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Papers by Edward Sun
Empirical Economics, 2009
Analyzing equity market co-movements is important for risk diversification of an international po... more Analyzing equity market co-movements is important for risk diversification of an international portfolio. Copulas have several advantages compared to the linear correlation measure in modeling co-movement. This paper introduces a copula ARMA-GARCH model for analyzing the co-movement of international equity markets. The model is implemented with an ARMA-GARCH model for the marginal distributions and a copula for the joint distribution. After goodness of fit testing, we find that the Student's t copula ARMA(1,1)-GARCH(1,1) model with fractional Gaussian noise is superior to alternative models investigated in our study where we model the simultaneous co-movement of nine international equity market indexes. This model is also suitable for capturing the long-range dependence and tail dependence observed in international equity markets.
Journal of Computational Analysis and Applications
In this paper, we propose the mixed-stable model for analyzing high-frequency stock return data t... more In this paper, we propose the mixed-stable model for analyzing high-frequency stock return data that usually contain a large number of zeros. Based on the data of German Dax component stocks, we apply the stable and mix-stable laws (both with dependent and independent states) to model the data. We also investigate the self-similarity and multifractality of the data. We show the empirical results of the model performance.
Journal of Computational Analysis and Applications
Statistical models of financial data series and algorithms of forecasting and investment are the ... more Statistical models of financial data series and algorithms of forecasting and investment are the topic of this research. The objects of research are the historical data of financial securities, statistical models of stock returns, parameter estimation methods, effects of self-similarity and multifractality, and algorithms of financial portfolio selection. The numerical methods (MLE and robust) for parameter estimation of stable models have been created and their efficiency were compared. Complex analysis methods of testing stability hypotheses have been created and special software was developed (nonparametric distribution fitting tests were performed and homogeneity of aggregated and original series was tested; theoretical and practical analysis of self-similarity and multifractality was made). The passivity problem in emerging markets is solved by introducing the mixed-stable model. This model generalizes the stable financial series modeling. 99% of the Baltic States series satisf...
Empirical Economics, 2009
Analyzing equity market co-movements is important for risk diversification of an international po... more Analyzing equity market co-movements is important for risk diversification of an international portfolio. Copulas have several advantages compared to the linear correlation measure in modeling co-movement. This paper introduces a copula ARMA-GARCH model for analyzing the co-movement of international equity markets. The model is implemented with an ARMA-GARCH model for the marginal distributions and a copula for the joint distribution. After goodness of fit testing, we find that the Student's t copula ARMA(1,1)-GARCH(1,1) model with fractional Gaussian noise is superior to alternative models investigated in our study where we model the simultaneous co-movement of nine international equity market indexes. This model is also suitable for capturing the long-range dependence and tail dependence observed in international equity markets.
Journal of Computational Analysis and Applications
In this paper, we propose the mixed-stable model for analyzing high-frequency stock return data t... more In this paper, we propose the mixed-stable model for analyzing high-frequency stock return data that usually contain a large number of zeros. Based on the data of German Dax component stocks, we apply the stable and mix-stable laws (both with dependent and independent states) to model the data. We also investigate the self-similarity and multifractality of the data. We show the empirical results of the model performance.
Journal of Computational Analysis and Applications
Statistical models of financial data series and algorithms of forecasting and investment are the ... more Statistical models of financial data series and algorithms of forecasting and investment are the topic of this research. The objects of research are the historical data of financial securities, statistical models of stock returns, parameter estimation methods, effects of self-similarity and multifractality, and algorithms of financial portfolio selection. The numerical methods (MLE and robust) for parameter estimation of stable models have been created and their efficiency were compared. Complex analysis methods of testing stability hypotheses have been created and special software was developed (nonparametric distribution fitting tests were performed and homogeneity of aggregated and original series was tested; theoretical and practical analysis of self-similarity and multifractality was made). The passivity problem in emerging markets is solved by introducing the mixed-stable model. This model generalizes the stable financial series modeling. 99% of the Baltic States series satisf...