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Value-at-Risk (VaR) is a standard tool for measuring potential risk of economic losses in financial markets. In this study, we examine the convenience of the FIGARCH (1, d, 1) and FIAPARCH (1, d, 1) models in evaluating asymmetry features and long memory in the volatility of the Turkish Stock Market. Furthermore, we investigate the performances in-sample and out-of-sample Value-at-Risk (VaR) analyses based on Kupiec-LR test by using FIGARCH(1, d, 1) and FIAPARCH (1, d, 1) models with the normal, student-t and skewed student-t distributions. For these analyses, we take into account both short and long trading positions. The empirical results display that the FIAPARCH (1, d, 1) model with skewed student-t distribution is more accurate for in-sample and out-of-sample Value-at-Risk (VaR) analysis for short and long trading positions. In addition, the FIAPARCH(1, d, 1) model with skewed student-t has better accuracy results in capturing stylized facts in the volatility of Turkish Stock Market. Consequently, evaluating of asymmetry and long memory property in volatility of the returns can ensure suitable Value-at-Risk (VaR) model selection for performance of risk management in the Turkish financial markets. The findings can be evaluated by portfolio managers, investors, regulators and financial risk managers. Keyword: Value-at-Risk; FIAPARCH Model; Long Memory; Volatility. JEL Classifications: C13; C58; G10; G15; G17

This study examines the weak-form market efficiency of Pakistan Stock Market namely Karachi Stock Exchange for the period 2010-2013. The efficiency of stock market has tested by using ARFIMA-FIGARCH models estimated under different distribution assumptions as Normal, Student-t, Skewed Student- t and GED distribution. According to findings of study, ARFIMA model do not support long memory behaviour for the stock market returns. However, FIGARCH model indicate that volatility of market returns has long memory. Moreover, in order to test the feature of long memory in the return and volatility of the stock market simultaneously, ARFIMA-FIGARCH models are estimated according to different distributions simultaneously. Predictable structure of volatility of Pakistan Stock Market display that this market is the weak-form market inefficiency. Consequently, it is possible to say that technical analysis related to this stock market may be valid. This implies that it is possible to predict future stock prices and extra ordinary gains could be obtained trading in this market.

Value-at-Risk (VaR) is a standard tool for measuring potential risk of economic losses in financial markets. In this study, we examine the convenience of the FIGARCH (1, d, 1) and FIAPARCH (1, d, 1) models in evaluating asymmetry features and long memory in the volatility of the Turkish Stock Market. Furthermore, we investigate the performances in-sample and out-of-sample Value-at-Risk (VaR) analyses based on Kupiec-LR test by using FIGARCH(1, d, 1) and FIAPARCH (1, d, 1) models with the normal, student-t and skewed student-t distributions. For these analyses, we take into account both short and long trading positions. The empirical results display that the FIAPARCH (1, d, 1) model with skewed student-t distribution is more accurate for in-sample and out-of-sample Value-at-Risk (VaR) analysis for short and long trading positions. In addition, the FIAPARCH(1, d, 1) model with skewed student-t has better accuracy results in capturing stylized facts in the volatility of Turkish Stock Market. Consequently, evaluating of asymmetry and long memory property in volatility of the returns can ensure suitable Value-at-Risk (VaR) model selection for performance of risk management in the Turkish financial markets. The findings can be evaluated by portfolio managers, investors, regulators and financial risk managers. Keyword: Value-at-Risk; FIAPARCH Model; Long Memory; Volatility. JEL Classifications: C13; C58; G10; G15; G17

Value-at-Risk (VaR) is a standard tool for measuring potential risk of economic losses in financial markets. In this study, we examine the convenience of the FIGARCH (1, d, 1) and FIAPARCH (1, d, 1) models in evaluating asymmetry features and long memory in the volatility of the Turkish Stock Market. Furthermore, we investigate the performances in-sample and out-of-sample Value-at-Risk (VaR) analyses based on Kupiec-LR test by using FIGARCH(1, d, 1) and FIAPARCH (1, d, 1) models with the normal, student-t and skewed student-t distributions. For these analyses, we take into account both short and long trading positions. The empirical results display that the FIAPARCH (1, d, 1) model with skewed student-t distribution is more accurate for in-sample and out-of-sample Value-at-Risk (VaR) analysis for short and long trading positions. In addition, the FIAPARCH(1, d, 1) model with skewed student-t has better accuracy results in capturing stylized facts in the volatility of Turkish Stock Market. Consequently, evaluating of asymmetry and long memory property in volatility of the returns can ensure suitable Value-at-Risk (VaR) model selection for performance of risk management in the Turkish financial markets. The findings can be evaluated by portfolio managers, investors, regulators and financial risk managers. Keyword: Value-at-Risk; FIAPARCH Model; Long Memory; Volatility. JEL Classifications: C13; C58; G10; G15; G17

Value-at-Risk (VaR) is a standard tool for measuring potential risk of economic losses in financial markets. In this study, we examine the convenience of the FIGARCH (1, d, 1) and FIAPARCH (1, d, 1) models in evaluating asymmetry features and long memory in the volatility of the Turkish Stock Market. Furthermore, we investigate the performances in-sample and out-of-sample Value-at-Risk (VaR) analyses based on Kupiec-LR test by using FIGARCH(1, d, 1) and FIAPARCH (1, d, 1) models with the normal, student-t and skewed student-t distributions. For these analyses, we take into account both short and long trading positions. The empirical results display that the FIAPARCH (1, d, 1) model with skewed student-t distribution is more accurate for in-sample and out-of-sample Value-at-Risk (VaR) analysis for short and long trading positions. In addition, the FIAPARCH(1, d, 1) model with skewed student-t has better accuracy results in capturing stylized facts in the volatility of Turkish Stock Market. Consequently, evaluating of asymmetry and long memory property in volatility of the returns can ensure suitable Value-at-Risk (VaR) model selection for performance of risk management in the Turkish financial markets. The findings can be evaluated by portfolio managers, investors, regulators and financial risk managers. Keyword: Value-at-Risk; FIAPARCH Model; Long Memory; Volatility. JEL Classifications: C13; C58; G10; G15; G17

This study examines the weak-form market efficiency of Pakistan Stock Market namely Karachi Stock Exchange for the period 2010-2013. The efficiency of stock market has tested by using ARFIMA-FIGARCH models estimated under different distribution assumptions as Normal, Student-t, Skewed Student- t and GED distribution. According to findings of study, ARFIMA model do not support long memory behaviour for the stock market returns. However, FIGARCH model indicate that volatility of market returns has long memory. Moreover, in order to test the feature of long memory in the return and volatility of the stock market simultaneously, ARFIMA-FIGARCH models are estimated according to different distributions simultaneously. Predictable structure of volatility of Pakistan Stock Market display that this market is the weak-form market inefficiency. Consequently, it is possible to say that technical analysis related to this stock market may be valid. This implies that it is possible to predict future stock prices and extra ordinary gains could be obtained trading in this market. Keyword: Weak-Form Efficient Market Hypothesis; Long Memory; ARFIMA-FIGARCH model; Volatility. JEL Classifications: C58; C13; G10; G15; G17

This study examines the weak-form market efficiency of Pakistan Stock Market namely Karachi Stock Exchange for the period 2010-2013. The efficiency of stock market has tested by using ARFIMA-FIGARCH models estimated under different distribution assumptions as Normal, Student-t, Skewed Student- t and GED distribution. According to findings of study, ARFIMA model do not support long memory behaviour for the stock market returns. However, FIGARCH model indicate that volatility of market returns has long memory. Moreover, in order to test the feature of long memory in the return and volatility of the stock market simultaneously, ARFIMA-FIGARCH models are estimated according to different distributions simultaneously. Predictable structure of volatility of Pakistan Stock Market display that this market is the weak-form market inefficiency. Consequently, it is possible to say that technical analysis related to this stock market may be valid. This implies that it is possible to predict future stock prices and extra ordinary gains could be obtained trading in this market. Keyword: Weak-Form Efficient Market Hypothesis; Long Memory; ARFIMA-FIGARCH model; Volatility. JEL Classifications: C58; C13; G10; G15; G17

This study examines the weak-form market efficiency of Pakistan Stock Market namely Karachi Stock Exchange for the period 2010-2013. The efficiency of stock market has tested by using ARFIMA-FIGARCH models estimated under different distribution assumptions as Normal, Student-t, Skewed Student- t and GED distribution. According to findings of study, ARFIMA model do not support long memory behaviour for the stock market returns. However, FIGARCH model indicate that volatility of market returns has long memory. Moreover, in order to test the feature of long memory in the return and volatility of the stock market simultaneously, ARFIMA-FIGARCH models are estimated according to different distributions simultaneously. Predictable structure of volatility of Pakistan Stock Market display that this market is the weak-form market inefficiency. Consequently, it is possible to say that technical analysis related to this stock market may be valid. This implies that it is possible to predict future stock prices and extra ordinary gains could be obtained trading in this market. Keyword: Weak-Form Efficient Market Hypothesis; Long Memory; ARFIMA-FIGARCH model; Volatility. JEL Classifications: C58; C13; G10; G15; G17