2

14

 

Princeton University Press (Visit publisher website)

2005

English

 

 

 

Intermediate

 

 

Investment management, quantitative analysis, portfolio theory, estimation, statistics, stochastic processes and stochastic calculus, asset price time series, data collection issues, sources of price time series, frequency, price definitions, bid/ask, futures prices, error checks, volatility clustering, definitions of returns, stock returns, stock indices, spot currency returns, futures returns

 

From the author - This first part of the book provides a foundation for the empirical modeling of time series of returns from financial assets. Chapter 2 explains how returns from investments are calculated from prices. A set of regularly observed prices can be used to define a time series of returns. Several examples are presented and advice is given about data-collection issues.

 

2.1 Introduction
2.2 Two Examples of Price Series
2.3 Data-Collection Issues
2.4 Two Returns Series
2.5 Definitions of Returns
2.6 Further Examples of Time Series of Returns

 

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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.