10

32

 

Princeton University Press (Visit publisher website)

2005

English

 

 

 

Intermediate

 

 

Investment management, quantitative analysis, portfolio theory, probability, estimation, statistics, stochastic processes and stochastic calculus, autoregressive conditional heteroskedasticity (ARCH) models, asymmetric volatility, EGARCH, news impact curves, long memory ARCH models, FIEGARCH(1,d,1), likelihood methods, maximum likelihood estimate (MLE), BHHH algorithm, QMLE, tests for conditional normality, tests for a unit root in volatility, model building, univariate volatility specifications, multivariate volatility specifications

 

From the author - Chapter 10 describes more complicated ARCH models and the likelihood theory required to perform hypothesis tests about ARCH parameters. Guidance concerning model selection is included, based upon tests and diagnostic checks.

 

10.1 Introduction
10.2 Asymmetric Volatility: Further Specifications and Evidence
10.3 Long Memory ARCH Models
10.4 Likelihood Methods
10.5 Results from Hypothesis Tests
10.6 Model Building
10.7 Further Volatility Specifications
10.8 Concluding Remarks
10.9 Appendix: Formulae for the Score Vector

 

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This book shows how current and recent market prices convey information about the probability distributions that govern future prices. Moving beyond purely theoretical models, Stephen Taylor applies methods supported by empirical research of equity and foreign exchange markets to show how daily and more frequent asset prices, and the prices of option contracts, can be used to construct and assess predictions about future prices, their volatility, and their probability distributions.

Stephen Taylor provides a comprehensive introduction to the dynamic behavior of asset prices, relying on finance theory and statistical evidence. He uses stochastic processes to define mathematical models for price dynamics, but with less mathematics than in alternative texts. The key topics covered include random walk tests, trading rules, ARCH models, stochastic volatility models, high-frequency datasets, and the information that option prices imply about volatility and distributions.

Asset Price Dynamics, Volatility, and Prediction is ideal for students of economics, finance, and mathematics who are studying financial econometrics, and will enable researchers to identify and apply appropriate models and methods. It will likewise be a valuable resource for quantitative analysts, fund managers, risk managers, and investors who seek realistic expectations about future asset prices and the risks to which they are exposed.