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研究生:莊珮玲
論文名稱:探究市場波動度資訊在技術分析中的價值
論文名稱(外文):The Informational Role of Market Volatility in Technical Analysis
指導教授:郭炳伸郭炳伸引用關係林信助林信助引用關係
學位類別:博士
校院名稱:國立政治大學
系所名稱:國際經營與貿易研究所
學門:商業及管理學門
學類:貿易學類
論文種類:學術論文
畢業學年度:101
語文別:英文
論文頁數:76
中文關鍵詞:市場波動度技術分析
外文關鍵詞:Market VolatilityTechnical Analysis
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The theme of this thesis seeks to explore the value of information of market volatility in technical analysis. In the literature, the technical analysis primarily involves the use of the information of past prices and/or volumes to predict future price movements in financial assets, yet little is known about whether there exists other information that is valuable to improve the predictability of technical analysis. The possible relation between volatility and profitability of technical analysis mentioned in some studies drives us to investigate whether the information of market volatility within the framework of the technical analysis can improve our understanding toward the market price movements.

1.Does Market Volatility Improve Profitability of Technical Analysis?

This chapter first studies whether the information of market volatility is capable of yielding higher profitability. Specifically, we compare the performance of a Variable Moving Average (VMA) rule, in which market volatility plays an important role, with five other popular trading rules. When applied to the Dow Jones Industrial Average index, the Superior Predictive Ability test by Hansen (2005) shows that the VMA rule outperforms other rules with higher profitability. Second, to further investigate the origin of superior profitability, we conduct the test of Cumby and Modest (1987), and find that the VMA rule does enjoy better market timing ability. Third, we explore whether the VMA rule has differential performance in different market conditions. The results show that the market timing ability of the best VMA rule is asymmetric in bull and bear markets, and the best VMA rule outperforms the Moving Average (MA) rule and the Momentum Strategies in Volume (MSV) rule both in bull and bear markets, particular in bear markets.

2.Exploring the Information Content of Market Volatility in Technical Analysis

In this chapter, we study how market volatility information affects trading signals generated from the technical analysis. Through the use of the time-varying-transition-probability (TVTP) Markov-switching model, we find that the increase of market volatility leads to a higher probability of signals generated from the VMA rule. Moreover, such an effect is asymmetric in bull and bear markets. This chapter also reexamines the value of market volatility in the simple MA rule by comparing the trading signals produced from the Fixed-transition-probability (FTP) and the TVTP Markov-switching model. Our results show that the time to enter or exit the market affected by market volatility information will benefit investors with higher profit.
1 Introduction 1

2 Does Market Volatility Improve Profitability of Technical Analysis? 3
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Technical Trading Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2.1 Static Trading Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2.2 Dynamic Trading Rule . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.1 Data Snooping Bias and the SPA test . . . . . . . . . . . . . . . . . . 10
2.4 Empirical Results on the Trading Rule Profitability . . . . . . . . . . . . . . . 12
2.4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.4.2 SPA Test Results: Full Sample and Sub-samples . . . . . . . . . . . . 12
2.5 Market Timing Ability Test . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.6 Is the Profitability of the Trading Rule Asymmetric in Different Market Conditions?
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.6.1 Market Timing Ability in Bull and Bear Market . . . . . . . . . . . . . 24
2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3 Exploring the Information Content of Market Volatility in Technical Analysis 32
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2 Moving Average Trading Systems . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2.1 Market Volatility in Moving Averages . . . . . . . . . . . . . . . . . . 35
3.3 Trading Signals and Market Volatility Ratio . . . . . . . . . . . . . . . . . . . 37
3.3.1 The TVTP Markov-Switching Model . . . . . . . . . . . . . . . . . . 38
3.3.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3.3 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.3.4 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.4 The Value of Market Volatility Ratio in Simple Moving Average Rule . . . . . 51
3.4.1 Data and Estimation Results . . . . . . . . . . . . . . . . . . . . . . . 52
3.4.2 The Profitability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.4.3 Simple Analysis for Trades . . . . . . . . . . . . . . . . . . . . . . . . 58
3.5 The Future Way of Exploring Explanations for Higher Profits Gained from Market Volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
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