New Robust Inference in Economics and Finance: Research Directions and Implications for Market Efficiency, Forecasting and Econometric Analysis

Authors: Professor Rustam Ibragimov (Imperial College Business School, Kazan Federal University and New Economic School)

When: 15:30-17:00 on Monday 14 April (hybrid)

Where:

• Institute of computational math and information technologies - Room 1206 (12th floor) Институт вычислительной математики и информационных технологий КФУ (2й корпус), ауд. 1206 (12й этаж)
• Zoom link

Abstract: Many key variables in finance, economics and risk management, including financial returns and foreign exchange rates, exhibit dependence over time, e.g., nonlinear dependence usually modelled GARCH-type volatility dynamics, 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 dependence over time (e.g., modelled using GARCH-type dynamics) 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 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 conservativeness properties of t-statistics (Ibragimov and Mueller, JBES, 2010, RESTAT, 2016) 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., those of predictive regression parameters or measures of nonlinear dependence) 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 range of heterogeneity and dependence assumptions satisfied in real-world financial and economic markets. . Numerical results and empirical applications confirm advantages of the new approaches over existing ones and their wide applicability in important problems in economics, finance, econometrics, robust forecasting and other areas. Several new theoretical and empirical results on applications of the robust inference approaches, including those in predictive regressions, the analysis of the dynamics of economic and financial markets, the study of market (in-)efficiency, nonlinear dependence and volatility clustering, and comparisons of forecasts and other fields will be discussed, together with perspective research directions.