This paper examines the properties of the variance risk premium (VRP). We propose a flexible asset pricing model that captures co-jumps in prices and volatility, and self-exciting jump clustering. We estimate the model on equity returns and variance swap rates at different horizons. The total...

The variance risk premium represents the compensation paid to index option sellers for the risk of losses following upward movements in realized market return volatility. Common wisdom connects these spikes with elevated uncertainty on economic fundamentals. I incorporate this link within a...

We propose a nonparametric Bayesian approach for conducting inference on probabilistic surveys. We use this approach to study whether U.S. Survey of Professional Forecasters density projections for output growth and inflation are consistent with the noisy rational expectations hypothesis. We...

This article introduces a very flexible framework for causal and predictive market views and stress-testing. The framework elegantly combines Bayesian networks (BNs) and Entropy Pooling (EP). In the new framework, BNs are used to generate a finite set of joint causal views / stress-tests for the...

In this paper we employ a fundamental principle of classical mechanics known as the Least Action Principle to model the complex relationship between expected load and expected price in electricity spot markets. We consider here markets that feature a centralised electricity dispatch system that...

This paper documents the existence of a significant forecast error on crude oil futures. We interpret it as a risk premium, which, in part, could have been explained by means of a real-time US business cycle indicator, such as the degree of capacity utilization in manufacturing. This result is...

Option market prices have often been regarded as a window on investor sentiment about the future price behavior of the underlying asset. Such prices can be very different from model prices and have long been noted by implied volatility plots revealing “smiles” or “smirks”. In this paper...

Modelling price formation in electricity markets is a notoriously difficult process, due to physical constraints on electricity generation and flow. This difficulty has inspired the recent development of bottom-up agent-based models of electricity markets. While these have proven quite...

We analyse the importance of macroeconomic information, such as industrial production index and oil price, for forecasting daily electricity prices in two of the main European markets, Germany and Italy. We do that by means of mixed-frequency models, introducing a Bayesian approach to reverse...

We analyse the importance of macroeconomic information, such as industrial production index and oil price, for forecasting daily electricity prices in two of the main European markets, Germany and Italy. We do that by means of mixed-frequency models, introducing a Bayesian approach to reverse...