Do the carry trades respond to geopolitical risks? Evidence from BRICS countries

This paper aims to analyze the implications of geopolitical risks on the return and volatility of carry trade transactions in the context of BRICS countries for the period 2006–2020. Fixed effects regressions considering the sample countries as a single portfolio document that geopolitical risks are correlated with volatility, while the results are inconclusive for returns. The non-parametric time-varying coefficients panel data estimations further indicate that the effect of geopolitical risks on carry trade volatility is amplified during the Global Financial Crisis and the post-2016 episode. Moving to the disaggregated data, the time-varying robust Granger causality test of Rossi and Wang (2019) show that geopolitical risks have a significant in-sample predictive power for both carry trade return and volatility during a myriad of sub-periods, which can not be captured by standard constant parameter techniques in the presence of instabilities. Overall, our empirical results suggest that the exposure to geopolitical risks should be taken into account by global investors for risk diversification purposes when entering carry trade positions in BRICS countries.     

A copula-based time series model for global horizontal irradiation

The increasing importance of solar power for electricity generation leads to increasing demand for probabilistic forecasting of local and aggregated photovoltaic (PV) yields. Based on publicly available irradiation data, this paper uses an indirect modeling approach for hourly medium to long-term local PV yields. We suggest a time series model for global horizontal irradiation that allows for multivariate probabilistic forecasts for arbitrary time horizons. It features several important stylized facts. Sharp time-dependent lower and upper bounds of global horizontal irradiations are estimated. The parameters of the beta distributed marginals of the transformed data are allowed to be time-dependent. A copula-based time series model is introduced for the hourly and daily dependence structure based on simple vine copulas with so-called tail dependence. Evaluation methods based on scoring rules are used to compare the model’s power for multivariate probabilistic forecasting with other models used in the literature showing that our model outperforms other models in many respects.