We summarize the general combination approach by Billio et al. [2010]. In the combination model the weights follow logistic autoregressive processes, change over time and their dynamics are possible driven by the past forecasting performances of the predictive densities. For illustrative...

We summarize the general combination approach by Billio et al. [2010]. In the combination model the weights follow logistic auto-regressive processes, change over time and their dynamics are possible driven by the past forecasting performances of the predictive densities. For illustrative...

We summarize the general combination approach by Billio et al. [2010]. In the combination model the weights follow logistic autoregressive processes, change over time and their dynamics are possible driven by the past forecasting performances of the predictive densities. For illustrative...

In assessing drivers of commodity prices and volatility at this stage of the current super-cycle in commodities (year 12 of a projected 25), it is vital to understand that production cost is a fundamental. Moreover, marginal production costs are among the most powerful drivers of commodity...

This working paper presents the general theory of the higher order "skew lognormal cascade distribution" as a mathematical extension of the previously proposed skew lognormal cascade distribution. In particular, the second order distribution is studied in details, which incorporates the fat...

In this research paper ARCH-type models and option implied volatilities (IV) are applied in order to estimate the Value-at-Risk (VaR) of a stock index futures portfolio for several time horizons. The relevance of the asymmetries in the estimated volatility estimation is considered. The empirical...

Overdependence on oil revenue has exposed the economy to shocks from oil price variations. In this paper, we investigated the relationship between oil price on the stock prices of oil and gas firms quoted in the Nigerian Stock Exchange market. In doing so, the ARDL and NARDL approach is applied...

We propose a new approach to imposing economic constraints on forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two...

Financial time series often exhibit properties that depart from the usual assumptions of serial independence and normality. These include volatility clustering, heavy-tailedness and serial dependence. A voluminous literature on different approaches for modeling these empirical regularities has...

Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing.I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set...