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Title: New measures of volatility clustering, nonlinear dependence and market (non-)efficiency Authors:  Anton Skrobotov - Russian Presidential Academy of National Economy and Public Administration and SPBU (Russia) [presenting]
Rasmus Soendergaard Pedersen - University of Copenhagen (Denmark)
Rustam Ibragimov - Imperial College London and St. Petersburg State University (United Kingdom)
Abstract: Many key variables in finance, economics and risk management 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 is of key importance for robustness of many key models and standard inference approaches. The presence of non-linear dependence and heavy-tailedness may problematic the analysis of (non-)efficiency, volatility clustering and predictive regressions in economic and financial markets using traditional approaches based on ACFs of squared returns and asymptotic methods. Several new approaches are presented in order to deal with the above problems. The approaches are based on conservativeness properties of t-statistics and several new results on applicability of t-statistic based robust inference methods in the settings considered. In the approaches, estimates of parameters of interest (e.g., measures of nonlinear dependence based on sample autocorrelations of powers of the returns' absolute values) are computed for groups of data and the inference is based on t-statistics in resulting group estimates. Numerical results and empirical applications confirm advantages of the new approaches over existing ones and their wide applicability in the study of market (non-)efficiency, volatility clustering, nonlinear dependence, and other areas.