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題名：臺灣景氣循環與股票市場波動性之探討:馬可夫轉換模型之應用
作者：陳仕偉 引用關係
作者(外文)：Shyh-Wei Chen
校院名稱：國立政治大學
系所名稱：經濟學系
指導教授：林金龍
學位類別：博士
出版日期：2000
主題關鍵詞：馬可夫轉換模型；景氣循環；領先指標；同時指標；股票市場波動性；卡爾漫過率；狀態空間模型；Markov-Switching Model；Business Cycle；Leading Index；Coincident Index；Stock Market Volatility；Kalman Filter；State-Space Model
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本論文包括三篇文章, 分別針對台灣景氣循環轉折點的認定
與經濟成長的預測進行研究, 以及台股指數波動性之探討。
第一篇文章應用變動移轉機率馬可夫轉換模型,
以分析同時指標及領先指標是否有助於
台灣經濟景氣循環轉折點之認定及經長之預測。
變動移轉機率模型較固定移轉機率模型更具有彈性,
可以處理景氣轉折前後移轉機率的變動。
實證結果發現同時指標與領先指標有助於景氣循環轉折點之預測,
而且同時指標有助於經濟成長的預測而領先指標則無此效果。
第二篇文章應用多變量動態馬可夫轉換單因子模型,
對台灣重要總體經濟變數如國內生產毛額、 消費、 投資以及出口進行估計,
探討台灣經濟景氣循環轉折點之認定及預測。
多變量動態馬可夫轉換單因子模型除了能夠刻劃景氣循環具有非對稱的特質外,
也同時掌握景氣循環的另一重要特性: 「重要總體經濟變數如消費、
投資偏離時間趨勢的波動與國內生產毛額波動共同變動的現象」,
這是單變量馬可夫轉換模型所無法做到的。
實證結果發現多變量動態馬可夫轉換單因子模型
的確較單變量馬可夫轉換模型更有助於台灣景氣循環轉折點之認定與預測,
尤其是在 1990 年代之後。
第三篇文章則針對台灣股票市場波動性行為進行研究,
利用日資料的台股指數報酬率資料進行實證分析我們發現：
具有馬可夫轉換特性的自我迴歸變異數模型比傳統的一般自我迴歸變異模型
更能掌握台股指數報酬率波動性行為, 而且前者在預測上的表現也比後者為佳。
台股指數報酬率也具有槓桿效果, 也就是股價下跌對未來股票波動所產生的影響比
股價上升對未來股票波動所產生的影響要來得大。除此之外,
我們也找出台灣股票市場可能處於高波動期的期間,
並提供造成台灣股票市場可能處於高波動期的原因。
This dissertation investigates the business cycles and stock market volatility in Taiwan.
The nonlinear and asymmetric behavior, i.e., expansion, peak, contraction and trough,
during different phases of the business cycle have long been recognized;
see Burns and Mitchell (1946).
The traditional linear models cannot adequately explain these nonlinear
and asymmetric phenomena because they assume that the
growth rate of GDP is linear and stationary, and we need to turn to nonlinear models.
As advocated by Hamilton (1989),
the Markov-switching model maintains the assumption
that time series data may display frequent changes
and accounts for such changes through
switches in states, where the data-generating process and average
duration of each state are allowed to differ.
More importantly, the statistical features and identification of the
states are not imposed exogenously on the data,
but are rather determined endogenously by the
estimation procedure.
In Taiwan, the leading and coincident indexes
have been regularly compiled
and published by the Council for Economic Planning and
Development (CEPD).
These are two particularly important statistical indexes
because the Taiwan government actually uses them to monitor the economy
and changes her economic policy accordingly.
An important question then arises: how useful are these two
statistics in dating business cycles and forecasting
the future growth rate of GDP?
It would be unwise to devise a discretionary policy rule based
on statistics that have no predictive power.
The primary purpose of the first
chapter of my dissertation is to investigate this alluded question.
I employ Hamilton''s (1989) original Markov-switching model
and the time-varying Markov-switching model developed by Filardo (1994),
respectively,
to investigate the business cycle and evaluate the usefulness
of the coincident and leading indexes in dating the business cycle
and in predicting future GDP in Taiwan.
By allowing the transition probability to depend upon economic predictors,
the time-varying Markov-switching model
provides a convenient framework for investigating the usefulness
of these two indicators in dating business cycles and forecasting
future GDP. More specifically, we can examine
the impacts of these two indicators on
filtered and smoothed probability estimates and then
compare the model-defined chronologies with the CEPD-defined chronologies.
The empirical results suggest that
these two indexes help date the business cycle in Taiwan and
improve precision in predicting turning points.
As for forecasting future GDP,
the coincident index is useful whereas the leading index is not.
From this empirical study we have learned
that the traditional univariate Markov-switching model,
with or without time-varying transition probabilities,
fails to identify Taiwan''s turning points for the post-1990s.
The failure of these models can be explained
by the fact that the annual growth rate
of GDP from 1962 to 1999 has changing trend.
The average economic growth rate over this period is 8.20 percent,
and there are 38 quarters where the economic growth rate is over 11 percent.
However, it is well-known that Taiwan''s economic growth rates
are relatively high in the periods of the 1970s and the 1980s,
but slowed down for the post-1990s.
The average economic growth rates for the
pre-1990s and the post-1990s are 8.91 and 6.20 percent, respectively.
If we estimate Hamilton''s (1989) Markov-switching model with
mean-switching on the GDP growth rate,
the estimates of high-growth and low-growth states
are 11.48 percent and 6.77 percent in Taiwan, respectively.
It is reasonable that the post-1990s will
be identified as a contraction phase, although there are three more
CEPD-defined contraction chronologies for the post-1990s.
The second chapter aims to solving the alluded problem.
As noted by Burns and Mitchell (1946, p. 3),
a business cycle ''consist of expansions occurring at
about the same time in many economic activities,
followed by similar general recessions, contractions and revivals...''''
That is, they established two defining characteristics of the business cycle --
the co-movement among economic variables
through the cycle and the nonlinearity in the evolution of the business cycle.
My strategy is to generalize Hamilton''s (1989) univariate
Markov-switching model to the multivariate case.
In particular, I pick up the idea of Diebold and Rudebusch
(1996) and estimate a multivariate dynamic Markov-switching factor
model for a vector of macroeconomic variables. The approach
captures both the idea of the business cycle as co-movement in
several macroeconomic variables and the asymmetric nature of the
business cycle phases.
The empirical model is first transformed to the state-space representation,
and Kim''s (1994) algorithm is adopted to implement the estimation.
The empirical results suggest that the business chronologies
identified by the multivariate Markov-switching factor model with
GDP, consumption and investment are more consistent with
the CEPD-defined chronologies than those of defined by the univariate
Markov-switching models,
especially for the post-1990.
The third chapter turns to analyze Taiwan''s stock market volatility.
As the stock market is closely intertwined with the economy in Taiwan,
it is important to understanding the stock market volatility behavior.
The high persistence in the GARCH model is difficult to
reconcile with the poor forecasting performance is well-known
in the literature.
Diebold (1986) and Lamoureux and Lastrapes (1990)
argued that the high persistence may reflect
structural change in the variance process.
Does Taiwan''s stock market also has above problems?
I examine the volatility of Taiwan''s stock market by means
of the generalized autoregressive conditional
heteroscedasticity (GARCH) and the switching autoregressive
conditional heteroscedasticity (SWARCH) models.
The daily return of Taiex
is used as a summative measure of stock volatility.
The empirical results conclude that the SWARCH models
do a better job in forecasting than the GARCH models.
In addition, for Taiwan stock market
there exists a positive and significant leverage effect
that a stock price decrease has a greater effect on
subsequent volatility than would a
stock price increase of the same magnitude.
We have identified every episode
causing the high volatility state in
Taiwan stock market.
The estimates attribute most
of the persistence in stock price volatility
to the persistence of low, medium and
high volatility regimes and the
high volatility regime is
associated with the business recession at the beginning of the 1990s.
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