To avert the impending global Cyber-Finance Insurance Crisis based upon large-scale commercial reliance upon quantitative models with inherent model risks, tail risks, and systemic risks in current form, this post-doctoral thesis makes the following key contributions: Develops the first known...

We provide an approach to forecasting the long-run (unconditional distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts...

This paper proposes a statistical framework in which artificial intelligence can assist human decision making. Using observational data we benchmark the performance of each decision maker against the machine predictions, and replace decision makers whose information process quality is dominated...

In the aftermath of the Global Financial Crisis, some risk management practitioners have advocated wider adoption of Bayesian inference to replace Value- at-Risk (VaR) models in order to minimize risk failures. Despite its limitations, the Bayesian methodology has significant advantages. Just...

In aftermath of the Financial Crisis, some risk management practitioners advocate wider adoption of Bayesian inference to replace Value-at-Risk (VaR) models for minimizing risk failures (Borison & Hamm, 2010). They claim reliance of Bayesian inference on subjective judgment, the key limitation...

This paper is a statistical time-series investigation addressed at testing the anthropogenic climate change hypothesis known as the “hockey-stick”. The time-series components of a select batch of 258 long-term yearly Climate Change Proxies (CCP) included in 19 paleoclimate datasets, all of...

The sample covariance matrix is known to contain substantial statistical noise, making it inappropriate for use in financial decision making. Leading researchers have proposed various filtering methods that attempt to reduce the level of noise in the covariance matrix estimator. In most cases,...

This paper establishes the following: Random Utility Models (RUMs) that assume consistent preferences result in inconsistent and therefore irrational choice behavior. Paradoxically, it is possible to create RUMs that result in consistent choice behavior, but these models assume inconsistent...

Bayesian inference in moment condition models is difficult to implement. For these models, a posterior distribution cannot be calculated because the likelihood function has not been fully specified. In this paper, we obtain a class of likelihoods by formal Bayesian calculations that take into...

We develop a reliable Bayesian inference for the RIF-regression model of Firpo, Fortin and Lemieux (Econometrica, 2009) in which we first estimate the log wage distribution by a mixture of normal densities. This approach is pursued so as to provide better estimates in the upper tail of the wage...