Applying the structural equation model rule-based fuzzy system with genetic algorithm for trading in currency market

The present study uses the structural equation model (SEM) to analyze the correlations between various economic indices pertaining to latent variables, such as the New Taiwan Dollar (NTD) value, the United States Dollar (USD) value, and USD index. In addition, a risk factor of volatility of currency returns is considered to develop a risk-controllable fuzzy inference system. The rational and linguistic knowledge-based fuzzy rules are established based on the SEM model and then optimized using the genetic algorithm. The empirical results reveal that the fuzzy logic trading system using the SEM indeed outperforms the buy-and-hold strategy. Moreover, when considering the risk factor of currency volatility, the performance appears significantly better. Remarkably, the trading strategy is apparently affected when the USD value or the volatility of currency returns shifts into either a higher or lower state.Knowledge-based Systems, Fuzzy Sets, Structural Equation Model (SEM), Genetic Algorithm (GA), Currency Volatility

Stock index hedge using trend and volatility regime switch model considering hedging cost

This paper studies the risk hedging between stock index and underlying futures. The hedging ratios are optimized using the mean-variance utility function as considering the hedging cost. The trend of returns and variance are estimated by the model of regime switch on both vector autoregression (VAR) and GARCH(1,1) compared to three restricted models: VAR switch only, GARCH(1,1) switch only, and no switch. The hedge portfolio is constructed by Morgan Stanley Taiwan Index (MSTI) and Singapore Traded MSTI futures. The hedge horizon is set as a week to reduce the hedging cost and the weekly in-sample data cover from 08/09/2001 to 05/31/2007. The rolling window technique is used to evaluate the hedge performances of out-of-sample period spanning subprime, Greek debt, and post-risk durations. The subprime period indeed is evidenced very vital to achieve the hedge performance. All models perform surprisingly far above average during subprime period. The hedge ratios indeed are the tradeoff between maximum expected return and minimum variance. It is demonstrated challenging for all models to increase returns and reduce risk together. The hedge context is further classified into four hedge states: uu, ud, du, and dd (u and d denote respectively usual and down) using the state probabilities of series. The regime switch models are found to have much greater wealth increase when in dd state. It is decisive to hedge risk in dd state when volatility is extensively higher as observed recurrently in subprime period. Remarkably, the trend switch is found having larger wealth increase while the volatility switch is not found prominent between models. While the no switch model has larger utility increase in uu state as most observed in Greek debt or post risk period, its performance is far below average like other models

Measuring and Testing Tail Dependence and Contagion Risk between Major Stock Markets

In this paper, three copula GARCH models i.e. Gaussian, Student-t, and Clayton are used to estimate and test the tail dependence measured by Kendall’s tau between six stock indices. Since the contagion risk spreads from large markets to small markets, the tail dependence is studied for smaller Taiwanese and South Korean stock markets, i.e. Taiex and Kospi against four larger stock markets, i.e. S&P500, Nikkei, MSCI China, and MSCI Europe. The vector autoregression result indicates that S&P500 and MSCI China indeed impact mostly and significantly to the other four stock markets. However, the tail dependence of both Taiex and Kospi against S&P500 and MSCI Chia are lower due to unilateral impacts from US and China. Using Clayton copula GARCH, the threshold tests of Kendall’s tau between most stock markets except China are significant during both subprime and Greek debt crises. The tests of Student-t copula GARCH estimated Kendall’s taus are only acceptable for subprime crisis but not for Greek debt crisis. Thus, Clayton copula GARCH is found appropriate to estimate Kendall’s taus as tested by threshold regression

Measuring Contagion Risk in High Volatility State between Major Banks in Taiwan by Threshold Copula GARCH Model

This paper aims to study the structural tail dependences and risk magnitude of contagion risk during high risk state between domestic and foreign banks. Empirically, volatility of stock returns has the properties of persistence, clustering, heteroscedasticity, and regime switchs. Thus, the threshold regression model having piecewise regression capability is used to classify the volatility index of influential foreign banks as “high” and “low” of two volatility states to further estimate Kendall taus i.e. structural tail dependences between banks using three models: Gaussian, t, and Clay copula GARCH. Using fuzzy c-means method, both domestic and foreign banks are categorized into 10 groups. Through the groups, 5 domestic and 7 foreign representative banks are identified as the research objects. Then, the in-sample data of daily banks’ stock prices covering 01/03/2003 ~06/30/2006 are used to estimate the parameters of threshold copula GARCH model and Kendall taus. The out-of-sample data covering 07/01/2006~03/25/2014 are used to estimate the Kendall taus gradually using rolling window technique. Several research findings are described as follows. In high state, the tail dependences are two times much larger than in low state and most of them have up-trend property after sub-prime crisis and reach the peak during Greek debt. It implies that the volatility is high in risk event and the contagion is high after risk event. In high state, HNC has the highest tail dependences with foreign banks but its value at risk is the lowest. It can be considered as an international attribute bank with lower risk. On the contrary, YCB and FCB have the lower tail dependences with foreign banks but their value at risks are quite high. They are viewed as a local attribute bank with higher risk. The Bank of American, Citigroup, and UBS AG have the relatively higher value at risk. Citigroup has been tested to Granger cause ANZ and all domestic banks. It is necessary to beware the contagion risk from Citigroup. Among three models, in low state, Gaussian and t copula models have the better significance of estimation than Clay copula model. However in high state, Clay copula model has the same acceptable estimation and more capability to uncover the instant nonlinear jumps of tail dependences while Gaussian and t copula models reveal the linear changes of tail dependences as a curve

Measuring and Testing Tail Dependence and Contagion Risk between Major Stock Markets

In this paper, three copula GARCH models i.e. Gaussian, Student-t, and Clayton are used to estimate and test the tail dependence measured by Kendall’s tau between six stock indices. Since the contagion risk spreads from large markets to small markets, the tail dependence is studied for smaller Taiwanese and South Korean stock markets, i.e. Taiex and Kospi against four larger stock markets, i.e. S&P500, Nikkei, MSCI China, and MSCI Europe. The vector autoregression result indicates that S&P500 and MSCI China indeed impact mostly and significantly to the other four stock markets. However, the tail dependence of both Taiex and Kospi against S&P500 and MSCI Chia are lower due to unilateral impacts from US and China. Using Clayton copula GARCH, the threshold tests of Kendall’s tau between most stock markets except China are significant during both subprime and Greek debt crises. The tests of Student-t copula GARCH estimated Kendall’s taus are only acceptable for subprime crisis but not for Greek debt crisis. Thus, Clayton copula GARCH is found appropriate to estimate Kendall’s taus as tested by threshold regression

Measuring and Testing Tail Dependence and Contagion Risk between Major Stock Markets

In this paper, three copula GARCH models i.e. Gaussian, Student-t, and Clayton are used to estimate and test the tail dependence measured by Kendall’s tau between six stock indices. Since the contagion risk spreads from large markets to small markets, the tail dependence is studied for smaller Taiwanese and South Korean stock markets, i.e. Taiex and Kospi against four larger stock markets, i.e. S&P500, Nikkei, MSCI China, and MSCI Europe. The vector autoregression result indicates that S&P500 and MSCI China indeed impact mostly and significantly to the other four stock markets. However, the tail dependence of both Taiex and Kospi against S&P500 and MSCI Chia are lower due to unilateral impacts from US and China. Using Clayton copula GARCH, the threshold tests of Kendall’s tau between most stock markets except China are significant during both subprime and Greek debt crises. The tests of Student-t copula GARCH estimated Kendall’s taus are only acceptable for subprime crisis but not for Greek debt crisis. Thus, Clayton copula GARCH is found appropriate to estimate Kendall’s taus as tested by threshold regression

Stock index hedge using trend and volatility regime switch model considering hedging cost

This paper studies the risk hedging between stock index and underlying futures. The hedging ratios are optimized using the mean-variance utility function as considering the hedging cost. The trend of returns and variance are estimated by the model of regime switch on both vector autoregression (VAR) and GARCH(1,1) compared to three restricted models: VAR switch only, GARCH(1,1) switch only, and no switch. The hedge portfolio is constructed by Morgan Stanley Taiwan Index (MSTI) and Singapore Traded MSTI futures. The hedge horizon is set as a week to reduce the hedging cost and the weekly in-sample data cover from 08/09/2001 to 05/31/2007. The rolling window technique is used to evaluate the hedge performances of out-of-sample period spanning subprime, Greek debt, and post-risk durations. The subprime period indeed is evidenced very vital to achieve the hedge performance. All models perform surprisingly far above average during subprime period. The hedge ratios indeed are the tradeoff between maximum expected return and minimum variance. It is demonstrated challenging for all models to increase returns and reduce risk together. The hedge context is further classified into four hedge states: uu, ud, du, and dd (u and d denote respectively usual and down) using the state probabilities of series. The regime switch models are found to have much greater wealth increase when in dd state. It is decisive to hedge risk in dd state when volatility is extensively higher as observed recurrently in subprime period. Remarkably, the trend switch is found having larger wealth increase while the volatility switch is not found prominent between models. While the no switch model has larger utility increase in uu state as most observed in Greek debt or post risk period, its performance is far below average like other models

Stock index hedge using trend and volatility regime switch model considering hedging cost

This paper studies the risk hedging between stock index and underlying futures. The hedging ratios are optimized using the mean-variance utility function as considering the hedging cost. The trend of returns and variance are estimated by the model of regime switch on both vector autoregression (VAR) and GARCH(1,1) compared to three restricted models: VAR switch only, GARCH(1,1) switch only, and no switch. The hedge portfolio is constructed by Morgan Stanley Taiwan Index (MSTI) and Singapore Traded MSTI futures. The hedge horizon is set as a week to reduce the hedging cost and the weekly in-sample data cover from 08/09/2001 to 05/31/2007. The rolling window technique is used to evaluate the hedge performances of out-of-sample period spanning subprime, Greek debt, and post-risk durations. The subprime period indeed is evidenced very vital to achieve the hedge performance. All models perform surprisingly far above average during subprime period. The hedge ratios indeed are the tradeoff between maximum expected return and minimum variance. It is demonstrated challenging for all models to increase returns and reduce risk together. The hedge context is further classified into four hedge states: uu, ud, du, and dd (u and d denote respectively usual and down) using the state probabilities of series. The regime switch models are found to have much greater wealth increase when in dd state. It is decisive to hedge risk in dd state when volatility is extensively higher as observed recurrently in subprime period. Remarkably, the trend switch is found having larger wealth increase while the volatility switch is not found prominent between models. While the no switch model has larger utility increase in uu state as most observed in Greek debt or post risk period, its performance is far below average like other models

Applying the structural equation model rule-based fuzzy system with genetic algorithm for trading in currency market

The present study uses the structural equation model (SEM) to analyze the correlations between various economic indices pertaining to latent variables, such as the New Taiwan Dollar (NTD) value, the United States Dollar (USD) value, and USD index. In addition, a risk factor of volatility of currency returns is considered to develop a risk-controllable fuzzy inference system. The rational and linguistic knowledge-based fuzzy rules are established based on the SEM model and then optimized using the genetic algorithm. The empirical results reveal that the fuzzy logic trading system using the SEM indeed outperforms the buy-and-hold strategy. Moreover, when considering the risk factor of currency volatility, the performance appears significantly better. Remarkably, the trading strategy is apparently affected when the USD value or the volatility of currency returns shifts into either a higher or lower state

Applying the structural equation model rule-based fuzzy system with genetic algorithm for trading in currency market

The present study uses the structural equation model (SEM) to analyze the correlations between various economic indices pertaining to latent variables, such as the New Taiwan Dollar (NTD) value, the United States Dollar (USD) value, and USD index. In addition, a risk factor of volatility of currency returns is considered to develop a risk-controllable fuzzy inference system. The rational and linguistic knowledge-based fuzzy rules are established based on the SEM model and then optimized using the genetic algorithm. The empirical results reveal that the fuzzy logic trading system using the SEM indeed outperforms the buy-and-hold strategy. Moreover, when considering the risk factor of currency volatility, the performance appears significantly better. Remarkably, the trading strategy is apparently affected when the USD value or the volatility of currency returns shifts into either a higher or lower state