Small-cap stocks are characterized by high volatility and offer investors the opportunity to earn... more Small-cap stocks are characterized by high volatility and offer investors the opportunity to earn higher returns. This paper empirically investigates the impact of the day-of-the-week and the month-of-the year effects on the volatility of daily and monthly CAC SMALL returns in Paris stock market during the period from 1999 to 2015. We propose the SEMIFARMA-SD-GJR-GARCH model, which incorporates stochastic trend, deterministic nonparametric trend, short-range, long-range dependence and seasonal dummy asymmetric GARCH errors. The main findings of this study are that the coefficients of the SEMIFARMA-SD-GJR-GARCH model including the long memory coefficient in the mean equation and the seasonal asymmetry in the variance equation are highly significant and the GJR-GARCH model without seasonal dummies is dominated by the GJR-GARCH model with seasonal dummies (SD-GJR-GARCH). The results indicate that the day-of-the-week and the month-of-the-year effects detected on volatility seem to improve the volatility forecasts. These results support the arbitrage opportunity hypothesis for realizing abnormal returns, and support the inefficiency of CAC small capital market.
This paper analyzes the cyclical behavior of Dow Jones by testing the existence of long memory th... more This paper analyzes the cyclical behavior of Dow Jones by testing the existence of long memory through a new class of semiparametric ARFIMA models with HYGARCH errors (SEMIFARMA-HYGARCH); this class includes nonparametric deterministic trend, stochastic trend, short-range and long-range dependence and long memory heteroscedastic errors. We study the daily returns of the Dow Jones from 1896 to 2006. We estimate several models and we find that the coefficients of the SEMIFARMA-HYGARCH model, including long memory coefficients for the equations of the mean and the conditional variance, are highly significant. The forecasting results show that the informational shocks have permanent effects on volatility and the SEMIFARMA-HYGARCH model has better performance over some other models for long and/or short horizons. The predictions from this model are also better than the predictions of the random walk model; accordingly, the weak efficiency assumption of financial markets seems violated fo...
This paper analyses cyclical behaviour of Orange stock price listed in French stock exchange over... more This paper analyses cyclical behaviour of Orange stock price listed in French stock exchange over 01/03/2000 to 02/02/2017 by testing the nonlinearities through a class of conditional heteroscedastic nonparametric models. The linearity and Gaussianity assumptions are rejected for Orange Stock returns and informational shocks have transitory effects on returns and volatility. The forecasting results show that Orange stock prices are short-term predictable and nonparametric NAR-ARCH model has better performance over parametric MA-APARCH model for short horizons. Plus, the estimates of this model are also better comparing to the predictions of the random walk model. This finding provides evidence for weak form of inefficiency in Paris stock market with limited rationality, thus it emerges arbitrage opportunities.
This paper tests the conditional and non-conditional versions of the Capital Asset Pricing Model ... more This paper tests the conditional and non-conditional versions of the Capital Asset Pricing Model (CAPM) in Gulf Cooperation Council GCC capital markets-wide equity sectors upon daily data during the period from February 22ed 2007 to February 22ed 2012. In the empirical analysis, we used Generalized Autoregressive Conditional heteroscedasticity (GARCH) models with CAPM. Main findings seem to show that the CAPM-EGARCH (1.1) appears more advantages than the traditional CAPM at the sectors considered in this study. This approach can be improved and developed in order to be widely applied as this model takes into account shocks, especially in the crisis period where volatilities are very high. Contribution/ Originality: This study contributes to the literature on the cost of equity capital. It uses a new estimation methodology for beta in a highly volatile environment. It proposes a model taking into account the period of crisis and high volatility of returns. This study is one of the very few studies that have investigated the conditional beta heterogeneity of variance at the level of industries in the GCC stock exchanges.
The aim of this paper is to analyze the cross-market interactions between crude oil prices and wh... more The aim of this paper is to analyze the cross-market interactions between crude oil prices and wheat prices. We investigate the dynamic relationship between world oil market and wheat market in assumption that the increase of volatility in wheat price is caused by the exogenous crude oil price. To this end, Granger Causality test and kernel Granger Causality test are applied to daily crude oil and wheat prices from January 2000 to June 2013. The linear causality analysis indicates that the oil prices and the wheat prices do not influence each other; this result supports the neutrality hypothesis of Granger causality. In Contrast, the non linear causality analysis proves the existence of non linear feedbacks between oil and wheat markets. These findings provide information for better understanding the recent dynamics of wheat market. Thus, the interdependence between wheat and oil markets is mainly explained by production cost, transportation cost, Biofuel markets and speculation.
This paper analyzes the cyclical behavior of Dow Jones by testing the existence of long memory th... more This paper analyzes the cyclical behavior of Dow Jones by testing the existence of long memory through a new class of semiparametric ARFIMA models with HYGARCH errors (SEMIFARMA-HYGARCH); this class includes nonparametric deterministic trend, stochastic trend, short-range and long-range dependence and long memory heteroscedastic errors. We study the daily returns of the Dow Jones from 1896 to 2006. We estimate several models and we find that the coefficients of the SEMIFARMA-HYGARCH model, including long memory coefficients for the equations of the mean and the conditional variance, are highly significant. The forecasting results show that the informational shocks have permanent effects on volatility and the SEMIFARMA-HYGARCH model has better performance over some other models for long and/or short horizons. The predictions from this model are also better than the predictions of the random walk model; accordingly, the weak efficiency assumption of financial markets seems violated for Dow Jones returns studied over a long period.
Resume: Nous etudions la puissance en terme de prevision des processus bases sur la methode du no... more Resume: Nous etudions la puissance en terme de prevision des processus bases sur la methode du noyau en utilisant la version non parametrique du critere « Final Prediction error » pour identifier un processus fonctionnel heteroscedastique. Cette identification necessite une selection rigoureuse des coefficients de Markov et du choix de la fenetre qui determine le degre de lissage de l’estimateur. Cette approche est comparee avec les resultats de l’estimation de modeles integres fractionnaires. Abstract: We study the forecast’s power of the nonparametric processes based on the kernel method by using the nonparametric version of Final Prediction error criterion to identify a heteroscedastic functional process. This identification requires the selection of the Markov coefficients and the choice of bandwidth, which determines the degree of the estimator’s smoothing. This approach is compared with the estimation results of the fractional integrated models.
Cet article analyse le comportement cyclique du cours du Dow Jones et notamment ses propriétés de... more Cet article analyse le comportement cyclique du cours du Dow Jones et notamment ses propriétés de mémoire longue à travers une nouvelle classe de modèles ARFIMA semiparamétriques avec erreurs GARCH hyperboliques, notée SEMIFARMA-HYGARCH ; cette classe inclut une tendance déterministe non paramétrique, une tendance stochastique, la dépendance à court et à long terme ainsi que le terme d’erreur hétéroscédastique à
Small-cap stocks are characterized by high volatility and offer investors the opportunity to earn... more Small-cap stocks are characterized by high volatility and offer investors the opportunity to earn higher returns. This paper empirically investigates the impact of the day-of-the-week and the month-of-the year effects on the volatility of daily and monthly CAC SMALL returns in Paris stock market during the period from 1999 to 2015. We propose the SEMIFARMA-SD-GJR-GARCH model, which incorporates stochastic trend, deterministic nonparametric trend, short-range, long-range dependence and seasonal dummy asymmetric GARCH errors. The main findings of this study are that the coefficients of the SEMIFARMA-SD-GJR-GARCH model including the long memory coefficient in the mean equation and the seasonal asymmetry in the variance equation are highly significant and the GJR-GARCH model without seasonal dummies is dominated by the GJR-GARCH model with seasonal dummies (SD-GJR-GARCH). The results indicate that the day-of-the-week and the month-of-the-year effects detected on volatility seem to improve the volatility forecasts. These results support the arbitrage opportunity hypothesis for realizing abnormal returns, and support the inefficiency of CAC small capital market.
This paper analyzes the cyclical behavior of Dow Jones by testing the existence of long memory th... more This paper analyzes the cyclical behavior of Dow Jones by testing the existence of long memory through a new class of semiparametric ARFIMA models with HYGARCH errors (SEMIFARMA-HYGARCH); this class includes nonparametric deterministic trend, stochastic trend, short-range and long-range dependence and long memory heteroscedastic errors. We study the daily returns of the Dow Jones from 1896 to 2006. We estimate several models and we find that the coefficients of the SEMIFARMA-HYGARCH model, including long memory coefficients for the equations of the mean and the conditional variance, are highly significant. The forecasting results show that the informational shocks have permanent effects on volatility and the SEMIFARMA-HYGARCH model has better performance over some other models for long and/or short horizons. The predictions from this model are also better than the predictions of the random walk model; accordingly, the weak efficiency assumption of financial markets seems violated fo...
This paper analyses cyclical behaviour of Orange stock price listed in French stock exchange over... more This paper analyses cyclical behaviour of Orange stock price listed in French stock exchange over 01/03/2000 to 02/02/2017 by testing the nonlinearities through a class of conditional heteroscedastic nonparametric models. The linearity and Gaussianity assumptions are rejected for Orange Stock returns and informational shocks have transitory effects on returns and volatility. The forecasting results show that Orange stock prices are short-term predictable and nonparametric NAR-ARCH model has better performance over parametric MA-APARCH model for short horizons. Plus, the estimates of this model are also better comparing to the predictions of the random walk model. This finding provides evidence for weak form of inefficiency in Paris stock market with limited rationality, thus it emerges arbitrage opportunities.
This paper tests the conditional and non-conditional versions of the Capital Asset Pricing Model ... more This paper tests the conditional and non-conditional versions of the Capital Asset Pricing Model (CAPM) in Gulf Cooperation Council GCC capital markets-wide equity sectors upon daily data during the period from February 22ed 2007 to February 22ed 2012. In the empirical analysis, we used Generalized Autoregressive Conditional heteroscedasticity (GARCH) models with CAPM. Main findings seem to show that the CAPM-EGARCH (1.1) appears more advantages than the traditional CAPM at the sectors considered in this study. This approach can be improved and developed in order to be widely applied as this model takes into account shocks, especially in the crisis period where volatilities are very high. Contribution/ Originality: This study contributes to the literature on the cost of equity capital. It uses a new estimation methodology for beta in a highly volatile environment. It proposes a model taking into account the period of crisis and high volatility of returns. This study is one of the very few studies that have investigated the conditional beta heterogeneity of variance at the level of industries in the GCC stock exchanges.
The aim of this paper is to analyze the cross-market interactions between crude oil prices and wh... more The aim of this paper is to analyze the cross-market interactions between crude oil prices and wheat prices. We investigate the dynamic relationship between world oil market and wheat market in assumption that the increase of volatility in wheat price is caused by the exogenous crude oil price. To this end, Granger Causality test and kernel Granger Causality test are applied to daily crude oil and wheat prices from January 2000 to June 2013. The linear causality analysis indicates that the oil prices and the wheat prices do not influence each other; this result supports the neutrality hypothesis of Granger causality. In Contrast, the non linear causality analysis proves the existence of non linear feedbacks between oil and wheat markets. These findings provide information for better understanding the recent dynamics of wheat market. Thus, the interdependence between wheat and oil markets is mainly explained by production cost, transportation cost, Biofuel markets and speculation.
This paper analyzes the cyclical behavior of Dow Jones by testing the existence of long memory th... more This paper analyzes the cyclical behavior of Dow Jones by testing the existence of long memory through a new class of semiparametric ARFIMA models with HYGARCH errors (SEMIFARMA-HYGARCH); this class includes nonparametric deterministic trend, stochastic trend, short-range and long-range dependence and long memory heteroscedastic errors. We study the daily returns of the Dow Jones from 1896 to 2006. We estimate several models and we find that the coefficients of the SEMIFARMA-HYGARCH model, including long memory coefficients for the equations of the mean and the conditional variance, are highly significant. The forecasting results show that the informational shocks have permanent effects on volatility and the SEMIFARMA-HYGARCH model has better performance over some other models for long and/or short horizons. The predictions from this model are also better than the predictions of the random walk model; accordingly, the weak efficiency assumption of financial markets seems violated for Dow Jones returns studied over a long period.
Resume: Nous etudions la puissance en terme de prevision des processus bases sur la methode du no... more Resume: Nous etudions la puissance en terme de prevision des processus bases sur la methode du noyau en utilisant la version non parametrique du critere « Final Prediction error » pour identifier un processus fonctionnel heteroscedastique. Cette identification necessite une selection rigoureuse des coefficients de Markov et du choix de la fenetre qui determine le degre de lissage de l’estimateur. Cette approche est comparee avec les resultats de l’estimation de modeles integres fractionnaires. Abstract: We study the forecast’s power of the nonparametric processes based on the kernel method by using the nonparametric version of Final Prediction error criterion to identify a heteroscedastic functional process. This identification requires the selection of the Markov coefficients and the choice of bandwidth, which determines the degree of the estimator’s smoothing. This approach is compared with the estimation results of the fractional integrated models.
Cet article analyse le comportement cyclique du cours du Dow Jones et notamment ses propriétés de... more Cet article analyse le comportement cyclique du cours du Dow Jones et notamment ses propriétés de mémoire longue à travers une nouvelle classe de modèles ARFIMA semiparamétriques avec erreurs GARCH hyperboliques, notée SEMIFARMA-HYGARCH ; cette classe inclut une tendance déterministe non paramétrique, une tendance stochastique, la dépendance à court et à long terme ainsi que le terme d’erreur hétéroscédastique à
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