JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A Study for Forecasting Methods of ARMA-GARCH Model Using MCMC Approach
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Study for Forecasting Methods of ARMA-GARCH Model Using MCMC Approach
Chae, Wha-Yeon; Choi, Bo-Seung; Kim, Kee-Whan; Park, You-Sung;
  PDF(new window)
 Abstract
The volatility is one of most important parameters in the areas of pricing of financial derivatives an measuring risks arising from a sudden change of economic circumstance. We propose a Bayesian approach to estimate the volatility varying with time under a linear model with ARMA(p, q)-GARCH(r, s) errors. This Bayesian estimate of the volatility is compared with the ML estimate. We also present the probability of existence of the unit root in the GARCH model.
 Keywords
Volatility;GARCH model;Bayesian inference;MCMC;
 Language
Korean
 Cited by
 References
1.
김우환 (2011). GARCH_ARJI 모형을 활용한 KOSPI 수익률의 변동성에 관한 실증분석, <응용통계연구>, 24, 78-81.

2.
박만식, 김나영, 김희영 (2008). 이분산 시계열모형을 이용한 국내주식자료의 군집분석, <한국통계학회논문집>, 15, 925-937. crossref(new window)

3.
박유성, 송석헌 (1998). <경영.경제자료분석>, 정일출판사, 서울.

4.
홍선영, 최성미, 박진아, 백지선, 황선영 (2009). 지속-변동성을 가진 비대칭 TARCH 모형을 이용한 국내금융시계열 분석, <한국통계학회논문집>, 16, 605-614.

5.
Albert, J. and Chib, S. (1993). Bayesian inference for autoregressive time series with mean and variance subject to Markov jumps, Journal of Business and Economic Statistics, 11, 1-15. crossref(new window)

6.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327. crossref(new window)

7.
Chib, S. and Greenberg, E. (1994). Bayesian inference in regression modes with ARMA(p, q) errors, Journal of Econometrics, 64, 183-206. crossref(new window)

8.
Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom in ation, Econometrica, 50, 987-1008. crossref(new window)

9.
Engle, R. F. and Bollerslev, T. (1986). Modeling the persistence of conditional variances, Econometric Reviews, 5, 1-50. crossref(new window)

10.
Engle, R. F., Lillien, D. M. and Robin, R. P. (1987). Estimating time varying risk premia in the term structure: The ARMA-M model, Econometrica, 55, 391-408. crossref(new window)

11.
Gelfand, A. E. and Smith, A. F. M. (1990). Sampling-based approachs to calculating marginal densities, Journal of the American Statistical Association, 85, 398-409. crossref(new window)

12.
Hamilton, J. D. (1994). Time Series Analisis, Princeton University Press, Princeton, New York.

13.
Hastings, W. K. (1970). Monte Carlo sampling methods using Markov Chains and their applications, Biometrika, 57, 97-109. crossref(new window)

14.
Jacquier, E., Polson, N. G. and Rossi, P. E. (1994). Bayesian analisis of stochastic volatiliy models(with discussion), Journal of Business & Economic Statistics, 12, 371-417. crossref(new window)

15.
Kleibergen, F. and Van Dijk, H. K. (1993). Non-stationarity in GARCH models: A Bayesian analisis, Journal of Applied Econometrics, 8, S41-S61. crossref(new window)

16.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. and Teller, E. (1953). Equations of state calculations by fast computing machines, Journal of Chemical Physics, 21, 1087-1092. crossref(new window)

17.
Muller, P. and Pole, A. (1995). Monte Carlo Posterior Intergration in GARCH Models, Manuscript, Duke University.

18.
Nakatsuma, T. (2000). Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach, Journal of Econometrics, 95, 57-69. crossref(new window)

19.
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 59, 347-370. crossref(new window)

20.
Tierney, L. (1994). Markov Chains for exploring posterior distributions(with discussion), Annals of Statistics, 22, 1701-1762. crossref(new window)

21.
Tsay, R. S. (2002). Analysis of Financial Time Series, Wiley Series in Probability and Statistics.