Empirical Techniques in Finance

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Springer Science & Business Media, May 9, 2005 - Business & Economics - 241 pages
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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.
 

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Contents

Basic Probability Theory and Markov Chains
5
22 Function of Random Variable
7
23 Normal Random Variable
8
24 Lognormal Random Variable
9
25 Markov Chains
10
26 Passage Time
14
27 Examples and Exercises
16
References
17
StateSpace Models II
105
92 EM Algorithm
108
93 Time Varying Parameters and Changing Conditional Variance EViews
111
94 GARCH and Stochastic Variance Model for Exchange Rate EViews
113
95 Examples and Exercises
116
References
126
Discrete Time Real Asset Valuation Model
127
102 Mining Project Background
129

Estimation Techniques
19
32 Maximum Likelihood Estimation and Covariance Matrix of Parameters
20
33 MLE Example Classical Linear Regression
22
34 Dependent Observations
23
35 Prediction Error Decomposition
24
36 Serially Correlated Errors Overview
25
37 Constrained Optimization and the Covariance Matrix
27
38 Examples and Exercises
28
References
29
NonParametric Method of Estimation
31
42 NonParametric Approach
32
43 Kernel Regression
33
44 Illustration 1 EViews
35
45 Optimal Bandwidth Selection
36
47 Examples and Exercises
38
References
39
Unit Root Cointegration and Related Issues
41
52 Unit Root
44
53 DickeyFuller Test
46
54 Cointegration
49
55 Residualbased Cointegration Test
50
56 Unit Root in a Regression Model
51
57 Application to Stock Markets
52
References
54
VAR Modeling
55
62 Granger Causality
57
63 Cointegration and Error Correction
59
64 Johansen Test
61
65 LAVAR
62
66 Application to Stock Prices
64
References
65
Time Varying Volatility Models
67
72 ARCH and GARCH Models
68
73 TGARCH and EGARCH Models
71
74 CausalityinVariance Approach
74
75 Information Flow between Price Change and Trading Volume
77
References
81
StateSpace Models I
83
83 Important Time Series Processes
86
84 Recursive Least Squares
89
85 StateSpace Representation
91
86 Examples and Exercises
94
References
103
103 Example 1
130
104 Example 2
131
105 Example 3
133
106 Example 4
135
Appendix
138
References
140
Discrete Time Model of Interest Rate
141
112 Forward Recursion for Lattice and Elementary Price
145
113 Matching the Current Term Structure
148
Application of Short Rate Lattice
149
115 Valuing Callable Bond
152
116 Exercises
153
References
154
Global Bubbles in Stock Markets and Linkages
155
122 Speculative Bubbles
156
123 Review of Key Empirical Papers
158
124 New Contribution
164
125 Global Stock Market Integration
165
126 Dynamic Linear Models for Bubble Solutions
167
127 Dynamic Linear Models for NoBubble Solutions
172
128 Subset VAR for Linkages between Markets
174
129 Results and Discussions
175
1210 Summary
186
References
187
Forward FX Market and the Risk Premium
193
132 Alternative Approach to Model Risk Premia
195
133 The Proposed Model
196
134 StateSpace Framework
201
135 Brief Description of WolffCheung Model
204
136 Application of the Model and Data Description
205
137 Summary and Conclusions
209
Calculation of
210
References
211
Equity Risk Premia from Derivative Prices
215
142 The Theory behind the Modeling Framework
217
143 The Continuous Time StateSpace Framework
220
144 Setting Up The Filtering Framework
223
145 The Data Set
228
147 Summary and Conclusions
235
References
236
Index
239
About the Authors
243
Copyright

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About the author (2005)

Bhar is an Associate Professor in the School of Banking and Finance at The University of New South Wales in Australia.

Hamori is a Professor in the Graduate School of Economics at Kobe University in Japan.