KAJIAN MODEL HIDDEN MARKOV UNTUK MENDUGA VOLATILITAS INDEKS HARGA SAHAM

Authors

DOI:

https://doi.org/10.31000/prima.v5i2.1476

Abstract

Abstrak

Volatility is a measure of uncertainty. Volatility can either be measured by using the standard deviation or variance between returns. The problem is volatility is unobservable, and estimating volatility is not a trivial task. Therefore, it needs daily volatility proxy as a benchmark in calculating error. This research used daily volatility proxy proposed by Alizadeh, Brandt, dan Diebolt (2002). Hidden Markov model is used for estimating volatility proposed by Rossi and Gallo (2006). Forecasting volatility for LQ45 index using the model performs well. This is indicated by SMAPE (Symmetric Mean Absolute Percentage Error) about 13.62%.

Kata kunci: volatility, hidden Markov model, forecasting

References

Alizadeh, S., Brandt, M. W., and Diebold, F. X., 1999. Range-based estimator of stochastic volatility models. Working paper, University of Pennsylvania.

Berndt, Ernst K., Bronwyn H. Hall, Robert E. Hall, and Jerry A. Hausman 1974.Estimation and Inference in Nonlinear Structural Models. Annals of Economic and Social Measurement 4, 653-665.

Bollerslev T. 1987. A conditionally heteroskedastic time series model for speculative prices and rates of return. Review of Economics & Statistics 69, 542-547.

Cvitanic J., Liptser R.S., dan Rozovskii B.L. 2005. A Filtering approach to tracking volatility from prices observed at random times.The Annals of Applied Probability 16, No.3, 1633-1652.

Hamilton J.D. 1994.Time Series Analysis. Princeton University Press.

Hamilton J.D., dan Susmel R. 1994. Autorgressive conditional heteroskedasticity and changes in regimes, Journal of Econometrics 64, 307-333.

Lamoureux C.G., dan Lastrapes W.D. 1990. Persistence in variance, structural changes, and the GARCH model, Journal of Business and Economic Statistics 8, No. 2, 226-234.

Rossi A. dan Gallo G. 2006. Volatility estimation via hidden Markov models, Journal of Empirical Finance, 13, 203-230.

Ruppert D. 2011. Statistics and data analysis for finance engineering. Springer.

Downloads

Published

2019-03-30

Issue

Vol. 5 No. 2 (2016)

Section

Articles

License

Authors who publish with Prima: Jurnal Pendidikan Matematika agree to the following terms: Authors retain copyright and grant the Prima: Jurnal Pendidikan Matematika right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) the work for any purpose, even commercially with an acknowledgement of the work's authorship and initial publication in Prima: Jurnal Pendidikan Matematika. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in Prima: Jurnal Pendidikan Matematika. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).