This paper analyzes conditional threshold effects of stock market volatility on crude oil market volatility. We use the conditional threshold autoregressive (CoTAR) model, a novel extension of TAR from a constant to time-varying threshold. The conditional threshold is specified as an empirical...

We conduct an out-of-sample backtesting exercise of Growth-at-Risk (GaR) predictions for 24 OECD countries. We consider forecasts constructed from quantile regression and GARCH models. The quantile regression forecasts are based on a set of recently proposed measures of downside risks to GDP,...

We forecast the realized and median realized volatility of agricultural commodities using variants of the Heterogeneous AutoRegressive (HAR) model. We obtain tick-by-tick data for five widely traded agricultural commodities (Corn, Rough Rice, Soybeans, Sugar, and Wheat) from the CME/ICE. Real...

We propose the use of a risk measure built on flight-to-safety (FTS) episodes into a volatility forecasting model. We assign to each day in the sample a probability of being a FTS day after observing (ab)normal movements in the US equity, US bond and gold markets. By allowing each FTS day to be...

The aim of this paper is to prove the phenotypic convergence of cryptocurrencies, in the sense that individual cryptocurrencies respond to similar selection pressures by developing similar characteristics. In order to retrieve the cryptocurrencies phenotype, we treat cryptocurrencies as...

Fitting a local linear trend model with a Kalman smoother to smooth out noise inherent in the data suggests that the UK has passed the peak of new coronavirus cases and daily deaths, while England has passed peak hospital admissions. New cases appear to be flatlining, while daily deaths and...

The benefits of using flight-to-safety (FTS) in volatility forecasting are assessed within a multivariate GARCH framework. In particular, we propose realized semi-covariance between falling equity and rising safe haven returns as a proxy of FTS and we use it to model the conditional distribution...

The ability of Google Trends data to forecast the number of new daily cases and deaths of COVID-19 is examined using a dataset of 158 countries. The analysis includes the computations of lag correlations between confirmed cases and Google data, Granger causality tests, and an out-of-sample...

Information on economic policy uncertainty (EPU) does matter in predicting oil returns especially when accounting for omitted nonlinearities in the relationship between these two variables via a time-varying coefficient approach. In this work, we compare the forecastability of standard, Bayesian...

Rapach et al. (2013) have recently shown that U.S. equity market returns carry valuable information to improve return forecasts in global equity markets. In this study, we extend the work of Rapach et al. (2013) and examine if U.S. based equity market information can be used to improve realized...