nep-ets 2016-07-16 papers

on Econometric Time Series

Issue of 2016‒07‒16
two papers chosen by
Yong Yin
SUNY at Buffalo

Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models By Peiris, S.; Asai, M.; McAleer, M.J.
Large Vector Autoregressions with Stochastic Volatility and Flexible Priors By Clark, Todd E.; Carriero, Andrea; Marcellino, Massimiliano

Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models

By:	Peiris, S.; Asai, M.; McAleer, M.J.
Abstract:	In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model.
Keywords:	Stochastic volatility, GARCH models, Gegenbauer Polynomial, Long Memory, Spectral Likelihood, Estimation, Forecasting
JEL:	C18 C21 C58
Date:	2016–06–01
URL:	http://d.repec.org/n?u=RePEc:ems:eureir:93114&r=ets

Large Vector Autoregressions with Stochastic Volatility and Flexible Priors

By:	Clark, Todd E. (Federal Reserve Bank of Cleveland); Carriero, Andrea; Marcellino, Massimiliano
Abstract:	Recent research has shown that a reliable vector autoregressive model (VAR) for forecasting and structural analysis of macroeconomic data requires a large set of variables and modeling time variation in their volatilities. Yet, there are no papers jointly allowing for stochastic volatilities and large datasets, due to computational complexity. Moreover, homoskedastic VAR models for large datasets so far restrict substantially the allowed prior distributions on the parameters. In this paper we propose a new Bayesian estimation procedure for (possibly very large) VARs featuring time varying volatilities and general priors. This is important both for reduced form applications, such as forecasting, and for more structural applications, such as computing response functions to structural shocks. We show that indeed empirically the new estimation procedure performs very well for both tasks.
Keywords:	forecasting; models; structural shocks;
JEL:	C11 C13 C33 C53
Date:	2016–06–30
URL:	http://d.repec.org/n?u=RePEc:fip:fedcwp:1617&r=ets

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