A practical guide for forecasting financial market volatility

Title:

Personal Author:

Poon, Ser-Huang

Series:

Wiley finance series

Publication Information:

Chichester, West Sussex : Wiley, 2005

ISBN:

9780470856130

Subject Term:

Options (Finance) -- Mathematical models

Securities -- Prices -- Mathematical models

Stock price forecasting -- Mathematical models

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Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000004985150	HG6024.A3 P66 2005	Open Access Book	Book	Searching... Unknown

Financial market volatility forecasting is one of today's most important areas of expertise for professionals and academics in investment, option pricing, and financial market regulation. While many books address financial market modelling, no single book is devoted primarily to the exploration of volatility forecasting and the practical use of forecasting models. A Practical Guide to Forecasting Financial Market Volatility provides practical guidance on this vital topic through an in-depth examination of a range of popular forecasting models. Details are provided on proven techniques for building volatility models, with guide-lines for actually using them in forecasting applications.

Author Notes

About the author

Dr SER-HUANG POON was promoted to Professor of Finance at Manchester University in 2003. Prior to that, she was a senior lecturer at Strathclyde University.

Ser-Huang graduated from the National University of Singapore and obtained her masters and PhD from Lancaster University, UK. She has researched financial market volatility for many years and has published in many top ranking peer reviewed finance and financial econometric journals with many co-authors from around the world. Her financial market volatility work was cited as a reference reading on the Nobel web site in 2003.

Clive Granger

Foreword	p. xiii
Preface	p. xv
1 Volatility Definition and Estimation	p. 1
1.1 What is volatility?	p. 1
1.2 Financial market stylized facts	p. 3
1.3 Volatility estimation	p. 10
1.3.1 Using squared return as a proxy for daily volatility	p. 11
1.3.2 Using the high-low measure to proxy volatility	p. 12
1.3.3 Realized volatility, quadratic variation and jumps	p. 14
1.3.4 Scaling and actual volatility	p. 16
1.4 The treatment of large numbers	p. 17
2 Volatility Forecast Evaluation	p. 21
2.1 The form of X[subscript t]	p. 21
2.2 Error statistics and the form of [epsilon subscript t]	p. 23
2.3 Comparing forecast errors of different models	p. 24
2.3.1 Diebold and Mariano's asymptotic test	p. 26
2.3.2 Diebold and Mariano's sign test	p. 27
2.3.3 Diebold and Mariano's Wilcoxon sign-rank test	p. 27
2.3.4 Serially correlated loss differentials	p. 28
2.4 Regression-based forecast efficiency and orthogonality test	p. 28
2.5 Other issues in forecast evaluation	p. 30
3 Historical Volatility Models	p. 31
3.1 Modelling issues	p. 31
3.2 Types of historical volatility models	p. 32
3.2.1 Single-state historical volatility models	p. 32
3.2.2 Regime switching and transition exponential smoothing	p. 34
3.3 Forecasting performance	p. 35
4 Arch	p. 37
4.1 Engle (1982)	p. 37
4.2 Generalized ARCH	p. 38
4.3 Integrated GARCH	p. 39
4.4 Exponential GARCH	p. 41
4.5 Other forms of nonlinearity	p. 41
4.6 Forecasting performance	p. 43
5 Linear and Nonlinear Long Memory Models	p. 45
5.1 What is long memory in volatility?	p. 45
5.2 Evidence and impact of volatility long memory	p. 46
5.3 Fractionally integrated model	p. 50
5.3.1 FIGARCH	p. 51
5.3.2 FIEGARCH	p. 52
5.3.3 The positive drift in fractional integrated series	p. 52
5.3.4 Forecasting performance	p. 53
5.4 Competing models for volatility long memory	p. 54
5.4.1 Breaks	p. 54
5.4.2 Components model	p. 55
5.4.3 Regime-switching model	p. 57
5.4.4 Forecasting performance	p. 58
6 Stochastic Volatility	p. 59
6.1 The volatility innovation	p. 59
6.2 The MCMC approach	p. 60
6.2.1 The volatility vector H	p. 61
6.2.2 The parameter w	p. 62
6.3 Forecasting performance	p. 63
7 Multivariate Volatility Models	p. 65
7.1 Asymmetric dynamic covariance model	p. 65
7.2 A bivariate example	p. 67
7.3 Applications	p. 68
8 Black-Scholes	p. 71
8.1 The Black-Scholes formula	p. 71
8.1.1 The Black-Scholes assumptions	p. 72
8.1.2 Black-Scholes implied volatility	p. 73
8.1.3 Black-Scholes implied volatility smile	p. 74
8.1.4 Explanations for the 'smile'	p. 75
8.2 Black-Scholes and no-arbitrage pricing	p. 77
8.2.1 The stock price dynamics	p. 77
8.2.2 The Black-Scholes partial differential equation	p. 77
8.2.3 Solving the partial differential equation	p. 79
8.3 Binomial method	p. 80
8.3.1 Matching volatility with u and d	p. 83
8.3.2 A two-step binomial tree and American-style options	p. 85
8.4 Testing option pricing model in practice	p. 86
8.5 Dividend and early exercise premium	p. 88
8.5.1 Known and finite dividends	p. 88
8.5.2 Dividend yield method	p. 88
8.5.3 Barone-Adesi and Whaley quadratic approximation	p. 89
8.6 Measurement errors and bias	p. 90
8.6.1 Investor risk preference	p. 91
8.7 Appendix: Implementing Barone-Adesi and Whaley's efficient algorithm	p. 92
9 Option Pricing with Stochastic Volatility	p. 97
9.1 The Heston stochastic volatility option pricing model	p. 98
9.2 Heston price and Black-Scholes implied	p. 99
9.3 Model assessment	p. 102
9.3.1 Zero correlation	p. 103
9.3.2 Nonzero correlation	p. 103
9.4 Volatility forecast using the Heston model	p. 105
9.5 Appendix: The market price of volatility risk	p. 107
9.5.1 Ito's lemma for two stochastic variables	p. 107
9.5.2 The case of stochastic volatility	p. 107
9.5.3 Constructing the risk-free strategy	p. 108
9.5.4 Correlated processes	p. 110
9.5.5 The market price of risk	p. 111
10 Option Forecasting Power	p. 115
10.1 Using option implied standard deviation to forecast volatility	p. 115
10.2 At-the-money or weighted implied?	p. 116
10.3 Implied biasedness	p. 117
10.4 Volatility risk premium	p. 119
11 Volatility Forecasting Records	p. 121
11.1 Which volatility forecasting model?	p. 121
11.2 Getting the right conditional variance and forecast with the 'wrong' models	p. 123
11.3 Predictability across different assets	p. 124
11.3.1 Individual stocks	p. 124
11.3.2 Stock market index	p. 125
11.3.3 Exchange rate	p. 126
11.3.4 Other assets	p. 127
12 Volatility Models in Risk Management	p. 129
12.1 Basel Committee and Basel Accords I & II	p. 129
12.2 VaR and backtest	p. 131
12.2.1 VaR	p. 131
12.2.2 Backtest	p. 132
12.2.3 The three-zone approach to backtest evaluation	p. 133
12.3 Extreme value theory and VaR estimation	p. 135
12.3.1 The model	p. 136
12.3.2 10-day VaR	p. 137
12.3.3 Multivariate analysis	p. 138
12.4 Evaluation of VaR models	p. 139
13 VIX and Recent Changes in VIX	p. 143
13.1 New definition for VIX	p. 143
13.2 What is the VXO?	p. 144
13.3 Reason for the change	p. 146
14 Where Next?	p. 147
Appendix	p. 149
References	p. 201
Index	p. 215

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Table of Contents