This paper considers a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and suggests the use of devolatized returns computed as returns standardized by realized volatilities rather than by GARCH type volatility estimates. The t-DCC estimation procedure is applied to a portfolio of daily returns on currency futures, government bonds and...

We put forward Value-at-Risk models relevant for commodity traders who have long and short trading positions in commodity markets. In a 5-year out-of-sample study on aluminium, copper, nickel, Brent crude oil and WTI crude oil daily cash prices and cocoa nearby futures contracts, we assess the performance of the RiskMetrics, skewed Student APARCH and skewed student ARCH models. While the skewed...

This paper studies model selection for a general class of models based on minimizing random distance functions. The proposed model selection criteria are consistent, regardless of whether models are nested or nonnested and regardless of whether models are correctly specified or not, in the sense that they select the best model with the least number of parameters with probability converging to 1...

In this paper we discuss sensitivity of forecasts with respect to the information set considered in prediction; a sensitivity measure called impact factor, IF, is defined. This notion is specialized to the case of VAR processes integrated of order 0, 1 and 2. For stationary VARs this measure corresponds to the sum of the impulse response coefficients. For integrated VAR systems, the IF has a di...

Article history: Received 25 October 2008 Received in revised form 19 July 2009 Accepted 5 January 2010 Available online 18 January 2010 In the finance literature, statistical inferences for large-scale testing problems usually suffer from data snooping bias. In this paper we extend the “superior predictive ability” (SPA) test of Hansen (2005, JBES) to a stepwise SPA test that can identify pred...

I extend the theory on factor models by incorporating “local” factors into the model. Local factors affect a decreasing fraction of the observed variables. This implies a continuum of eigenvalues of the covariance matrix, as is commonly observed in applications. I derive conditions under which local factors will be estimated consistently using the common principal component estimator. I further...

In this paper, I examine the …nite sample properties of the Vuong test (Vuong (1989)) for nonnested model comparison. I …nd that both the one-step and the two-step classical Vuong tests over-reject the null hypothesis when the null hypothesis is true. The over-rejection can be large in certain scenarios. A simple one-step modi…ed test is proposed. The modi…ed test applies to both overlapping mo...

Big Data offer potential benefits for statistical modelling, but confront problems like an excess of false positives, mistaking correlations for causes, ignoring sampling biases, and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considera...

This paper investigates if component GARCH models introduced by Engle and Lee (1999) and Ding and Granger (1996) can capture the long-range dependence observed in measures of time-series volatility. Long-range dependence is assessed through the sample autocorrelations, two popular semiparametric estimators of the long-memory parameter, and the parametric fractionally integrated GARCH (FIGARCH) ...

Building on previous literature, we assess when foreign aid is effective in fighting terrorism using quantile regressions on a panel of 78 developing countries for the period 1984-2008. Bilteral, multilateral and total aid indicators are used whereas terrorism includes: domestic, transnational, unclear and total terrorism dynamics. We consistently establish that foreign aid (bilateral, multilat...