Hidden Markov Models
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RRamaprasad Bhar, Shigeyuki Hamori
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Hidden Markov Models
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Kluwer Academic Publishers
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9781402079405
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1
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CHF 58.60
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Naturwissenschaft
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English
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160
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DRM
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PC/MAC/eReader/Tablet
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PDF
Markov chains have increasingly become useful way of capturing stochastic nature of many economic and financial variables. Although the hidden Markov processes have been widely employed for some time in many engineering applications e.g. speech recognition, its effectiveness has now been recognized in areas of social science research as well. The main aim of Hidden Markov Models: Applications to Financial Economics is to make such techniques available to more researchers in financial economics. As such we only cover the necessary theoretical aspects in each chapter while focusing on real life applications using contemporary data mainly from OECD group of countries.
The underlying assumption here is that the researchers in financial economics would be familiar with such application although empirical techniques would be more traditional econometrics. Keeping the application level in a more familiar level, we focus on the methodology based on hidden Markov processes. This will, we believe, help the reader to develop more in-depth understanding of the modeling issues thereby benefiting their future research.
Chapter 4 INTERPLAY BETWEEN INDUSTRIAL PRODUCTION AND STOCK MARKET
(p.55)
1. Introduction
Extensive studies have focused on the empirical regularity in the degree of correlations and related issues between di.erent equity markets. These studies have provided insight into the nature of comovements across markets and what might be driving this phenomenon. Baca et al. (2000) ascribe the phenomenon to the global industry factor. Brooks and Del Negro (2002) .nd that there may be only one industry factor (high technology) that might explain this notion. They also demonstrate that this might be a temporary phenomenon, as country-based diversi- .cation appears to have remained e.ective for portfolio risk since the collapse of the technology bubble.
In studies adopting a slightly di.erent focus, researchers such as Kasa (1992) and Engsted and Lund (1997) rely on the results from cointegration tests in international equity prices. They also examine whether the underlying dividend process can explain this cointegrating behavior. Although the return patterns from these markets can di.er in the short term, cointegrating behavior would suggest that they are closely linked over the long term. In a paper stressing the unlikelihood of cointegrating behavior from the perspective of economic theory, Richard (1995) proves that Kasa’s (1992) .nding may result from the inappropriate use of critical values in their statistical tests.
In this chapter we attempt to explore the phenomenon of comovement among the G7 equity markets from a di.erent perspective. We make use of a concordance measure to document whether any of these markets tend to be in phase with the equivalent volatility state of the economy. Speci.cally, our analysis focuses on the G7 markets over a 30-year period at a monthly frequency. The .rst step in our approach is to model the interaction between the state of the economy and the stock market. This stems from the work of Hamilton and Lin (1996) and Chauvet (1998/1999), who demonstrate important relationships between stock markets and business cycle variables such as industrial production. Once equipped with our model, we can develop a probabilistic picture of whether a particular month is in the state of expansion or contraction. We then utilize this information and apply the concordance measure (Harding and Pagan, 1999) to capture the likelihood that the stock markets are in the same phase. Our results show that not all the G7 economies are commoving in this respect.
In modeling stock return and return volatility, the return volatility can be forecasted though the return itself cannot. Another important point to recognize is the dynamic relationship between the stock market and business cycle observed by Chauvet (1998/1999) in his attempts to anticipate turning points in the business cycle using stock market factors in a dynamic factor framework at a monthly frequency.
As large changes in volatility may alarm investors and adversely in- .uence their investment behavior, many investigators have sought to capture the patterns of stock return volatility. Several studies have applied time-varying conditional moments to capture such stylized facts. ARCH and GARCH models have been popular for this purpose, and Nelson (1991) and Glosten, Jagannathan and Runkle (1993) have proposed a variation of the GARCH model to capture asymmetries in stock return and stock index return.
Contents
7
Acknowledgments
11
List of Figures
13
List of Tables
17
1 INTRODUCTION
19
1. Introduction
19
2. Markov Chains
19
3. Passage Time
23
4. Markov Chains and the Term Structure of Interest Rates
24
5. State Space Methods and Kalman Filter
29
6. Hidden Markov Models and Hidden Markov Experts
31
7. HMM Estimation Algorithm
34
8. HMM Parameter Estimation
36
9. HMM Most Probable State Sequence: Viterbi Algorithm
40
10. HMM Illustrative Examples
42
2 VOLATILITY IN GROWTH RATE OF REAL GDP
47
1. Introduction
47
2. Models
49
2.1 GARCH Model
49
2.2 Markov Switching Variance Model
50
3. Data
51
4. Empirical Results
51
5. Conclusion
56
3 LINKAGES AMONG G7 STOCK MARKETS
59
1. Introduction
59
2. Empirical Technique
62
2.1 Markov Switching Stock Return Model
62
2.2 Concordance Measure
63
3. Data
64
4. Empirical Results
64
5. Conclusion
69
4 INTERPLAY BETWEEN INDUSTRIAL PRODUCTION AND STOCK MARKET
73
1. Introduction
73
2. Markov Switching Heteroscedasticity Model of Output and Equity
76
3. Data
80
4. Empirical Results
81
5. Conclusion
94
5 LINKING INFLATION AND INFLATION UNCERTAINTY
99
1. Introduction
99
1.1 Inflation and Inflation Uncertainty
99
1.2 Inflation Uncertainty and Markov Switching Model
101
2. Empirical Technique
103
2.1 Markov Switching Heteroscedasticity Model of the Inflation Rate
103
2.2 Non-Nested Model Selection using Vuong Statistic
104
3. Data
105
4. Empirical Results
109
5. Conclusion
125
6 EXPLORING PERMANENT AND TRANSITORY COMPONENTS OF STOCK RETURN
135
1. Introduction
135
2. Markov Switching Heteroscedasticity Model of Stock Return
137
3. Data
138
4. Empirical Results
139
5. Conclusion
143
7 EXPLORING THE RELATIONSHIP BETWEEN COINCIDENT FINANCIAL MARKET INDICATORS
145
1. Introduction
145
2. Markov Switching Coincidence Index Model
147
3. Data
149
4. Empirical Results
149
5. Conclusion
157
References
163
Index
171
