We propose a simple but rigorous stochastic volatility – stochastic correlation model, formulated as a pair of correlated CIRCEV and Jacobi processes. Our model proves to fit both marginal and joint distributions of implied volatility and correlation. When risk factors estimated from...

We propose a reduced-form eSIRD model to operationalize the country-specific Basic Reproduction Number and Mortality Rate for the COVID-19 pandemic. Enhanced from the classical epidemiological structural SIR model, our model has several contributions: 1) Allowing the effective Basic Reproduction...

We propose a method to integrate frequentist and subjective probabilities in order to obtain a coherent asset allocation in the presence of stress events. Our working assumption is that in normal market asset returns are sufficiently regular for frequentist statistical techniques to identify...

Today's top financial-risk professionals have come to rely on ever-more sophisticated mathematics in their attempts to come to grips with financial risk. But this excessive reliance on quantitative precision is misleading--and it puts us all at risk. This is the case that Riccardo Rebonato makes...

We propose a method to integrate frequentist and subjective probabilities in order to obtain a coherent asset allocation in the presence of stress events. Our working assumption is that in normal market asset returns are sufficiently regular for frequentist statistical techniques to identify...

This paper presents a simple reformulation of the restricted Cieslak and Povala (2010) return-predicting factor which retains by construction exactly the same (impressive) explanatory power as the original one, but affords an alternative and attractive interpretation. What determines the future...

We look at the dependence of the magnitude of rate moves on the level of rates, and we find a universal relationship that holds across currencies and over a very extended period of time (almost 50 years). For the very low level of rates, we find a proportional behaviour; for rates of an...

The paper present a simple, yet surprisingly effective, approximation for the no-arbitrage drifts that appear in LMM(-SABR)-family term structure models. The approximation reduces the burden of the computational bottleneck for these models by one order of magnitude. As the size of the problem...

In this paper we try to understand the economic explanation of the difference in predictability afforded by the old and the new-generation return-predicting factors. To do so, first we show that the Cieslak-Povala (2010) approach can be expressed in terms of a conditional prediction of where the...

We introduce Kinetic Component Analysis (KCA), a state-space application that extracts the signal from a series of noisy measurements by applying a Kalman Filter on a Taylor expansion of a stochastic process. We show that KCA presents several advantages compared to other popular noise-reduction...