Overview

Given the magnitude of currency speculation and sports gambling, it is surprising that much literature on the subject contains mostly negative forecasting results. Majority opinion still holds that short term fluctuations in financial markets follow random walk. In this non-random walk through financial and sports gambling markets, parallels are drawn between modelling short term currency movements and modelling outcomes of athletic encounters. The forecasting concepts and methodologies are identical; only the variables change names. If, in fact, these markets are driven by mechanisms of non-random walk, there must be some explanation for the negative forecasting results. This text examines this issue.

Full Product Details

Author:   William S. Mallios
Publisher:   Springer
Imprint:   Springer
Dimensions:   Width: 15.50cm , Height: 1.90cm , Length: 23.50cm
Weight:   0.641kg
ISBN:  

9780792377139

ISBN 10:   0792377133
Pages:   294
Publication Date:   31 December 1999
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

Introduction: A Variety of Betting Lines.- I Models, Moralities, and Misconceptions.- II Modeling Concepts.- III Football.- IV Basketball.- V Baseball.- VI Selection of Athletes.- VII Financial Markets.- A.1 Time Series Analysis: Overview of Arma, Bilinear, and Higher Order Models.- A.1.1 Preliminary Comments.- A.1.2 Overview of Autoregressive Moving Average (ARMA) Models.- A.1.3 Overview of Bilinear Models.- A.1.4 Approaches to Modeling Heteroskedasticity Through Time Varying Coefficients.- A.1.5 Autoregressive Conditional Heteroskedasticity.- A.1.6 Generalized Autoregressive Conditional Heteroskedasticity.- A.1.7 ARMA Models with GARCH Errors.- A.1.8 Model Misspecification.- A.1.9 Least Squares Estimation for Non-Varying Coefficients.- A.1.10 Empirical Bayes Estimation for Time Varying Coefficients.- A.2 Multiple Time Series Equations.- A.2.1 Models Based on Wold’s Decomposition Theorem.- A.2.2 Multiple, Higher-Order Systems of Time Series Equations.- A.2.3 Extensions to Rational Expectations.- A.2.4 Classification of Events According to Observed Outcomes and States of Nature in Currency Markets.- A.3 Quantification of Structural Effects in Regression Systems.- A.3.1 Preliminary Comments.- A.3.2 Structural and Reduced Systems: Exploratory Models and Assumptions.- A.3.3 Increasing Efficiency Through Restricted Systems: Adjustments for Intra Sample Biases.- A.3.4 Estimation in Structural Systems.- A.3.5 Examples of Model Ambiguity in Structural Systems.- A.3.6 Structural Experimental Design Reconsidered.

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