After lying dormant for more than two decades, the rare disaster framework has emerged as
a leading contender to explain facts about the aggregate market, interest rates, and financial …

Using the COVID-19 pandemic as a laboratory, we show that asset markets assign a time-
varying price to firms' disaster risk exposure. The cross-section of stock returns reflected …

The paper examines the relationships among market assets during stressful times, using two
recently proposed econometric modeling techniques for tail risk measurement: the extreme …

Hidden semi-Markov models (HSMMs) are among the most important models in the area of
artificial intelligence/machine learning. Since the first HSMM was introduced in 1980 for …

We develop a model that generates slowly unfolding disasters not only in the
macroeconomy but also in financial markets. In our model, investors cannot exactly …

Traditional disaster models with time-varying disaster risk are not perfect in explaining asset
returns. We redefine rare economic disasters and develop a novel disaster model with long …

We propose new systematic tail risk measures constructed using two different approaches.
The first is a non-parametric measure that captures the tendency of a stock to crash at the …

We extend a production-based asset pricing model by introducing learning about disaster
risk. The information is not perfect, and Bayesian learning is adopted to update beliefs about …

We study an economy subject to recurrent disasters when the frequency and duration of the
disasters are unobservable parameters. Imprecise information about transition intensities …

The aim of this paper is to propose a portfolio selection methodology capable to take into
account asset tail co-movements as additional constraints in Markowitz model. We apply the …