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A g-and-h Copula Approach to Risk Measurement in Multivariate Financial Models

Huggenberger, Markus - 2010

We propose and backtest a multivariate Value-at-Risk model for financial returns based on Tukey's g-and-h distribution. This distributional assumption is especially useful if (conditional) asymmetries as well as heavy tails have to be considered and fast random sampling is of importance. To...

Persistent link: https://www.econbiz.de/10013138164

Closed Form Solutions of Measures of Systemic Risk

Jaeger-Ambrozewicz, Manfred - 2012

models of volatility and correlation (Engle (2009), McNeil et al. (2005, chapter 4.6)) …

Persistent link: https://www.econbiz.de/10013115707

Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models

Blasques, Francisco - 2014

We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...

Persistent link: https://www.econbiz.de/10013049149

Using Transfer Entropy to Measure Information Flows Between Financial Markets

Peter, Franziska J. - 2017

We apply the concept of transfer entropy to quantify information flows between financial time series. Transfer entropy is a model-free measure designed as the Kullback-Leibler distance of transition probabilities. This approach allows to determine information transfer without being restricted to...

Persistent link: https://www.econbiz.de/10012976357

Dependent Microstructure Noise and Integrated Volatility Estimation from High-Frequency Data

Li, Z. Merrick - 2019

In this paper, we develop econometric tools to analyze the integrated volatility (IV) of the efficient price and the … estimators of the variance and autocovariances of noise using a variant of realized volatility. Next, we employ these estimators …

Persistent link: https://www.econbiz.de/10012860921

Factor and Idiosyncratic VAR-Ito Volatility Models for Heavy-Tailed High-Frequency Financial Data

Shin, Minseok; Kim, Donggyu; Wang, Yazhen; Fan, Jianqing - 2021

Various parametric models have been developed to predict large volatility matrices, based on the approximate factor … model structure. They mainly focus on the dynamics of the factor volatility with some finite high-order moment assumptions …. However, the empirical studies have shown that the idiosyncratic volatility also has a dynamic structure and it comprises a …

Persistent link: https://www.econbiz.de/10013211439

Causality effects in return volatility measures with random times

Renault, Eric; Werker, Bas J. M. - In: Journal of econometrics 160 (2011) 1, pp. 272-279

Persistent link: https://www.econbiz.de/10009242519

Measuring nonlinear Granger causality in mean

Song, Xiaojun; Taamouti, Abderrahim - In: Journal of business & economic statistics : JBES ; a … 36 (2018) 2, pp. 321-333

Persistent link: https://www.econbiz.de/10011895015

Sparse Graphical Vector Autoregression : A Bayesian Approach

Ahelegbey, Daniel Felix - 2016

This paper considers a sparsity approach for inference in large vector autoregressive (VAR) models. The approach is based on a Bayesian procedure and a graphical representation of VAR models. We discuss a Markov chain Monte Carlo algorithm for sparse graph selection, parameter estimation, and...

Persistent link: https://www.econbiz.de/10013005518

A bibliometric analysis of machine learning econometrics in asset pricing

Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of risk and financial management : JRFM 15 (2022) 11, pp. 1-17

measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to …

Persistent link: https://www.econbiz.de/10013475217