Forecasting Volatility in the Financial Markets
John L. Knight, Stephen Satchell
Butterworth-Heinemann, 2002 - Business & Economics - 407 pages
'Forecasting Volatility in the Financial Markets' assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting edge modelling and forecasting techniques. It then uses a technical survey to explain the different ways to measure risk and define the different models of volatility and return.
The editors have brought together a set of contributors that give the reader a firm grounding in relevant theory and research and an insight into the cutting edge techniques applied in this field of the financial markets.
This book is of particular relevance to anyone who wants to understand dynamic areas of the financial markets.
* Traders will profit by learning to arbitrage opportunities and modify their strategies to account for volatility.
* Investment managers will be able to enhance their asset allocation strategies with an improved understanding of likely risks and returns.
* Risk managers will understand how to improve their measurement systems and forecasts, enhancing their risk management models and controls.
* Derivative specialists will gain an in-depth understanding of volatility that they can use to improve their pricing models.
* Students and academics will find the collection of papers an invaluable overview of this field.
This book is of particular relevance to those wanting to understand the dynamic areas of volatility modeling and forecasting of the financial marketsProvides the latest research and techniques for Traders, Investment Managers, Risk Managers and Derivative Specialists wishing to manage their downside risk exposure
Current research on the key forecasting methods to use in risk management, including two new chapters
Other editions - View all
analysis ARCH models ARCH-SV model asset price asset returns assume asymmetric beta beta coefficients Black-Scholes model Bollerslev business cycle call option coefficients conditional variance conditional volatility correlation covariance derived distribution dynamic Econometrica efficient empirical Engle equation estimates excess returns exchange rate factor forecast error forecast horizon forecasting performance foreign exchange future volatility GARCH Harvey heteroscedasticity implied RND functions implied volatility index options information content interest rate Journal of Econometrics Journal of Finance kurtosis likelihood linear lognormal matrix maximum likelihood mean measure moving average NBER non-linear normal distribution observations OOOOOOOOOOOOOOO option prices out-of-sample parameters period prediction random recession regime regression regressors return volatility risk sample Satchell scaled truncated Schwert semivariance Shephard simulation specification standard deviation stationary statistics stochastic volatility stochastic volatility models stock market stock price stock returns SV models threshold trading underlying asset variable volatility forecasts zero