KAJIAN MODEL HIDDEN MARKOV UNTUK MENDUGA VOLATILITAS INDEKS HARGA SAHAM

Penulis

DOI:

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

Abstrak

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

Referensi

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

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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.

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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.

Unduhan

Diterbitkan

2019-03-30

Terbitan

Vol 5 No 2 (2016)

Bagian

Articles

Lisensi

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