Incorporating a leading indicator into the trading rule through the Markov-switching vector autoregression model

Tzu Pu Chang*, Jin-Li Hu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This article examines the profitability of trading rules based on the smoothed probability of Markov-switching models and executes two models in Taiwan's case. The results present that both proposed models can earn excess returns over the buy-and-hold strategy and support that both can be used to trade. However, the univariate Markov-switching model, which only uses daily returns series does not successfully capture the trend in the stock market, especially during a bull market. This implies that high-frequency returns series contain lots of noises. In order to overcome this problem, the Markov-switching vector autoregression model that combines a leading indicator and returns is performed in this study. The results indicate a better trading pattern. We conclude that the leading indicator chosen from open interest in the future market increases useful information and reduces noises to improve model estimation, which can well identify the position of bull and bear markets.

Original languageEnglish
Pages (from-to)1255-1259
Number of pages5
JournalApplied Economics Letters
Volume16
Issue number12
DOIs
StatePublished - 1 Aug 2009

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