Econometric Modelling of Stock Market Intraday Activity

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Springer Science & Business Media, Aug 31, 2001 - Business & Economics - 177 pages
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Over the past 25 years, applied econometrics has undergone tremen dous changes, with active developments in fields of research such as time series, labor econometrics, financial econometrics and simulation based methods. Time series analysis has been an active field of research since the seminal work by Box and Jenkins (1976), who introduced a gen eral framework in which time series can be analyzed. In the world of financial econometrics and the application of time series techniques, the ARCH model of Engle (1982) has shifted the focus from the modelling of the process in itself to the modelling of the volatility of the process. In less than 15 years, it has become one of the most successful fields of 1 applied econometric research with hundreds of published papers. As an alternative to the ARCH modelling of the volatility, Taylor (1986) intro duced the stochastic volatility model, whose features are quite similar to the ARCH specification but which involves an unobserved or latent component for the volatility. While being more difficult to estimate than usual GARCH models, stochastic volatility models have found numerous applications in the modelling of volatility and more particularly in the econometric part of option pricing formulas. Although modelling volatil ity is one of the best known examples of applied financial econometrics, other topics (factor models, present value relationships, term structure 2 models) were also successfully tackled.
 

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Contents

MARKET MICROSTRUCTURE TRADING MECHANISMS AND EXCHANGES
1
2 Price Setting in financial markets
2
23 Characteristics of trading mechanisms
6
24 Market liquidity
7
3 Exchanges
11
32 The NASDAQ
15
33 The Foreign Exchange market
17
34 The Paris Bourse
18
33 Estimation
81
34 Diagnostics
83
4 Illustration on NYSE data
91
Probability distributions
97
EMPIRICAL RESULTS AND EXTENSIONS
107
2 Market microstructure effects
108
22 Empirical application
109
3 A joint model of durations and price change indicators
111

4 Market microstructure
21
42 Empirical research
24
NYSE TAQ DATABASE AND FINANCIAL DURATIONS
35
2 The TAQ database
36
22 The quote database
37
23 Best bidask quotes
38
24 Direction of a trade
40
27 Bidask bounce
41
4 Durations
44
41 Price durations
45
42 Volume durations
47
a descriptive analysis
48
51 Trades and quotes
49
52 Intraday seasonality
50
53 Timeofday adjusted durations
52
INTRADAY DURATION MODELS
65
3 Econometric models
69
31 ACD models
70
32 Logarithmic ACD models
76
31 The model
113
32 Empirical application
116
33 Forecasting and trading rules
118
APPENDIX 4A
122
INTRADAY VOLATILITY AND VALUEATRISK
125
2 A review of arch models
126
22 The ARCH model
128
23 extensions
130
3 ARCH models for intraday data
132
31 Time transformations and intraday seasonality
133
32 GARCH and EGARCH models
141
33 Volume and number of trades
144
4 Intraday Valueatrisk
147
42 VaR models for intraday data
149
43 Empirical application
152
About the Authors
173
Index
175
Copyright

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Page 161 - Morana. C. (1999). Computing value at risk with high frequency data.

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About the author (2001)

Luc Bauwens is Professor of Economics at the Université catholique de Louvain, Belgium where he chairs the Department of Economics, and has been co-director of the Center for Operations Research and Econometrics (CORE) from 1992 to 1998. He has published several books and papers in the fields of Bayesian inference, time series methods, simulation and numerical methods in econometrics, as well as empirical finance and international trade. Pierre Giot is Professor of Econometrics and Quantitative Finance at Maastricht University in The Netherlands, and he is a member of CORE in Belgium. After graduating as a Civil Engineer (Polytechnique) in Electronics, he got his Ph.D. in Economics at the Université catholique de Louvain in 1999. His current research interests focus on quantitative finance, models for intraday data and empirical market microstructure.