Bhar / Hamori Empirical Techniques in Finance

1. Auflage 2005
ISBN: 978-3-540-27642-5
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 243 Seiten

Reihe: Springer Finance

This book offers the opportunity to study and experience advanced empi- cal techniques in finance and in general financial economics. It is not only suitable for students with an interest in the field, it is also highly rec- mended for academic researchers as well as the researchers in the industry. The book focuses on the contemporary empirical techniques used in the analysis of financial markets and how these are implemented using actual market data. With an emphasis on Implementation, this book helps foc- ing on strategies for rigorously combing finance theory and modeling technology to extend extant considerations in the literature. The main aim of this book is to equip the readers with an array of tools and techniques that will allow them to explore financial market problems with a fresh perspective. In this sense it is not another volume in eco- metrics. Of course, the traditional econometric methods are still valid and important; the contents of this book will bring in other related modeling topics that help more in-depth exploration of finance theory and putting it into practice. As seen in the derivatives analysis, modern finance theory requires a sophisticated understanding of stochastic processes. The actual data analyses also require new Statistical tools that can address the unique aspects of financial data. To meet these new demands, this book explains diverse modeling approaches with an emphasis on the application in the field of finance.

Autoren/Hrsg.

Bhar, Ramaprasad

Hamori, Shigeyuki

Inhaltsverzeichnis

1;Acknowledgements;7
2;Table of Contents;9
3;1 Introduction;13
4;2 Basic Probability Theory and Markov Chains;17
4.1;2.1 Random Variables;17
4.2;2.2 Function of Random Variable;19
4.3;2.3 Normal Random Variable;20
4.4;2.4 Lognormal Random Variable;21
4.5;2.5 Markov Chains;22
4.6;2.6 Passage Time;26
4.7;2.7 Examples and Exercises;28
4.8;References;29
5;3 Estimation Techniques;31
5.1;3.1 Models, Parameters and Likelihood - An Overview;31
5.2;3.2 Maximum Likelihood Estimation and Covariance Matrix of Parameters;32
5.3;3.3 MLE Example - Classical Linear Regression;34
5.4;3.4 Dependent Observations;35
5.5;3.5 Prediction Error Decomposition;36
5.6;3.6 Serially Correlated Errors - Overview;37
5.7;3.7 Constrained Optimization and the Covariance Matrix;39
5.8;3.8 Examples and Exercises;40
5.9;References;41
6;4 Non-Parametric Method of Estimation;43
6.1;4.1 Background;43
6.2;4.2 Non-Parametric Approach;44
6.3;4.3 Kernel Regression;45
6.4;4.4 Illustration 1 (EViews);47
6.5;4.5 Optimal Bandwidth Seiection;48
6.6;4.6 Illustration 2 (EViews);48
6.7;4.7 Examples and Exercises;50
6.8;References;51
7;5 Unit Root, Cointegration and Related Issues;53
7.1;5.1 Stationary Process;53
7.2;5.2 Unit Root;56
7.3;5.3 Dickey-Fuller Test;58
7.4;5.4 Cointegration;61
7.5;5.5 Residual-based Cointegration Test;62
7.6;5.6 Unit Root in a Regression Model;63
7.7;5.7 Application to Stocic Markets;64
7.8;References;66
8;6 VAR Modeling;67
8.1;6.1 Stationary Process;67
8.2;6.2 Granger Causality;69
8.3;6.3 Cointegration and Error Correction;71
8.4;6.4 Johansen Test;73
8.5;6.5 LA-VAR;74
8.6;6.6 Application to Stocic Prices;76
8.7;References;77
9;7 Time Varying Volatility Models;79
9.1;7.1 Background;79
9.2;7.2 ARCH and GARCH Models;80
9.3;7.3 TGARCH and EGARCH Models;83
9.4;7.4 Causality-in-Variance Approach;86
9.5;7.5 Information Flow between Price Change and Trading Volume;89
9.6;References;93
10;8 State-Space Models (I);95
10.1;8.1 Background;95
10.2;8.2 Classical Regression;95
10.3;8.3 Important Time Series Processes;98
10.4;8.4 Recursive Least Squares;101
10.5;8.5 State-Space Representation;103
10.6;8.6 Examples and Exercises;106
10.7;References;115
11;9 State-Space Models (II);117
11.1;9.1 Likelihood Function Maximization;117
11.2;9.2 EM Algorithm;120
11.3;9.3 Time Varying Parameters and Changing Conditional Variance (EViews);123
11.4;9.4 GARCH and Stochastic Variance Model for Exchange Rate (EViews);125
11.5;9.5 Examples and Exercises;128
11.6;References;138
12;10 Discrete Time Real Asset Valuation Model;139
12.1;10.1 Asset Price Basics;139
12.2;10.2 Mining Project Background;141
12.3;10.3 Example 1;142
12.4;10.4Example2;143
12.5;10.5 Example 3;145
12.6;10.6 Example4;147
12.7;Appendix;150
12.8;References;152
13;11 Discrete Time Model of Interest Rate;153
13.1;11.1 Preliminaries of Short Rate Lattice;153
13.2;11.2 Forward Recursion for Lattice and Elementary Price;157
13.3;11.3 Matching the Current Term Structure;160
13.4;11.4 Immunization: Application of Short Rate Lattice;161
13.5;11.5 Valuing Callable Bond;164
13.6;11.6 Exercises;165
13.7;References;166
14;12 Global Bubbles in Stock Markets and Linkages;167
14.1;12.1 Introduction;167
14.2;12.2 Speculative Bubbles;168
14.3;12.3 Review of Key Empirical Papers;170
14.3.1;12.3.1 Flood and Garber (1980);170
14.3.2;12,3.2 West (1987);172
14.3.3;12.3.3 Ikeda and Shibata (1992);173
14.3.4;12.3.4 Wu (1997);175
14.3.5;12.3.5 Wu (1995);176
14.4;12.4 New Contribution;176
14.5;12.5 Global Stock Market Integration;177
14.6;12.6 Dynamic Linear Models for Bubble Solutions;179
14.7;12.7 Dynamic Linear Models for No-Bubble Solutions;184
14.8;12.8 Subset VAR for Linkages between Markets;186
14.9;12.9 Results and Discussions;187
14.10;12.10 Summary;198
14.11;References;199
15;13 Forward FX Market and the Risk Premium;205
15.1;13.1 Introduction;205
15.2;13.2 Alternative Approach to Model Risk Premia;207
15.3;13.3 The Proposed Model;208
15.4;13.4 State-Space Framework;213
15.5;13.5 Brief Description of Wolff/Cheung Model;216
15.6;13.6 Application of the Model and Data Description;217
15.7;13.7 Summary and Conclusions;221
15.8;Appendix;222
15.9;References;223
16;14 Equity Risk Premia from Derivative Prices;227
16.1;14.1 Introduction;227
16.2;14.2 The Theory behind the Modeling Framework;229
16.3;14.3 The Continuous Time State-Space Framework;232
16.4;14.4 Setting Up The Filtering Framework;235
16.5;14.5 The Data Set;240
16.6;14.6 Estimation Results;240
16.7;14.7 Summary and Conclusions;247
16.8;References;248
17;Index;251
18;About the Authors;254

Leseproben

(p.31)

4.1 Background

In some financial applications we may face a functional relationship between two variables Y and X without the benefit of a structural model to restrict the parametric form of the relation. In these situations, we can apply nonparametric estimation techniques to capture a wide variety of non- Hnearities without recourse to any one particular specification of the nonUnear relation. In contrast to a highly structured or parametric approach to estimating non-linearities, nonparametric estimation requires few assumptions about the nature of the non-linearities.

This is not to say that the approach is free of drawbacks. To begin with, the highly data-intensive nature of the process can make it somewhat costly. Further, nonparametric estimation is poorly suited to small samples and has been found to over fit the data. A regression curve describes the general relationship between an explanatory variable X and a response variable Y. Having observed X, the average value of Y is given by the regression function. The form of the regression function may teil us where higher Y-values are to be expected for certain values of X or where a special sort of dependence is indicated. A pre-selected parametric model might be too restricted to fit unexpected features of the data. The term "non-parametric" refers to the flexible functional form of the regression curve.

The non-parametric approach to a regression curve serves four main functions. First, it provides a versatile method for exploring a general relationship between two variables. Second, it gives predictions of observations yet to be made without reference to a fixed parametric model. Third, it provides a tool for finding spurious observations by studying the influence of isolated points. Fourth, it constitutes a flexible method for substituting missing values or interpolating between adjacent X values. The flexibility of the method is extremely helpful in a preliminary and exploratory Statistical analysis of a data set. When no a priori model Information about the regression curve is available, non-parametric analysis can help in providing simple parametric formulations of the regression relationship.