Financial risk management is difficult at the best of times, but especially so in the presence of economic policy uncertainty. The purpose of this special issue on “Advances in Financial Risk Management and Economic Policy Uncertainty” is to highlight some areas of research in which novel econometric, financial econometric and empirical finance methods have contributed significantly to the analysis of financial risk management when there is economic policy uncertainty, specifically the power of print: uncertainty shocks, markets, and the economy, determinants of the banking spread in the Brazilian economy, forecasting value-at-risk using block structure multivariate stochastic volatility models, the time-varying causality between spot and futures crude oil prices, a regime-dependent assessment of the information transmission dynamics between oil prices, precious metal prices and exchange rates, a practical approach to constructing price-based funding liquidity factors, realized range volatility forecasting, modelling a latent daily tourism financial conditions index, bank ownership, financial segments and the measurement of systemic risk, model-free volatility indexes in the financial literature, robust hedging performance and volatility risk in option markets, price cointegration between sovereign CDS and currency option markets in the GFC, whether zombie lending should always be prevented, preferences of risk-averse and risk-seeking investors for oil spot and futures before, during and after the GFC, managing financial risk in Chinese stock markets, managing systemic risk in The Netherlands, mean-variance portfolio methods for energy policy risk management, on robust properties of the SIML estimation of volatility under micro-market noise and random sampling, asymmetric large-scale (I)GARCH with hetero-tails, the economic fundamentals and economic policy uncertainty of Mainland China and their impacts on Taiwan and Hong Kong, prediction and simulation using simple models characterized by nonstationarity and seasonality, and volatility forecast of stock indexes by model averaging using high frequency data.
