Abstract
This article investigates the use of AR models with stochastic coefficients to describe the changes in volatility observed in time series of financial returns. Such models can reproduce the main stylised facts observed in financial series: excess kurtosis, serial correlated square returns and time-varying conditional variance. We first cast the model in a state space form. Then the EM algorithm is used to estimate the parameters of the model. With the state-space formulation one can use the Kalman filter to evaluate the conditional variance of future returns. The model is tested using daily returns of TELEBRÁS-PN, one of the main stocks of the brazilian market.
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© 1998 Springer Science+Business Media Dordrecht
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Veiga, Á., Medeiros, M.C., Fernandes, C. (1998). State Space Arch: Forecasting Volatility with a Stochastic Coefficient Model. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_21
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DOI: https://doi.org/10.1007/978-1-4615-5625-1_21
Publisher Name: Springer, Boston, MA
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