The aim of this paper is to move away from a Gaussian assumption and to provide new algorithms that can be used to implement a Markov-functional model driven by a more general class of one-dimensional diffusion processes.
Parameter estimation, bias correction and uncertainty quantification in the Vasicek credit portfolio model
This paper is devoted to the parameterization of correlations in the Vasicek credit portfolio model. First, the authors analytically approximate standard errors for value-at-risk and expected shortfall based on the standard errors of intra-cohort…
This paper develops a parsimonious model for evaluating portfolio credit derivatives dependent on aggregate loss.
Quant grads should be taught follies of LTCM, Gaussian copula and London Whale, writes UBS’s Gordon Lee
Dependence dynamics among exchange rates, commodities and the Brazilian stock market using the R-vine SCAR model
The objective of this paper is to assess the dependence dynamics among Brazilian real exchange rates, commodity prices and the Brazilian stock market using a regular vine copula combined with the stochastic autoregressive copula model.
In this paper, the authors consider wind power utilization in thirty-one different locations in Germany.
In this paper, the authors combine MS dynamic copulas with the skewed t SV model to study the optimal hedge ratios of portfolios.
The author presents a comparison between maximal and daily average production of photovoltaic and wind energy based on a transmission system operator in Germany using statistical analysis with different seasonality functions.
Quant sceptical of machine learning algos and black boxes
In this paper, a sensitivity analysis using pair–copula decomposition of multivariate dependency models is performed on estimates of value-at-risk (VaR) and conditional value-at-risk (CVaR).
In this paper, the authors focus on seven stock market indexes: two US, three European, one emerging and one Japanese. They select different pairs of markets and, with the help of wavelets, decompose these series at different timescales.
In this paper, the authors study tail dependence by defining the conditions required for all the methods used to perform and to quantify their efficiency and accuracy.
This paper assesses the model risk associated with the copula choice for the calculation of the Default Risk Charge (DRC) measure.
This paper deals with the credit valuation adjustment (CVA) of interest rate swap (IRS) contracts in the presence of an adverse dependence between the default time and interest rates: so-called wrong-way risk (WWR).
A latent variable credit risk model comprising nonlinear dependencies in a sector framework with a stochastically dependent loss given default
This paper proposes a latent variable credit risk model for large loan portfolios. It employs the concept of nested Archimedean copulas to account for both a sector-type dependence structure and a copula-dependent stochastic loss given default (LGD).
This paper applies vine copulas with GARCH marginals to the problem of capturing asset dependence and tail dynamics for currency and commodity exposures commonly found in portfolios of global corporates.
CCP’s risk managers propose a framework for generating extreme but plausible stress scenarios
This paper presents a clearinghouse framework to establish initial margin requirements for portfolios of credit default swap instruments.
This paper uses simulation studies and an example of operational risk modeling to show the necessity and benefit of using RMT to fit high-dimensional t-copulas in risk modeling.
The author of this paper presents a general and path-consistent wrong-way risk (WWR) model that does not require simulation of credit and market variables simultaneously.
This paper investigates the extent to which the nonstationarity of financial time series affects both the estimation and the modeling of empirical copulas.
Value-at-risk bounds for multivariate heavy tailed distribution: an application to the Glosten–Jagannathan–Runkle generalized autoregressive conditional heteroscedasticity model
This paper aims to derive VaR bounds for the portfolios of possibly dependent financial assets for heavy tailed Glosten–Jagannathan–Runkle generalized autoregressive conditional heteroscedasticity processes using extreme value theory copulas.
The AMA doesn’t make any sense – but the idea of a single, simple equation does, writes Ruben Cohen
The authors of this paper simulate realistic total bank return distributions by means of a top-down copula approach for different parameter settings.