Title: Robustness, market (non-)efficiency, volatility slustering, stock return predictability and beyond
Authors: Rustam Ibragimov - Imperial College London and St. Petersburg State University (United Kingdom) [presenting]
Abstract: The analysis of (in-)efficiency, volatility clustering and predictive regressions in financial markets using traditional approaches based on ACF's of squared returns and asymptotic methods is complicated by the presence of non-linear dependence (e.g., modelled using GARCH-type dynamics) and heavy-tailedness. Similar problems appear with commonly used used predictive regressors. Several new approaches are presented to deal with the above problems. The approaches are based on the use of autocorrelations of absolute values of the returns, together with new methods of robust inference using conservativeness of t-statistics. In the methods, estimates of parameters of interest are computed for groups of data and the inference is based on t-statistics in resulting group estimates. This results in valid robust inference under a wide rage of heterogeneity and dependence assumptions under the only conditions of asymptotic normality of 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 (in-)efficiency, volatility clustering, predictive regressions and beyond.