nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒06‒04
three papers chosen by
Yong Yin
SUNY at Buffalo

  1. Volatility Dependent Dynamic Equicorrelation By Adam Clements; Ayesha Scott; Annastiina Silvennoinen
  2. Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks By Gary Koop; Markus Jochmann; Rodney W. Strachan
  3. Testing of Changes in Persistence and Their Effect on the Forecasting Quality By Petrenko, Victoria; Skrobotov, Anton; Turuntseva, Maria

  1. By: Adam Clements (QUT); Ayesha Scott (QUT); Annastiina Silvennoinen (QUT)
    Abstract: This paper explores the link between equicorrelation and market volatility. The standard equicorrelation model is extended to condition the correlation process on volatility, based on the Volatility Dependent Dynamic Conditional Correlation class of model. Analysis of this relationship is presented in two empirical examples, with both a national and international context studied. The various correlation forecasting methods are compared using a portfolio allocation problem, specifically the global minimum variance portfolio and Model Confidence Set. Relative economic value is also considered. In the case of U.S. equities, overall the equicorrelation models perform well and the inclusion of volatility in the equicorrelations performs well against the standard equicorrelated model. For large portfolios a simple specification such as constant conditional correlation seems sufficient, particularly during periods of market calm. Internationally, the equicorrelated models perform poorly against the dynamic conditional corelation-based models. Reasoning is provided that the information pooling advantage equicorrelation has over dynamic conditional correlation models is eroded when forecasting correlations between indices, rather than equities. In both applications, there appears to be no statistically significant difference between the standard equicorrelation model and the Volatility Dependent class although in general a volatility dependent structure leads to lower portfolio variances.
    Keywords: Volatility, multivariate GARCH, equicorrelation, portfolio allocation
    JEL: C22 G11 G17
    Date: 2016–05–11
  2. By: Gary Koop (University of Strathclyde, Glasgow, UK and The Rimini Centre for Economic Analysis, Italy); Markus Jochmann (University of Strathclyde, Glasgow, UK and The Rimini Centre for Economic Analysis, Italy); Rodney W. Strachan (University of Queensland, UK and The Rimini Centre for Economic Analysis, Italy)
    Abstract: This paper builds a model which has two extensions over a standard VAR. The first of these is stochastic search variable selection, which is an automatic model selection device which allows for coefficients in a possibly over-parameterized VAR to be set to zero. The second allows for an unknown number of structural breaks in the VAR parameters. We investigate the in-sample and forecasting performance of our model in an application involving a commonly-used US macro-economic data set. We find that, in-sample, these extensions clearly are warranted. In a recursive forecasting exercise, we find moderate improvements over a standard VAR, although most of these improvements are due to the use of stochastic search variable selection rather than the inclusion of breaks. Creation-Date: 200801
  3. By: Petrenko, Victoria (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Skrobotov, Anton (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Turuntseva, Maria (Russian Presidential Academy of National Economy and Public Administration (RANEPA))
    Abstract: This paper provides a review of contributions to the field of change in persistence testing. We discuss both the constant/changed persistence testing (including multiple changes in persistence testing) and methods of estimation and inference for the dates of persistence changes.
    Keywords: persistence testing, methods of estimation, forecasting
    Date: 2016–04–08

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