Uncertainty in Systemic Risks Rankings: Bayesian and Frequentist Analysis

“In this paper we propose efficient #bayesian Hamiltonian #montecarlo method for estimation of #systemicrisk #measures , LRMES, SRISK and ΔCoVaR, and apply it for thirty global systemically important banks and for eighteen largest #us #financialinstitutions over the period of 2000-2020. The systemic risk measures are computed based on the Dynamic Conditional Correlations model with generalized asymmetric #volatility. A policymaker may choose to rank the firms using some quantile of […]

Bayesian Mixed-Frequency Quantile Vector Autoregression: Eliciting Tail Risks of Monthly Us GDP

This paper proposes a novel mixed-frequency quantile vector autoregression (MF-QVAR) model that uses a #bayesian framework and multivariate asymmetric Laplace distribution to estimate missing low-frequency variables at higher frequencies. The proposed method allows for timely policy interventions by analyzing conditional quantiles for multiple variables of interest and deriving quantile-related #riskmeasures at high frequency. The model is applied to the […]

Bayesian Cart Models for Insurance Claims Frequency

This paper focuses on the development of #bayesian classification and regression tree (#cart) models for claims frequency modeling in non-life #insurance pricing. The authors propose the use of the zero-inflated #poisson distribution to address the issue of imbalanced claims data and introduce a general MCMC algorithm for posterior tree exploration. Additionally, the deviance information criterion (DIC) is used for model selection. […]

Bayesian Model Selection and Prior Calibration for Structural Models in Economic Experiments: Some Guidance for the Practitioner

“Bayesian estimates from experimental data can be influenced by highly diffuse or “uninformative” priors. This paper discusses how practitioners can use their own expertise to critique and select a prior that (i) incorporates our knowledge as experts in the field, and (ii) achieves favorable sampling properties. I demonstrate these techniques using data from eleven experiments […]

Multi-Population Mortality Modelling: A Bayesian Approach

“An efficient Bayesian Markov Chain Monte-Carlo method is developed to estimate the unknown parameters to address the computational complexity. Our empirical application to the mortality data collected for the Group of Seven (G7) nations demonstrates the efficacy of our approach.” Lire

A Unified Bayesian Framework for Pricing Catastrophe Bond Derivatives

“The primary contribution of this paper is to present a unified Bayesian CAT bond pricing framework based on uncertainty quantification of catastrophes and interest rates. Our framework allows for complex beliefs about catastrophe risks to capture the distinct and common patterns in catastrophe occurrences, and when combined with stochastic interest rates, yields a unified asset […]