Forecasting Volatility in the Financial Markets

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 provides a survey of ways to measure risk and define the different models of volatility and return. Editors John Knight and Stephen Satchell have brought together an impressive array of contributors who present research from their area of specialization related to volatility forecasting. Readers with an understanding of volatility measures and risk management strategies will benefit from this collection of up-to-date chapters on the latest techniques in forecasting volatility.

Author’s Note:

This book presents recent research on volatility in financial markets with a special emphasis on forecasting. This literature has grown at a frightening rate in recent years, and would-be readers may feel daunted by the prospect of learning hundreds of new acronyms prior to grappling with the ideas they represent. To reduce the entry costs of our readers, we present two summary chapters; a chapter on volatility in finance by Linlan Xiao and A.B. Aydemir, and a survey of applications of stochastic volatility models to option pricing problems by G.J. Jiang. This is an area of some importance, as one of the sources of data in the study of volatility is the implied volatility series derived from option prices.

As mentioned previously, we are delighted to reproduce a paper by Professor Engle written jointly with A. Patton. We include a number of practitioner chapters, namely one by D. diBartolomeo, one by R. Cornish, one by E. Acar and E. Petitdidier, and one by P. Lequeux. We have a chapter by a monetary economist, B. Bahra. All these chapters focus on practical issues concerning the use of volatilities; some examine high-frequency data, others consider how risk-neutral probability measurement can be put into a forecasting framework.

We have a number of chapters concentrating on direct forecasting using GARCH, forecasting implied volatility and looking at tick-by-tick data. These chapters concentrate much more on theoretical issues in volatility and risk modelling. S. Bond considers dynamic models of semi-variance, a measure of downside risk. G. Perez-Quiros and A. Timmermann examine connections between volatility of stock markets and business cycle turning points. A. Harvey examines long memory stochastic volatility, while J. Knight and S. Satchell consider some exact properties of conditional heteroscedasticity models. T. Silvey answers a question, very vexing to theorists, as to why simple moving average rules for forecasting volatility can outperform sophisticated models.

Taken together, these chapters reflect the extraordinary diversity of procedures now available for forecasting volatility. It seems likely that many of these can be incorporated into trading strategies or built into investment technology products. The editors have put the book together with the twin goals of encouraging both researchers and practitioners, and we hope that this book is useful to both audiences.

Contents:

• Volatility modelling and forecasting in finance
• What good is a volatility model?
• Applications of portfolio variety
• A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices
• An investigation of the relative performance of GARCH models versus simple rules in forecasting volatility
• Stochastic volatility and option pricing
• Modelling slippage: an application to the bund futures contract
• Real trading volume and price action in the foreign exchange markets
• Implied risk-neutral probability density functions from option prices: a central bank perspective
• Hashing GARCH: a reassessment of volatility forecasting performance
• Implied volatility forecasting: a comparison of different procedures including fractionally integrated models with applications to UK equity options
• GARCH predictions and the predictions of option prices
• Volatility forecasting in a tick data model
• An econometric model of downside risk
• Variations in the mean and volatility of stock returns around turning points of the business cycle
• Long memory in stochastic volatility
• GARCH processes – some exact results, some difficulties and a suggested remedy
• Generating composite volatility forecasts with random factor betas

Forecasting Volatility in the Financial Markets By Stephen Satchell, John Knight pdf