Volatilidade do mercado de ações do Paquistão: uma comparação de modelos do tipo Garch com cinco
Palavras-chave:
Volatilidade, mercado de ações, modelo GARCH, investidor, econômicoResumo
Este estudo realiza análises empíricas modelando a volatilidade do mercado de ações paquistanês no período de 1º de janeiro de 2008 a 30 de junho de 2018 através de diferentes modelos do tipo GARCH; Simétrico (GARCH & GARCH-M) e Assimétrico (EGARCH & TGARCH) com cinco diferentes Técnicas de Distribuição, como Distribuição Normal (Norm), Distribuição t de Student (Padrão), Distribuição de Erro Generalizada (GED), Distribuição t de Student com correção do grau de liberdade (Std. com correção de DOF) e distribuição de erros generalizada com parâmetros de correção (GED com parâmetros de correção). Os resultados são apresentados na variância condicional defasada GARCH (1, 1) e na perturbação quadrada que afeta a variância condicional em todas as distribuições. GARCH-M (1, 1) representa um significante positivo com resultados de 1% em Std. e GED que indica a existência de prêmio de risco e insignificante em resto da distribuição em. EGARCH e TGARCH ambos são encontrados para alavancar o efeito significativo ao nível de 1%. Ao determinar a precisão e a adequação da densidade de previsão e a escolha do modelo de volatilidade, os resultados em dados simulados indicam que a escolha da distribuição condicional aparece como um fator mais dominante. O modelo EGARCH com Student t a técnica de distribuição apresenta resultados satisfatórios quando comparado a outros modelos que foram censurados por ferramentas estatísticas de máxima Likelihood, mínima AIC e SIC. O estudo anterior do mercado de ações paquistanês é limitado a modelos de família GARCH com uma ou duas distribuições. Este estudo cobre as limitações e também contribui com a literatura existente a esse respeito. Esta pesquisa é considerada importante para investidores, formuladores de políticas e pesquisadores.
Downloads
Não há dados estatísticos.
Referências
Ahmed, A. E. M., & Suliman, S. Z. (2011). Modeling stock market volatility using GARCH models evidence from Sudan. International Journal of Business and Social Science, 2(23).
Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723.
Akhtar, S., & Khan, N. (2016). Modelling volatility on Karachi Stock Exchange, Pakistan. Journal of Asia Business Studies. 1-34.
Alberg, D., Shalit, H., & Yosef, R. (2008). Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics, 18(15), 1201-1208.
Ali Ahmed, H. J., Hassan, T., & Nasir, A. (2005). The relationship between trading volume, volatility and stock market returns: a test of mixed distribution hypothesis for a pre-and post crisis on Kuala Lumpur Stock Exchange. Investment Management and Financial Innovations, 2(3), 146-158.
Asemota, O. J., & Ekejiuba, U. C. (2017). An Application of Asymmetric GARCH Models on Volatility of Banks Equity in Nigeria’s Stock Market. CBN Journal of Applied Statistics. 8 (1), 73-99.
Azzalini, A. (1985). A class of distributions which includes the normal ones. Scandinavian journal of statistics, 171-178.
Banumathy, K., & Azhagaiah, R. (2015). Modelling Stock Market Volatility: Evidence from India. Managing Global Transitions: International Research Journal, 13(1), 28-42.
Black, F. (1976). Studies of stock price volatility of changes. American Statistical Association Journal. 177-181.
Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52(3), 345-370.
Bucevska, V. (2013). An Empirical evaluation of GARCH models in value-at-risk estimation: Evidence from the Macedonian stock exchange. Business systems research journal: international journal of the Society for Advancing Business & Information Technology (BIT), 4(1), 49-64.
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: understanding AIC and BIC in model selection. Sociological methods & research, 33(2), 261-304.
Chan, S., Chu, J., Nadarajah, S., & Osterrieder, J. (2017). A statistical analysis of cryptocurrencies. Journal of Risk and Financial Management, 10(2), 12..
Cheteni, P. (2016). Stock market volatility using GARCH models: Evidence from South Africa and China stock markets. 4-12.
Ding, Z., Granger, C. W., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of empirical finance, 1(1), 83-106.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50 (4), 987-1007.
Engle, R. F., & Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric reviews, 5(1), 1-50.
Engle, R. F., & Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The journal of finance, 48(5), 1749-1778.
Engle, R. F., Lilien, D. M., & Robins, R. P. (1987). Estimating time varying risk premia in the term structure: The ARCH-M model. Econometrica: journal of the Econometric Society, 391-407.
Fang, J., Nakamura, H., & Maeda, H. (2011). The EPR effect: unique features of tumor blood vessels for drug delivery, factors involved, and limitations and augmentation of the effect. Advanced drug delivery reviews, 63(3), 136-151.
Fernández, C., & Steel, M. F. (1998). On Bayesian modeling of fat tails and skewness. Journal of the American Statistical Association, 93(441), 359-371.
Floros, C. (2008). Modelling volatility using GARCH models: evidence from Egypt and Israel. Middle Eastern Finance and Economics, (2), 31-41.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801.
Gosset, W. S. (1908). Probable error of a correlation coefficient. Biometrika, 6(2-3), 302-310.
Goudarzi, H., & Ramanarayanan, C. S. (2010). Modeling and estimation of volatility in the Indian stock market. International Journal of Business and Management, 5(2), 85.
Hameed, A., Ashraf, H., & Siddiqui, R. (2006). Stock Market Volatility and Weak-form Efficiency: Evidence from an Emerging Market [with Comments]. The Pakistan Development Review, 1029-1040.
Hannan, E. J., & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society. Series B (Methodological), 190-195.
Hassan, H. E., Mercer, S. L., Cunningham, C. W., Coop, A., & Eddington, N. D. (2009). Evaluation of the P-glycoprotein (Abcb1) affinity status of a series of morphine analogs: comparative study with meperidine analogs to identify opioids with minimal P-glycoprotein interactions. International journal of pharmaceutics, 375(1-2), 48-54.
Higgins, M. L., & Bera, A. K. (1992). A class of nonlinear ARCH models. International Economic Review, 137-158.
Hung , N. T. (2018). An empirical analysis of Euro Hungarian Forint exchange rate volatility using GARCH. Challenges in National and International Economic Policies. 57–67.
Jarque, C. M., & Bera, A. K. (1987). A test for normality of observations and regression residuals. International Statistical Review/Revue Internationale de Statistique, 163-172.
Jyothi , U., & Suresh, K. (2014). Estimating Stock Market Volatility Using Non-linear Models . IOSR Journal of Business and Management (IOSR-JBM). 62-65.
Kanasro, H. A., Rohra, C. L., & Junejo, M. A. (2009). Measurement of stock market volatility through ARCH and GARCH models: a case study of Karachi stock exchange. Australian Journal of Basic and Applied Sciences, 3(4), 3123-3127.
Karmakar, M. (2005). Modeling conditional volatility of the Indian stock markets. Vikalpa, 30(3), 21-38.
Khan, N. (2011). Dividend policy and the stock market reaction to dividend announcements in Pakistan (Doctoral dissertation, University of Dundee).
Kumar , H. P., & Patil , B. S. (August 2016 ). Volatility Forecasting- A Performance Measure of GARCH Techniques with Different Distribution Models. International Journal of Soft Computing, Mathematics and Control (IJSCMC), 5(No), 1-13.
Lim, C. M., & Sek, S. K. (2013). Comparing the performances of GARCH-type models in capturing the stock market volatility in Malaysia. Procedia Economics and Finance, 5, 478-487.
Ling, S., & McAleer, M. (2003). Asymptotic theory for a vector ARMA-GARCH model. Econometric theory, 19(2), 280-310.
Mwita, P. N., & Nassiuma, D. K. (2015). Volatility estimation of stock prices using Garch method. Kabarak Journal of Research & Innovation, 3(1), 48-53.
Najjar, M., Saleh, D., Zelic, M., Nogusa, S., Shah, S., Tai, A., ... & Whalen, M. J. (2016). RIPK1 and RIPK3 kinases promote cell-death-independent inflammation by Toll-like receptor 4. Immunity, 45(1), 46-59.
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
Olbrys, J. (2013). Price and volatility spillovers in the case of stock markets located in different time zones. Emerging Markets Finance and Trade, 49(sup2), 145-157.
Omorogbe, J. A., Mc Nally, M., Cretu, I., & O'Connor, A. (2017). Mo1181 Gastric Intestinal Metaplasia Outcomes: Results From a 17 Year Tertiary Centre Upper GI Surveillance Programme in Ireland. Gastrointestinal Endoscopy, 85(5), AB452-AB453.
Peters, J. P. (2001). Estimating and forecasting volatility of stock indices using asymmetric GARCH models and (Skewed) Student-t densities. Preprint, University of Liege, Belgium, 3, 19-34.
Poon, S. H., & Granger, C. W. (2003). Forecasting volatility in financial markets: A review. Journal of economic literature, 41(2), 478-539.
Saeed, A. F., Wang, R., Ling, S., & Wang, S. (2017). Antibody engineering for pursuing a healthier future. Frontiers in microbiology, 8, 495.
Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 6(2), 461-464.
Shah, A., Shahf, A. H., Parveen, N., Rehman, Z. U., Khan, S. Z., Rana, U. A., ... & Qureshi, R. (2016). Synthesis and electrochemical investigations of piperazines. Electrochimica Acta, 220, 705-711.
Stoyanov, J. V., Gantenbein-Ritter, B., Bertolo, A., Aebli, N., Baur, M., Alini, M., & Grad, S. (2011). Role of hypoxia and growth and differentiation factor-5 on differentiation of human mesenchymal stem cells towards intervertebral nucleus pulposus-like cells. Eur Cell Mater, 21(533), e47.
Taylor, S. J. (2008). Modelling financial time series. world scientific.
Theodossiou, P. (1998). Financial data and the skewed generalized t distribution. Management Science, 44(12-part-1), 1650-1661.
Vijayalakshmi, S., & Gaur, S. (2013). Modelling volatility: Indian stock and foreign exchange markets. Journal of Emerging Issues in Economics, Finance and Banking, 2(1), 583-98.
Wennström, A. (2014). Volatility Forecasting Performance: Evaluation of GARCH type volatility models on Nordic equity indices.
Wilhelmsson, A. (2006). Garch Forecasting Performance under Different Distribution Assumptions. Journal of Forecasting, 25, 561–578.
Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and control, 18(5), 931-955.
Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723.
Akhtar, S., & Khan, N. (2016). Modelling volatility on Karachi Stock Exchange, Pakistan. Journal of Asia Business Studies. 1-34.
Alberg, D., Shalit, H., & Yosef, R. (2008). Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics, 18(15), 1201-1208.
Ali Ahmed, H. J., Hassan, T., & Nasir, A. (2005). The relationship between trading volume, volatility and stock market returns: a test of mixed distribution hypothesis for a pre-and post crisis on Kuala Lumpur Stock Exchange. Investment Management and Financial Innovations, 2(3), 146-158.
Asemota, O. J., & Ekejiuba, U. C. (2017). An Application of Asymmetric GARCH Models on Volatility of Banks Equity in Nigeria’s Stock Market. CBN Journal of Applied Statistics. 8 (1), 73-99.
Azzalini, A. (1985). A class of distributions which includes the normal ones. Scandinavian journal of statistics, 171-178.
Banumathy, K., & Azhagaiah, R. (2015). Modelling Stock Market Volatility: Evidence from India. Managing Global Transitions: International Research Journal, 13(1), 28-42.
Black, F. (1976). Studies of stock price volatility of changes. American Statistical Association Journal. 177-181.
Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52(3), 345-370.
Bucevska, V. (2013). An Empirical evaluation of GARCH models in value-at-risk estimation: Evidence from the Macedonian stock exchange. Business systems research journal: international journal of the Society for Advancing Business & Information Technology (BIT), 4(1), 49-64.
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: understanding AIC and BIC in model selection. Sociological methods & research, 33(2), 261-304.
Chan, S., Chu, J., Nadarajah, S., & Osterrieder, J. (2017). A statistical analysis of cryptocurrencies. Journal of Risk and Financial Management, 10(2), 12..
Cheteni, P. (2016). Stock market volatility using GARCH models: Evidence from South Africa and China stock markets. 4-12.
Ding, Z., Granger, C. W., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of empirical finance, 1(1), 83-106.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50 (4), 987-1007.
Engle, R. F., & Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric reviews, 5(1), 1-50.
Engle, R. F., & Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The journal of finance, 48(5), 1749-1778.
Engle, R. F., Lilien, D. M., & Robins, R. P. (1987). Estimating time varying risk premia in the term structure: The ARCH-M model. Econometrica: journal of the Econometric Society, 391-407.
Fang, J., Nakamura, H., & Maeda, H. (2011). The EPR effect: unique features of tumor blood vessels for drug delivery, factors involved, and limitations and augmentation of the effect. Advanced drug delivery reviews, 63(3), 136-151.
Fernández, C., & Steel, M. F. (1998). On Bayesian modeling of fat tails and skewness. Journal of the American Statistical Association, 93(441), 359-371.
Floros, C. (2008). Modelling volatility using GARCH models: evidence from Egypt and Israel. Middle Eastern Finance and Economics, (2), 31-41.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801.
Gosset, W. S. (1908). Probable error of a correlation coefficient. Biometrika, 6(2-3), 302-310.
Goudarzi, H., & Ramanarayanan, C. S. (2010). Modeling and estimation of volatility in the Indian stock market. International Journal of Business and Management, 5(2), 85.
Hameed, A., Ashraf, H., & Siddiqui, R. (2006). Stock Market Volatility and Weak-form Efficiency: Evidence from an Emerging Market [with Comments]. The Pakistan Development Review, 1029-1040.
Hannan, E. J., & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society. Series B (Methodological), 190-195.
Hassan, H. E., Mercer, S. L., Cunningham, C. W., Coop, A., & Eddington, N. D. (2009). Evaluation of the P-glycoprotein (Abcb1) affinity status of a series of morphine analogs: comparative study with meperidine analogs to identify opioids with minimal P-glycoprotein interactions. International journal of pharmaceutics, 375(1-2), 48-54.
Higgins, M. L., & Bera, A. K. (1992). A class of nonlinear ARCH models. International Economic Review, 137-158.
Hung , N. T. (2018). An empirical analysis of Euro Hungarian Forint exchange rate volatility using GARCH. Challenges in National and International Economic Policies. 57–67.
Jarque, C. M., & Bera, A. K. (1987). A test for normality of observations and regression residuals. International Statistical Review/Revue Internationale de Statistique, 163-172.
Jyothi , U., & Suresh, K. (2014). Estimating Stock Market Volatility Using Non-linear Models . IOSR Journal of Business and Management (IOSR-JBM). 62-65.
Kanasro, H. A., Rohra, C. L., & Junejo, M. A. (2009). Measurement of stock market volatility through ARCH and GARCH models: a case study of Karachi stock exchange. Australian Journal of Basic and Applied Sciences, 3(4), 3123-3127.
Karmakar, M. (2005). Modeling conditional volatility of the Indian stock markets. Vikalpa, 30(3), 21-38.
Khan, N. (2011). Dividend policy and the stock market reaction to dividend announcements in Pakistan (Doctoral dissertation, University of Dundee).
Kumar , H. P., & Patil , B. S. (August 2016 ). Volatility Forecasting- A Performance Measure of GARCH Techniques with Different Distribution Models. International Journal of Soft Computing, Mathematics and Control (IJSCMC), 5(No), 1-13.
Lim, C. M., & Sek, S. K. (2013). Comparing the performances of GARCH-type models in capturing the stock market volatility in Malaysia. Procedia Economics and Finance, 5, 478-487.
Ling, S., & McAleer, M. (2003). Asymptotic theory for a vector ARMA-GARCH model. Econometric theory, 19(2), 280-310.
Mwita, P. N., & Nassiuma, D. K. (2015). Volatility estimation of stock prices using Garch method. Kabarak Journal of Research & Innovation, 3(1), 48-53.
Najjar, M., Saleh, D., Zelic, M., Nogusa, S., Shah, S., Tai, A., ... & Whalen, M. J. (2016). RIPK1 and RIPK3 kinases promote cell-death-independent inflammation by Toll-like receptor 4. Immunity, 45(1), 46-59.
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
Olbrys, J. (2013). Price and volatility spillovers in the case of stock markets located in different time zones. Emerging Markets Finance and Trade, 49(sup2), 145-157.
Omorogbe, J. A., Mc Nally, M., Cretu, I., & O'Connor, A. (2017). Mo1181 Gastric Intestinal Metaplasia Outcomes: Results From a 17 Year Tertiary Centre Upper GI Surveillance Programme in Ireland. Gastrointestinal Endoscopy, 85(5), AB452-AB453.
Peters, J. P. (2001). Estimating and forecasting volatility of stock indices using asymmetric GARCH models and (Skewed) Student-t densities. Preprint, University of Liege, Belgium, 3, 19-34.
Poon, S. H., & Granger, C. W. (2003). Forecasting volatility in financial markets: A review. Journal of economic literature, 41(2), 478-539.
Saeed, A. F., Wang, R., Ling, S., & Wang, S. (2017). Antibody engineering for pursuing a healthier future. Frontiers in microbiology, 8, 495.
Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 6(2), 461-464.
Shah, A., Shahf, A. H., Parveen, N., Rehman, Z. U., Khan, S. Z., Rana, U. A., ... & Qureshi, R. (2016). Synthesis and electrochemical investigations of piperazines. Electrochimica Acta, 220, 705-711.
Stoyanov, J. V., Gantenbein-Ritter, B., Bertolo, A., Aebli, N., Baur, M., Alini, M., & Grad, S. (2011). Role of hypoxia and growth and differentiation factor-5 on differentiation of human mesenchymal stem cells towards intervertebral nucleus pulposus-like cells. Eur Cell Mater, 21(533), e47.
Taylor, S. J. (2008). Modelling financial time series. world scientific.
Theodossiou, P. (1998). Financial data and the skewed generalized t distribution. Management Science, 44(12-part-1), 1650-1661.
Vijayalakshmi, S., & Gaur, S. (2013). Modelling volatility: Indian stock and foreign exchange markets. Journal of Emerging Issues in Economics, Finance and Banking, 2(1), 583-98.
Wennström, A. (2014). Volatility Forecasting Performance: Evaluation of GARCH type volatility models on Nordic equity indices.
Wilhelmsson, A. (2006). Garch Forecasting Performance under Different Distribution Assumptions. Journal of Forecasting, 25, 561–578.
Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and control, 18(5), 931-955.
Downloads
Publicado
2018-12-27
Como Citar
Naseem, S., fu, G. lei, Mohsin, M., Zia-ur-Rehman, M., & Baig, S. A. (2018). Volatilidade do mercado de ações do Paquistão: uma comparação de modelos do tipo Garch com cinco. Amazonia Investiga, 7(17), 486–504. Recuperado de https://amazoniainvestiga.info/index.php/amazonia/article/view/763
Edição
Seção
Articles
.gif)
