¿Qué tan buenos son los patrones del igbc para predecir su comportamiento? una aplicación con datos de alta frecuencia

Información del artículo

Resumen

El objetivo del artículo es evaluar la utilidad de patrones de comportamiento para predecir el comportamiento futuro del Índice General de la Bolsa de Colombia (IGBC). Para tal fin, se emplearon 18 diferentes especificaciones del modelo GARCH-M y datos de alta frecuencia. Los modelos considerados tienen en cuenta el efecto Leverage, el efecto Día de la Semana, el efecto Hora y el efecto Día-Hora. Se evalúan 115 pronósticos para los siguientes 10 minutos para cada uno de los 18 modelos, empleando estadísticas descriptivas y las pruebas de Granger y Newbold (1977) y Diebold y Mariano (1995). Se encuentra que la mejor especificación es la que no tiene en cuenta el efecto día-hora en la media ni en la varianza.

Palabras clave:

Intraday

Garch-M

efecto Día de la Semana

efecto Hora

efecto Día-Hora

Abstract

The purpose of this article is to evaluate the usefulness of performance trends for forecasting the future performance of the IGBC (Colombian exchange market index). To this end, 18 different specifications of the GARCH-M model and high frequency data were used. The models in review considered the leverage, day-of-the-week, hour-of-the-day, and day-hour effects. 115 different forecasts for the next 10 minutes were assessed for each of the 18 models, using descriptive statistics and the Granger's and Newbold (1977) and Diebold's and Mariano (1995) tests. The best model was found to be the one that does not consider the day-hour effect on the mean or the variance.

Keywords:

Intraday

Garch-M

day-of-the-week effect

hour-of-the-day effect

and day-hour effect

Resumo

O objetivo do artigo é avaliar a utilidade de padrões de comportamento para prever o comportamento futuro do Índice Geral da Bolsa da Colômbia (IGBC). Para esse fim, se empregaram 18 diferentes especificações do modelo GARCH-M e dados de alta frequência. Os modelos considerados têm em conta o efeito “Leverage” (Avalancagem), o efeito “Dia da Semana”, o efeito “Hora” e o efeito “Dia-Hora. São avaliados 115 prognósticos para os 10 minutos seguintes para cada um dos 18 modelos, empregando estatísticas descritivas e as provas de Granger e Newbold (1977) e Diebold e Mariano (1995). Se verifica que a melhor especificação é aquela que não tem em conta o efeito dia-hora na média nem na variação.

Palavras-chave:

Intraday

Garch-M

efeito Dia da Semana

efeito Hora

efeito Dia-Hora

El Texto completo está disponible en PDF

Referencias Bibliográficas

[1.]

S. Alizadeh, M.W. Brandt, F.X. Diebold.

Range-Based Estimation of Stochastic Volatility Models.

The Journal of Finance, 57 (2002), pp. 1047-1091

[2.]

J.C. Alonso.

The Day-of-The-Week effect: the Colombian exchange and stock market case.

Presentado en el III Simposio Nacional de docentes de finanzas, (2006),

[3.]

J.C. Alonso, M.A. Arcos.

Cuatro hechos estilizados de las series de rendimientos: Una ilustración para Colombia.

Estudios Gerenciales, 22 (2006), pp. 103-123

[4.]

J.C. Alonso, C.I. Patiño.

Evaluación de pronósticos para la tasa de cambio en Colombia.

Estudios Gerenciales, 96 (2005), pp. 13-26

[5.]

Y. Amihud, H. Mendelson.

Trading Mechanisms and Stock Returns: An Empirical Investigation.

The Journal of Finance, 42 (1987), pp. 533-553

[6.]

Y. Amihud, H. Mendelson.

Volatility Efficiency, and Trading: Evidence from the Japanese Stock Market.

The Journal of Finance, 46 (1991), pp. 1765-1789

[7.]

T.G. Andersen, T. Bollerslev.

Intraday periodicity and volatility persistence in financial markets.

Journal of Empirical Finance, 4 (1997), pp. 115-158

[8.]

R.T. Baillie, T. Bollerslev.

Intra-Day and Inter-Market Volatility in Foreign Exchange Rates.

The Review of Economic Studies, 58 (1991), pp. 565-585

[9.]

E. Berndt, B. Hall, R. Hall, J. Hausman.

Estimation and inference in nonlinear structural models.

Annals of Economic and Social Measurement, 3/4 (1974), pp. 653-666

[10.]

H. Berument, H. Kiymaz.

The day of the week effect on stock market volatility and volume: International evidence.

Review of Financial Economics, 12 (2003), pp. 363-380

[11.]

T. Bollerslev.

Generalized autoregressive conditional heteroskedasticity.

Journal of Econometrics, 31 (1986), pp. 307-327

[12.]

M.W. Brandt, C.S. Jones.

Volatility Forecasting with Range-Based EGARCH Models.

Journal of Business y Economic Statistics, 24 (2006), pp. 470-486

[13.]

R.Y. Chou.

Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model.

Journal of Money Credit y Banking, 37 (2005), pp. 561-582

[14.]

P.F. Christoffersen.

Elements of financial risk management.

Academic Press, (2003),

[15.]

K.B. Cyree, D.B. Winters.

An Intraday Examination of the Federal Funds Market: Implications for the Theories of the Reverse-J Pattern.

The Journal of Business, 74 (2001), pp. 535-556

[16.]

F.X. Diebold, R.S. Mariano.

Comparing Predictive Accuracy.

Journal of Business and Economic Statistics, 13 (1995), pp. 253-263

[17.]

W. Enders.

Applied econometric time series.

2nd, J. Wiley, (2004),

[18.]

R.F. Engle.

Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation.

Econométrica, 50 (1982), pp. 987-1007

[19.]

R.F. Engle, T. Bollerslev.

Modeling the persistence of conditional variances.

Econometric Reviews, 5 (1986), pp. 1-50

[20.]

M.R. Gibbons, P. Hess.

Day of the Week Effects and Asset Returns.

The Journal of Business, 54 (1981), pp. 579-596

[21.]

P. Giot.

Market risk models for intraday data.

European Journal of Finance, 11 (2005), pp. 309-324

[22.]

L. Glosten, R. Jagannathan, D.E. Runkle.

On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks.

Journal of Finance, 48 (1993), pp. 17798-21801

[23.]

C.W.J. Granger, P. Newbold.

Forecasting economic time series.

Academic Press, (1977),

[24.]

B.D. Grundy, M. McNichols.

Trade and the Revelation of Information through Prices and Direct Disclosure.

The Review of Financial Studies, 2 (1989), pp. 495-526

[25.]

H. Hong, W. Jiang.

Trading and Returns under Periodic Market Closures.

Journal of Finance, 55 (2000), pp. 297-354

[26.]

J.E. Jarrett, E. Kyper.

Capital market efficiency and the predictability of daily returns.

Applied Economics, 38 (2006), pp. 631-636

[27.]

S.P. Keef, M.L. Roush.

Day-of-the-week effects in the pre-holiday returns of the Standard y Poor's 500 stock index.

Applied Financial Economics, 15 (2005), pp. 107-119

[28.]

D.B. Keim, R.F. Stambaugh.

A Further Investigation of the Weekend Effect in Stock Returns.

The Journal of Finance, 39 (1984), pp. 819-835

[29.]

J. Lakonishok, M. Levi.

Weekend Effects on Stock Returns: A Note.

The Journal of Finance, 37 (1982), pp. 883-889

[30.]

J. Leach, A. Madhavan.

Price experimentation and security market structure.

Review of Financial Studies, 6 (1993), pp. 375-404

[31.]

R.J. Rogalski.

New Findings Regarding Day-of-the-Week Returns over Trading and Non-Trading Periods: A Note.

The Journal of Finance, 39 (1984), pp. 1603-1614

[32.]

L.C.G. Rogers, S.E. Satchell.

Estimating Variance From High Low and Closing Prices.

The Annals of Applied Probability, 1 (1991), pp. 504-512

[33.]

D. Romer.

Rational Asset-Price Movements Without News.

The American Economic Review, 83 (1993), pp. 1112-1130

[34.]

H. Stoll, R. Whaley.

Stock market structure and volatility.

Review of Financial Studies, 3 (1990), pp. 37-71.35

Indexada en:

Síguenos:

Indexada en:

Síguenos:

Suscríbase a la newsletter