Theories of Risk: Testing Investor Behaviour on the Taiwan Stock and Stock Index Futures Markets

Clark, Ephraim; Qiao, Zhuo; Wong, Wing‐Keung

doi:10.2139/ssrn.3076338

Cited by 7 publications

(8 citation statements)

References 58 publications

(22 reference statements)

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“…The SD theory can be used in many areas, including indifference curves (Wong, 2006(Wong, , 2007Ma and Wong, 2010;Broll, Egozcue, Wong, and Zitikis, 2010), two-moment decision model (Broll, Guo, Welzel, and Wong, 2015;Guo, Wagener, and Wong, 2018), moment rule (Chan, Chow, Guo, and Wong, 2018), economic growth (Chow, Vieito, and Wong, 2018), diversification Egozcue, Fuentes García, Wong, and Zitikis, 2011;Lozza, Wong, Fabozzi, and Egozcue, 2018). It can also be applied to many different assets, including stock (Fong, Lean, and Wong, 2008), fund Zumwalt, 2007, 2012;Wong, Phoon, Lean, 2008), futures (Lean, McAleer, Wong, 2010;Lean, Phoon, Wong, 2012;Qiao, Clark, Wong, 2012;Qiao, Wong, Fung, 2013;Lean, McAleer, Wong, 2015;Clark, Qiao, Wong, 2016), Warrant (Chan, de Peretti, Qiao, Wong, 2012;Wong, Lean, McAleer, Tsai, 2018), Option (Abid, Mroua, and 2009), wine (Bouri, Gupta, Wong, and Zhu, 2018), warrants (Chan, de Peretti, Qiao, and Wong, 2012), gold Zhu, 2015, 2018;Hoang, Zhu, El Khamlichi, and Wong, 2019) , property market (Qiao, Wong, 2015;Tsang, Wong, Horowitz, 2016).…”

Section: Stochastic Dominancementioning

confidence: 99%

Optimal Solution Techniques in Decision Sciences: A Review

Pho

Tran

et al. 2019

SSRN Journal

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Methods to find the optimization solution are fundamental and extremely crucial for scientists to program computational software to solve optimization problems efficiently and for practitioners to use it efficiently. Thus, it is very essential to know about the idea, origin, and usage of these methods. Although the methods have been used for very long time and the theory has been developed too long, most, if not all, of the authors who develop the theory are unknown and the theory has not been stated clearly and systematically. To bridge the gap in the literature in this area and provide academics and practitioners with an overview of the methods, this paper reviews and discusses the four most commonly used methods to find the optimization solution including the bisection, gradient, Newton-Raphson, and secant methods. We first introduce the origin and idea of the methods and develop all the necessary theorems to prove the existence and convergence of the estimate for each method.We then give two examples to illustrate the approaches. Thereafter, we review the literature of the applications of getting the optimization solutions in some important issues and discuss the advantages and disadvantages of each method. We note that all the theorems developed in our paper could be well-known but, so far, we have not seen any book or paper that discusses all the theorems stated in our paper in detail. Thus, we believe the theorems developed in our paper could still have some contributions to the literature. Our review is useful for academics and practitioners in finding the optimization solutions in their studies.

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Section: Stochastic Dominancementioning

confidence: 99%

Optimal Solution Techniques in Decision Sciences: A Review

Pho

Tran

et al. 2019

SSRN Journal

Self Cite

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“…As Fong et al (2005), Lean et al (2008Lean et al ( , 2010, among others, recommend a limited number (100) of grids for comparison, we adopt their suggestion. In order to minimize Type II errors and to accommodate the effect of almost SD (Leshno and Levy, 2002;Guo, et al, 2013Guo, et al, , 2014, we follow Gasbarro et al (2007), Clark et al (2016), among others, to use a conservative 5% cut-off point in examining the proportion of test statistics to draw inferences.…”

Section: Stochastic Dominance (Sd) Approachmentioning

confidence: 99%

“…This implies that the markets for China and Taiwan warrants are not efficient if investors are risk averters (see Qiao et al (2012) , Clark et al (2016), and the references cited therein, for further information).…”

Section: Sd Analysismentioning

confidence: 99%

Why Did Warrant Markets Close in China but Not Taiwan?

2018

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The paper bridges a gap in the literature by using moment analysis, CAPM statistics, stochastic dominance (SD) test, and volume analysis to examine investor preferences for warrants between China and Taiwan, and investigating why the market for warrants in China has to close while the market for Taiwan warrants is successful. Using moment analysis, it is shown that that buying China warrants has a higher likelihood of losses than its Taiwan counterpart. Using CAPM analysis, in general, both the Sharpe ratio and Jensen index for warrants from the Taiwan market are more reasonable, while that from the China market is too negative. On the other hand, the Treynor index for China warrants shows that China warrants are highly volatile. This could make investors avoid investing in China warrants which, in turn, could lead to its closure. Using SD analysis, though there is no arbitrage opportunity between the China and Taiwan warrant markets, it is shown that the markets for China and Taiwan warrants are not efficient, and second-and third-order risk averters prefer to invest in China warrants to warrants in Taiwan. This implies that the warrant issuers prefer to issue Taiwan warrants than China warrants. Using volume analysis, the China warrant market is much more active than the Taiwan warrant market. This could imply that there are more speculative activities in China than in Taiwan which, in turn, could lead to China's decision to close its warrant market. The findings in the paper are useful for investors for investment decisions regarding Taiwan and China warrants, for academic analysis for modelling Taiwan and China warrants, and policy makers for policy making related to Taiwan and China warrants. In the future, China may rethink reopening warrant markets and learning from mature-covered warrant markets such as Taiwan how to inhibit excess speculation and educate warrant investors.

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“…For example, Qiao, Clark, and Wong [49,50] apply SD tests for risk averters and risk seekers to examine investors' preferences with respect to the Taiwan stock index and its corresponding index futures. They find that spot prices dominate futures for risk averters, whereas futures dominate spot for risk seekers.…”

Section: Empirical Studiesmentioning

confidence: 99%