Event Details

This Seminar will be hosted by the Economics Department

Rustam Ibragimov will present "Robust InferenceUnder Heavy-Tailedness and Dependence in Economics and Finance: Market(In-)Efficiency, Volatility Clustering, Stock Return Predictability and Beyond"

Abstract

Many key variables in finance, economics and risk management, including financialreturns and foreign exchange rates, exhibit nonlinear dependence, heterogeneity and heavy-tailedness of some usually largely unknown type. Recent works in the literature have shown that heavy-tailedness the property of financial and economic markets that governs large downfalls and large fluctuations in them -is of key importance for robustness of many key models and standard inference approaches in economics, finance, econometrics and statistics. The presence of non-lineardependence (e.g., modelled using GARCH-type dynamics) and heavy-tailedness may problematic the analysis of (in-)efficiency, volatility clustering and predictive regressions in economic and financial markets using traditional approaches based on ACF’s of squared returns and asymptotic methods. Similar problems appear with commonly used predictive regressors. The talk will present several new approaches to deal with the above problems. The approaches are based on new methods of robust inference using conservativeness oft−statistics. In the methods, estimates of parameters of interest are computedfor groups of data and the inference is based on t−statistics in resulting group estimates. This results in valid robust inference under a wide range of heterogeneity and dependence assumptions under the only conditions of asymptotic mixed normality of group estimates that are satisfied in many settings. Numerical results and empirical applications confirm advantages of the new approaches over existing ones and their wide applicability in the study of market(in-)efficiency, volatility clustering, predictive regressions and other areas.