Essex Centre for Macro and Financial Econometrics (ECMFE) Mini Workshop

Workshop programme

1 - 2pm

Matei Demetrescu

“Is the U.S. real output growth really non-normal? Testing distributional assumptions in time-varying location-scale models” (joint work with Robinson Kruse-Becher)

Testing distributional assumptions is an evergreen topic in econometrics. A key assumption in this context is stationarity. Yet, under time-varying moments, the marginal distribution belongs to a mixture family. Therefore, tests consistently reject when stationarity assumptions are violated, even under correct specification of the baseline distribution. However, time-varying moments are common in economic data. We propose robust tests by means of local standardization. We demonstrate our approach in detail for normality, while our main results are extended to general location-scale models without essential modifications. Probability integral transforms accommodate a wide range of null distributions and imply simple raw moment restrictions. We use raw moments of probability integral transformations of locally standardized series (by flexible nonparametric estimators). Short-run dynamics are accounted for by the fixed-bandwidth approach which leads to robustness of the proposed test statistics to the estimation error induced by the local standardization. We propose a simple rule for choosing the tuning parameters and an effective finite-sample adjustment. Monte Carlo experiments show that the new tests perform well in terms of size and power and outperform alternative tests even under stationarity. We find - in contrast to other studies - no evidence against normality of U.S. real output growth after accounting for time-variation.

2 - 3pm

Ilias Chronopoulos

“High Dimensional Generalised Penalised Least Squares” (joint work with Katerina Chrysikou and George Kapetanios)

In this paper we develop inference in high dimensional linear models with serially correlated errors. We examine the Lasso estimator under the assumption of strong mixing in the covariates and error processes. While the Lasso estimator performs poorly under such circumstances, we estimate via GLS Lasso the parameters of interest and extend the asymptotic properties of the Lasso under more general conditions. Our theoretical results indicate that the non-asymptotic bounds for stationary dependent processes are sharper, while the rate of Lasso under general conditions appears slower as T, p→∞. Further, we employ debiasing methods to perform inference uniformly on the parameters of interest. Monte Carlo results support the proposed estimator, as it has significant efficiency gains over traditional methods.

3 - 3.30pm

Tea and coffee break

3.30 - 4.30pm

Paulo Rodrigues

"Tail Index Estimation in the Presence of Covariates: Stock returns' tail risk dynamics"

This paper provides novel theoretical results for the estimation of the conditional tail index of Pareto and Pareto-type distributions in a time series context. We show that both the estimators and relevant test statistics are normally distributed in the limit, when independent and identically distributed or dependent data are considered. Simulation results provide support for the theoretical findings and highlight the good finite sample properties of the approach in a time series context. The proposed methodology is then used to analyze stock returns' tail risk dynamics. Two empirical applications are provided. The first consists in testing whether the time-varying tail exponents across firms follow Kelly and Jiang's (2014) assumption of common firm level tail dynamics. The results obtained from our sample seem not to favour this hypothesis. The second application, consists of the evaluation of the impact of two market risk indicators, VIX and Expected Shortfall (ES) and two firm specific covariates, capitalization and market-to-book on stocks tail risk dynamics. Although all variables seem important drivers of firms' tail risk dynamics, it is found that overall ES and firms' capitalization seem to have overall wider impact.