The availability of high-frequency trading data and developments in computing technology make it possible to evaluate Intraday Value-at-Risk (IVaR), a useful risk management tool for investors and regulators. In this paper, we propose a new model to evaluate and predict the IVaR for stocks based...

Institutional investors have significantly increased exposure to commodity futures especially oil futures after 2004 in a process called commodity market financialization, raising questions about the risk-sharing and price-discovery functions of the market. We propose an ARMA-GARCH R-vine copula...

We investigate a new family of distributionally robust optimization problem under marginal and copula ambiguity with applications to portfolio optimization problems. The proposed model considers the ambiguity set of portfolio return in which the marginal distributions and their copula are close...

We introduce a new stochastic order for the tail dependence between random variables. We then study different measures of tail dependence which are monotone in the proposed order, thereby extending various known tail dependence coefficients from the literature. We apply our concepts in an...

Estimating the market risk is conditioned by the fat tail of the distribution of returns. But the tail index depends on the threshold of this distribution fat tail. We propose a methodology based on the decomposition of the series into positive outliers, Gaussian central part and negative...

This study proposes a Bayesian approach for exact finite-sample inference of an instrument-free estimation method that builds upon joint estimation using copulas to deal with endogenous covariates. Although copula approaches with applications to handle regressor-endogeneity have been frequently...

Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and its associated loss are above their respective quantiles but bounded above by their respective larger quantiles. In...

Chapter 1. Introduction -- Chapter 2. Robust Dynamic Space–time Panel Data Models Using εε-contamination: An Application to Crop Yields and Climate Change -- Chapter 3. Unbiased Estimation of the OLS Covariance Matrix When the Errors are Clustered -- Chapter 4. Refined GMM Estimators for...