RCAR and CHARMA Models Versus Stochastic Volatility Models: A Forecasting Analysis Using Macrofinancial Data

35 Pages Posted: 28 Jul 2018

See all articles by Stefanos Dimitrakopoulos

Stefanos Dimitrakopoulos

University of Leeds - Leeds University Business School (LUBS)

Efthymios G. Tsionas

Lancaster University

Date Written: June 6, 2018

Abstract

Using inflation and return time series, we first evaluate the forecasting performance of two classes of conditional heteroscedastic models: the random coefficient autoregressive (RCAR) models and the conditional heteroscedastic autoregressive moving average (CHARMA) models. Markov Chain Monte Carlo schemes are developed for the estimation of the model parameters. Furthermore, the forecasting ability of these two models is compared against that of several stochastic volatility models that control for time-varying parameters, in mean effects, leverage effects and moving average errors. We found that our proposed models can produce better point and density forecasts than various stochastic volatility specifications, with the CHARMA models outperforming the RCAR models.

Suggested Citation

Dimitrakopoulos, Stefanos and Tsionas, Efthymios G., RCAR and CHARMA Models Versus Stochastic Volatility Models: A Forecasting Analysis Using Macrofinancial Data (June 6, 2018). Available at SSRN: https://ssrn.com/abstract=3210081 or http://dx.doi.org/10.2139/ssrn.3210081

Stefanos Dimitrakopoulos (Contact Author)

University of Leeds - Leeds University Business School (LUBS) ( email )

Leeds LS2 9JT
United Kingdom

Efthymios G. Tsionas

Lancaster University ( email )

Lancaster LA1 4YX
United Kingdom

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