We argue that negative skew and positive mean of the distribution of stock returns are largely due to the broken symmetry of stochastic volatility governing gains and losses. Starting with stochastic differential equations for stock returns and for stochastic volatility, we argue that the...

We examine tail dependence between consumer sentiment and spending during crises, focusing on COVID-19 and the Global Financial Crisis. Using copula models on U.S. monthly data from 2003-2024, we quantify extreme co-movements and find asymmetric tail dependence that intensifies during crises:...

This paper investigates tail-risk transmission and asymmetric dependence in commodity markets using an asymmetric fuzzy vine copula framework applied to gold, crude oil, natural gas, and silver from 1 January 2015 to 1 January 2025, extracted from Yahoo Finance. Bootstrap-based trapezoidal fuzzy...

This work presents a practical approach to improve risk quantification for heavy-tailed insurance claims through model averaging and grid map visualization, addressing the drawbacks of traditional single "best" model selection commonly used in actuarial and model-fitting literature. This is a...

Statistical inference-both estimation and testing-for stochastic volatility (SV) models is known to be challenging and computationally demanding. We propose simple and efficient estimators for SV models with conditionally heavy-tailed error distributions, particularly the Student's t and...

A dynamic modelling for heavy-tailed cylindrical time series is developed by combining score-driven models with a generalised Pareto-type cylindrical distribution. The proposed specification extends existing cylindrical models by allowing location, scale, concentration, and crucially, the tail...

This paper introduces Bai-Perron structural break detection combined with negative binomial regression to model overdispersed U.S. IPO count data. Using monthly data from 1995 to 2024, we identify five breaks that partition IPO activity into six distinct regimes, each with fundamentally...

This research explores the application of extreme value theory in modelling and quantifying tail risks across different economic equity markets, with focus on the Nairobi Securities Exchange (NSE20), the South African Equity Market (FTSE/JSE Top40) and the US Equity Index (S&P500). The study...

We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for...

We study the ambiguity-adjusted return distribution induced by an investor with smooth ambiguity preferences 'a la Klibano! et al. (2005), who faces uncertainty about the variance of asset returns. The variance uncertainty is modeled using a gamma distribution, a second-order prior over the...

This paper develops a tractable framework in which heterogeneity, captured by the distribution of agent characteristics within an alternative (sector, location, or occupation), emerges endogenously in equilibrium through selection rather than being imposed exogenously. I show that widely used...

Weibull distribution is extensively used for wind resource assessment in fitting wind speed frequency distribution because of its reliability and simplicity. Its precision is highly dependent on the parameter estimation methods and different methods may result in varied results. This paper...

The mean-variance rule (M-V) conforms with the expected utility paradigm only in limited and economically unacceptable scenarios. Thus, the most widely employed portfolio-selection rule seemingly loses ground. We show with the commonly employed utility functions in economics, with a preference...

In the insurance literature, accurately predicting extreme losses has been a persistent and important problem. Recently, under the modelling framework of weighted distributions, several finite-mixture size-biased distributions, including size-biased Weibull and size-biased truncated log-normal...

Considering achieving a goal in each of several time intervals when, in every time interval, an adverse event may lead to a failure raises the question of the return probability of adverse events, so the probability of at least one failure to happen during the time period of interest. Through...

This paper introduces the Copula Asymmetry Index (CAI), a rolling, rank-based measure of asymmetric tail dependence between equity returns and implied-volatility proxies. CAI is defined as the difference between the empirical frequency of joint "equity-down & volatility-up" tail events and that...

Probability distributions, which are typically used to describe income distributions, are not suitable to describe a population's distribution of wealth because of the existence of negative observations and a large concentration of values close to zero. To overcome these problems, we describe...

The propensity score defining the probability of completing a given degree of education - to balance covariates - and the Mincer equation is here estimated at various degrees of higher education. The novelty is in implementing propensity score and regression estimators together in a...

We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for...

This paper presents a novel copula-based autoregressive framework for multilayer arrays of integer-valued time series with tensor structure. It complements recent advances in tensor time series that predominantly focus on real-valued data and overlook the unique properties of integer-valued time...

Power laws are pervasive in economics and social sciences, particularly in the upper tails of distributions such as wealth, income, firm size, and city populations. Their scale-free property makes them a universal framework to understand phenomena spanning several orders of magnitude. This...

The main objective of the research described in the article is to study the maximum likelihood (ML) estimation and the Bayesian approach for parameter estimation of the Lomax distribution. Additionally, the study aims to determine the approximate intervals for the parameters and the survival...

This study introduces a new family of distributions (FoD) called type I heavy-tailed odd Burr III-G (TI-HT-OBIII-G) distribution. Several statistical properties of the family are derived along with actuarial risk measures. The maximum likelihood estimation (MLE) approach is adopted in the...

Maximum likelihood estimation (MLE) in infinite mixture distributions often lacks closed-form solutions, requiring numerical methods such as the Newton-Raphson algorithm. Selecting appropriate initial values is a critical challenge in these procedures. This study introduces a bootstrap-based...

This paper studies how a forward-looking decision maker experiments on unknown alternatives of correlated utilities. The utilities are modeled by a Brownian motion such that similar alternatives yield similar utilities. Experimentation trades off between the continuation value of exploration and...

This paper studies asymmetry in economic activity in a multisector model with shocks to productivity and labor wedges. Complementarity across inputs - creating nonlinear intersectoral interactions - creates negative skewness. The analysis generates additional predictions: skewness is smaller at...