Lewis is a member of the technical advisory board of the Investment Management Consultants Association, helping design a risk-based curriculum for their 10,000 members. He obtained his PhD from the University of Cambridge and now spends his time writing, speaking and consulting on all things risk out of his homestead in the hill country of Texas.


1  Risk Modelling and its Myths

2  Mastering the R Statistical Package

Getting Started in R
How to Create and Manipulate Objects
Managing your R Workspace
Writing Functions in a Nutshell
Graphical Output
Programming in the R Language

3  Key Concepts on Probability

The Basics of Random Variables
Risk Factors, Instruments, Random Variables and Mapping
Probability Mass Function and Probability Density Function
Cumulative Distribution Function
Percentiles and Percentile Function
Bivariate Joint and Marginal Distribution Functions
Multivariate Joint and Marginal Distribution Functions
Conditional Expectation
Variance and Standard Deviation
Covariance and Correlation
Six Useful Rules For Correlation, Variance and Covariance
A Note on Populations and Samples
Relevance Of Probability, Random Variables and Expectation

4  Tools for Describing Risk Factors and Portfolios

Calculating Risk Factor Returns
Measures of Central Tendency
Measures of Dispersion
Measures of Shape

5  The Essentials of Hypothesis Testing for Risk Managers

The Basics: Normal Distribution
Central Limit Theorem
Hypothesis Testing

6  Alternative Methods to Measure Correlation

Popular Metrics for Measuring Correlation
Hypothesis Testing and Confidence Intervals
Other Useful Types of Correlation Coefficient

7  A Primer on Maximum Likelihood Estimation

The Likelihood Equation
The Score Vector
The Information Matrix
Newton–Raphson Method
Linear Regression

8  Regression in a Nutshell

Parameter Estimation
Assessing the Simple Linear Regression Model
R-Squared and the Regression Model
Assumptions of the Linear Regression Model
Multiple Regression

9  Fitting Probability Distributions to Data

Understanding Probability Distributions
Library Of Probability Distributions

10  Practical Principal Component Analysis

Procedure for Principal Component Analysis
Numerical Estimation of Principal Components
Principal Component Analysis in Market Risk Management
Scenario Analysis

11  Three Essential Models for Volatility

Mastering Volatility
Moving Average Model
The GARCH(1,1) Model
Exponentially Weighted Moving Average

12  Random Numbers and Applied Simulation

Random Number Generation
Generating Fat-Tailed Random Variables
Historical Simulation and Monte Carlo Simulation
Monte Carlo Simulation
Case Study: The Role of Gold in Lifecycle Retirement Wealth Accumulation

13  Tail Risk Modelling

Value-at-Risk Modelling
Calculating VaR
Other Models for Calculating VaR
Extreme Value Theory

14  Conclusion