Taiwan Stock Index Research Articles

The Effect of Futures Introduction on Market Volatilityand Information Transmission

This paper provides some evidence in support of the information transmission hypothesis that the onset of SIMEXfs MSCI Taiwan index futures and TAIFEXfs Taiwan index futures has helped stabilize the volatility process of Taiwanfs spot market. We use daily data of Taiwanfs stock index from January 1995 to December 2000 to estimate a GARCH model before and after futures trading. Before futures trading, the effects of volatility shocks tended to be more persistent. After futures trading, the volatility process became more stabilized such that shocks are more quickly reflected in the system and the system reverts to its normal state relatively quicker. This is consistent with the hypothesis that the flow of information has accelerated with the opening of futures trading. In other words, Taiwanfs stock market has improved its efficiency in processing market information due to trading of futures contracts in SIMEX and in TAIFEX. To check the robustness of our result, other data set was employed - the over-the-counter market index. The study showed that using the over-the-counter data set revealed the consistent phenomena with using the Taiwan stock index. Namely, the introduction of futures trading on market volatility and information transmission has positive effects in the underlying assetfs market efficiency. Key words:GARCH Model, Information Transmission Hypothesis, Volatility, Futures market

Trading activity in stock index futures markets: The evidence of emerging markets

AbstractThis study investigates the trading activity of the Taiwan Futures Exchange (TAIFEX) and Singapore Exchange Derivatives Trading Limited (SGX‐DT) Taiwan Stock Index Futures markets by analyzing the intraday patterns of volume and volatility. In addition, the market closure theory, which may explain such patterns, is examined. Overall, the trading pattern appears to be U‐shaped for the TAIFEX futures and U+W‐shaped for the SGX‐DT. For the SGX‐DT futures, volatility follows the same pattern as that of the number of price changes. For the TAIFEX futures, however, after the peak at the close of the spot market, the volatility in the TAIFEX futures drops consistently until the end of the day while volatility in the SGX‐DT still reaches a smaller peak at the close of the futures market. In addition, a visual inspection of the intraday patterns of these two markets shows that the market closure theory can effectively explain the intraday patterns of these two markets. The empirical results support the market closure theory in that liquidity demand from traders rebalancing their portfolios before and after market closures creates larger volume and volatility at both the open and close. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:983–1003, 2002

Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index

In this study, we attempt to model and predict the direction of return on market index of the Taiwan Stock Exchange, one of the fastest growing financial exchanges in developing Asian countries. Our motivation is based on the notion that trading strategies guided by forecasts of the direction of price movement may be more effective and lead to higher profits. The probabilistic neural network (PNN) is used to forecast the direction of index return after it is trained by historical data. Statistical performance of the PNN forecasts are measured and compared with that of the generalized methods of moments (GMM) with Kalman filter. Moreover, the forecasts are applied to various index trading strategies, of which the performances are compared with those generated by the buy-and-hold strategy as well as the investment strategies guided by forecasts estimated by the random walk model and the parametric GMM models. Empirical results show that the PNN-based investment strategies obtain higher returns than other investment strategies examined in this study. Influences of length of investment horizon and commission rate are also considered.

On the Effect of Stock Stabilization Fund: A Case of Taiwan

The purpose of this paper is to investigate the effect of Taiwan's stock stabilization fund on the stock market. Stabilization funds have long been used by many countries to achieve the goal of market stabilization. However, the actual impacts of the funds still remain a puzzle. This is the first paper that empirically examines the effect of the stabilization fund on the stock market. Taiwan's stock stabilization fund, which was inaugurated in February 1996 after the panic selling induced by the announcement of military exercise by mainland China, provides an experimental environment for investigating whether this alternative mechanism is effective in stabilizing the stock price when the market is in panic. By intervention analysis, we find that in the short run, the stabilization fund is statistically significant in preventing the market price from further declining after the announcement of the mainland China's military exercise. This evidence still holds after controlling the effects of the following two events: Morgan-Stanley's announcement to include Taiwan stock index into its worldwide equity market indices, and the event of U.S. aircraft carrier sent to Taiwan Strait to secure this area during the period of military exercise by mainland China.

Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index

In the last decade, neural networks have drawn noticeable attention from many computer and operations researchers. While some previous studies have found encouraging results with using this artificial intelligence technique to predict the movements of established financial markets, it is interesting to verify the persistence of this performance in the emerging markets. These rapid growing financial markets are usually characterized by high volatility, relatively smaller capitalization, and less price efficiency, features which may hinder the effectiveness of those forecasting models developed for established markets. In this study, we attempt to model and predict the direction of return on the Taiwan Stock Exchange Index, one of the fastest growing financial exchanges in developing Asian countries. Our approach is based on the notion that trading strategies guided by forecasts of the direction of price movement may be more effective and lead to higher profits. The Probabilistic Neural Network (PNN) is used to forecast the direction of index return after it is trained by historical data. The forecasts are applied to various index trading strategies, of which the performances are compared with those generated by the buy and hold strategy, and the investment strategies guided by the forecasts estimated by the random walk model and the parametric Generalized Methods of Moments (GMM) with Kalman filter. Empirical results show that the PNN-based investment strategies obtain higher returns than other investment strategies examined in this study. The influences of the length of investment horizon and the commission rate are also considered.

An empirical investigation of trading volume and return volatility of the Taiwan Stock Market

This study examines the Mixed Distribution Hypothesis (MDH) using 5-min interval stock returns of the Taiwan Stock Index (TSI). Startlingly enough, the persistence of stock volatility remains dominant after the stochastic mixing variable was included in the variance equation. It implies that the MDH cannot explain away the ARCH phenomenon. We have found that the composition of participants (approximately 92% of participants are individual investors) in TSI is a major contributing factor to the persistent volatility. In addition, the existence of limits on price changes, to some extent, accounts for the persistence phenomenon. Similar results are also found for individual stocks in the sample. Interestingly enough, the explanatory power of trading volume exhibits a U-shaped pattern in explaining return volatility in Taiwan Stock Market.