Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers

We use a dataset for the group of G7 countries and China to study the out-of-sample predictive value of uncertainty and its international spillovers for the realized variance of crude oil (West Texas Intermediate and Brent) over the sample period from 1996Q1 to 2020Q4. Using the Lasso estimator, we found evidence that uncertainty and international spillovers had predictive value for the realized variance at intermediate (two quarters) and long (one year) forecasting horizons in several of the forecasting models that we studied. This result holds also for upside (good) and downside (bad) variance, and irrespective of whether we used a recursive or a rolling estimation window. Our results have important implications for investors and policymakers.

Uncertainty, Spillovers, and Forecasts of the Realized Variance of Gold Returns

Using data for the group of G7 countries and China for the sample period 1996Q1 to 2020Q4, we study the role of uncertainty and spillovers for the out-of-sample forecasting of the realized variance of gold returns and its upside (good) and downside (bad) counterparts. We go beyond earlier research in that we do not focus exclusively on U.S.-based measures of uncertainty, and in that we account for international spillovers of uncertainty. Our results, based on the Lasso estimator, show that, across the various model configurations that we study, uncertainty has a more systematic effect on out-of-sample forecast accuracy than spillovers. Our results have important implications for investors in terms of, for example, pricing of related derivative securities and the development of portfolio-allocation strategies.

Structural Panel Bayesian VAR with Multivariate Time-Varying Volatility to Jointly Deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues

This paper improves a standard Structural Panel Bayesian Vector Autoregression model in order to jointly deal with issues of endogeneity, because of omitted factors and unobserved heterogeneity, and volatility, because of policy regime shifts and structural changes. Bayesian methods are used to select the best model solution for examining if international spillovers come from multivariate volatility, time variation, or contemporaneous relationship. An empirical application among Central-Eastern and Western Europe economies is conducted to describe the performance of the methodology, with particular emphasis on the Great Recession and post-crisis periods. A simulated example is also addressed to highlight the performance of the estimating procedure. Findings from evidence-based forecasting are also addressed to evaluate the impact of an ongoing pandemic crisis on the global economy.

International monetary spillovers and macroeconomic stability in developing countries

<abstract> <p>This paper analyses the impact of international spillovers on macroeconomic stability in developing countries. Specifically, the study investigates the impact of United States (US) monetary policy spillovers in the form of US inflation and Federal funds interest rate on Uganda and Kenya's inflation rates, interest rates and the exchange rates, key macroeconomic indicators of importance to macroeconomic stability. The focus on international spillovers from the USA is due to the dominant role it plays in determining global economic conditions. The study applies the Generalized Vector Autoregressive (GVAR) approach to quantify spillovers across these economies. The results shows that despite recent efforts towards East African regional integration, international spillovers from global economies like the US are more significant in determining macroeconomic stability in developing countries, underscoring the importance of global policy coordination. Specifically, we find an amplification of return and volatility spillovers after the onset of the Global financial crisis.</p> </abstract>