We introduce a data driven and model free approach for computing conditional expectations. The new method is based on classical techniques combined with machine learning methods. In particular, we consider kernel density estimation based on simulated risk factors combined with a control variate....

Financial contagion and systemic risk measures are commonly derived from conditional quantiles by using imposed model assumptions such as a linear parametrization. In this paper, we provide model free measures for contagion and systemic risk which are independent of the specifcation of...

This study is the first attempt to investigate both the linear and non-linear Granger causality between wavelet transformed spot and futures oil prices. Our findings consistently indicate bidirectional causality between the spot and futures oil markets at different time scales, under linear and...

The paper introduces the appropriate within estimators for the most frequently used three-dimensional fixed effects panel data models. It analyzes the behavior of these estimators in the cases of no self-flow data, unbalanced data, and dynamic autoregressive models. The main results are then...

The main objective of present study is to investigate the implications of intellectual property rights (IPR) protection on foreign direct investment and economic growth in Malaysia. We used the ARDL bounds testing approach to cointegration for long run relationship between the variables....