This paper shows that under absence of arbitrage opportunities the exchange rate reacts to restore equilibrium in international bond markets. The key factors determining its value are the difference between realized and implicit interest rate differentials, the underlying risk premium in bond...

This study investigates the practical importance of several VaR modeling and forecasting issues in the context of intraday stock returns. Value-at-Risk (VaR) predictions obtained from daily GARCH models extended with additional information such as the realized volatility and squared overnight...

This study investigates the practical importance of several VaR modeling and forecasting issues in the context of intraday stock returns. Value-at-Risk (VaR) predictions obtained from daily GARCH models extended with additional information such as the realized volatility and squared overnight...

We make use of quantile regression theory to obtain a combination of individual potentially-biased VaR forecasts that is optimal because it meets by construction ex post the correct out-of-sample conditional coverage criterion. This enables a Wald-type conditional quantile forecast encompassing...

This paper analyzes the ability of unconventional monetary policies to reduce the spread between the credit and the short-term policy interest rates. We provide a theoretical framework based on the bank-lending channel that incorporates an interbank money market. The proposed model shows that...

We propose an optimal architecture for deep neural networks of given size. The optimal architecture obtains from maximizing the minimum number of linear regions approximated by a deep neural network with a ReLu activation function. The accuracy of the approximation function relies on the neural...

This paper proposes a network regression model to account for peer contextual effects on the outcome variable. In contrast to the literature, we estimate the interaction matrix that defines the network structure. Spill-over effects are modelled as a functional coefficient that is approximated...

This paper proposes a novel methodology to construct optimal portfolios that incorporates the occurrence of systemic events. Investors maximize a modified Sharpe ratio conditional on a systemic event. We solve the portfolio allocation problem analytically under the absence of short-selling...

This paper proposes a novel methodology to detect Granger causality in mean in vector autoregressive settings using feedforward neural networks. The approach accommodates unknown dependence structures between the elements of highly-dimensional multivariate time series with weak and strong...