3

28

 

Princeton University Press (Visit publisher website)

2005

English

 

 

 

Intermediate

 

 

Quantitative analysis, probability, estimation, statistics, stochastic processes and stochastic calculus, random variables, cumulative distribution function, probability distribution function, mean, standard deviation, moments, skewness, kurtosis, Normal distribution, lognormal distribution, conditional distribution, covariance, correlation, dependence, stationarity, Gaussian process, autocorrelation, spectral density, white noise, martingale, autoregressive, moving average (ARMA) process, AR(1), MA(1), ARMA(1,1), ARMA(p,q), aggregation, ARIMA process, ARFIMA process, linear stochastic process, continuous-time stochastic process, Wiener process, geometric Brownian motion

 

From the author - Chapter 3 commences with a summary of the theoretical properties of random variables. It then continues with the definitions and properties of important probability models for time-ordered sequences of random variables, called stochastic processes. Consideration is given to a variety of stochastic processes that are used throughout the book to develop descriptions of the dynamic behavior of asset prices.

 

3.1 Introduction
3.2 Random Variables
3.3 Stationary Stochastic Processes
3.4 Uncorrelated Processes
3.5 ARMA Processes
3.6 Examples of ARMA(1, 1) Specifications
3.7 ARIMA Processes
3.8 ARFIMA Processes
3.9 Linear Stochastic Processes
3.10 Continuous-Time Stochastic Processes
3.11 Notation for Random Variables and Observations

 

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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.