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Bu çalışmada, Türkiye’de 2001:05-2005:12 dönemi için Para Arzı (M1), Faiz Oranları ve Hisse Senedi Fiyatları arasındaki dinamik ilişkiler incelenmiştir. Bu amaçla, bir Standart VAR Modeli altında, Granger Nedensellik Testi ile değişkenler arasındaki nedensellik ilişkilerinin yönü belirlenmeye çalışılmış, Para Arzı’ ndan Hisse Senedi Fiyatları’ na doğru tek yönlü bir nedensellik ilişkisi bulunmuştur. Standart VAR modeli yardımıyla elde edilen Etki- Tepki Analizi ve Varyans Ayrımlaştırma Analizi sonuçları da, nedensellik ilişkisini desteklemektedir. Bu sonuçlara göre; Hisse Senedi Fiyatları’ nın, Merkez Bankası’ nın para politikası ayarlaması için bir gösterge olarak kullanılabileceğini söylemek mümkündür.

Bu çalışmada, Türkiye’de 2001:05-2005:12 dönemi için Para Arzı (M1), Faiz Oranları ve Hisse Senedi Fiyatları arasındaki dinamik ilişkiler incelenmiştir. Bu amaçla, bir Standart VAR Modeli altında, Granger Nedensellik Testi ile değişkenler arasındaki nedensellik ilişkilerinin yönü belirlenmeye çalışılmış, Para Arzı’ ndan Hisse Senedi Fiyatları’ na doğru tek yönlü bir nedensellik ilişkisi bulunmuştur. Standart VAR modeli yardımıyla elde edilen Etki- Tepki Analizi ve Varyans Ayrımlaştırma Analizi sonuçları da, nedensellik ilişkisini desteklemektedir. Bu sonuçlara göre; Hisse Senedi Fiyatları’ nın, Merkez Bankası’ nın para politikası ayarlaması için bir gösterge olarak kullanılabileceğini söylemek mümkündür.

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.

ÖZ: Çalışma ARFIMA-FIGARCH modelleri yardımıyla Türkiye hisse senedi piyasası getirilerinde ikili uzun hafıza özelliğinin varlığını incelemekte dolayısıyla zayıf formda etkin piyasa hipotezini test etmektedir. Bu amaçla kullanılan veri 2010-2013 dönemi Borsa İstanbul (BIST) için günlük hisse senedi kapanış fiyatlarını içermektedir. Öncelikle ortalama ve oynaklıktaki uzun hafızanın varlığı ayrı olarak incelenmiştir. ARFIMA modeli sonuçları BIST getirileri için ortalamada uzun hafıza özelliği gösterirken, getiri oynaklıklarındaki uzun hafızanın varlığı için FIGARCH modeli de istatistiksel olarak anlamlı sonuçlar vermiştir. İkinci olarak, ortalama ve oynaklıktaki birlikte uzun hafıza özelliği ARFIMA-FIGARCH modeli ile değerlendirilmiştir. Sonuç olarak, ortalamada uzun hafızanın varlığına dair bir bulgu elde edilemezken, oynaklığın öngörülebilir bir yapı gösterdiği Türkiye borsası etkin bir piyasa değildir. Anahtar Kelimeler: ARFIMA-FIGARCH, İkili Uzun Hafıza, Oynaklık, Yapısal Kırılma, Etkin Piyasa Hipotezi. ABSTRACT: The study examines presence of dual long memory property in returns of Turkish Stock Market by using ARFIMA-FIGARCH model and, tests Weak Form Efficient Market Hypothesis. The data set consists of daily closing prices for the period 2010 to 2013 of Istanbul Stock Exchange. Firstly, long memory property in return and volatility has been investigated separately. FIGARCH model indicates statistically significant findings while the results of ARFIMA model display long memory dynamics in returns of BIST. Secondly, long memory in return and volatility has been evaluated simultaneously by using ARFIMA-FIGARCH model. Consequently, Turkish Stock Market is not Efficient Market because volatility shows forecastable structure while there have not been obtained any finding about presence of long memory in return. Keywords: ARFIMA-FIGARCH, Dual Long Memory, Volatility, Structural Break, Efficient Market Hypothesis.

Bu çalışmada, Türkiye’de 2001:05-2005:12 dönemi için Para Arzı (M1), Faiz Oranları ve Hisse Senedi Fiyatları arasındaki dinamik ilişkiler incelenmiştir. Bu amaçla, bir Standart VAR Modeli altında, Granger Nedensellik Testi ile değişkenler arasındaki nedensellik ilişkilerinin yönü belirlenmeye çalışılmış, Para Arzı’ ndan Hisse Senedi Fiyatları’ na doğru tek yönlü bir nedensellik ilişkisi bulunmuştur. Standart VAR modeli yardımıyla elde edilen Etki- Tepki Analizi ve Varyans Ayrımlaştırma Analizi sonuçları da, nedensellik ilişkisini desteklemektedir. Bu sonuçlara göre; Hisse Senedi Fiyatları’ nın, Merkez Bankası’ nın para politikası ayarlaması için bir gösterge olarak kullanılabileceğini söylemek mümkündür.

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