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Adaptive Polar Sampling with an Application to a Bayes Measure of Value-at-Risk

Bauwens, L.; Bos, C.S.; Dijk, H.K. van - Erasmus University Rotterdam, Econometric Institute - 1999

parsimony and robustness. APS is applied within a Bayesian analysis of a GARCH-mixture model which is used for the evaluation of …

Persistent link: https://www.econbiz.de/10005051715

Daily exchange rate behaviour and hedging of currency risk

Bos, C.S.; Mahieu, R.J.; Dijk, H.K. van - Erasmus University Rotterdam, Econometric Institute - 2000

methods. The effects of several model characteristics (unit roots, GARCH, stochastic volatility, heavy tailed disturbance …

Persistent link: https://www.econbiz.de/10008570624

Daily exchange rate behaviour and hedging of currency risk

Bos, C.S.; Mahieu, R.J.; Dijk, H.K. van - Erasmus University Rotterdam, Econometric Institute - 1999

Carlo methods. The effects of several model characteristics (unit roots, GARCH, stochastic volatility, heavy tailed …

Persistent link: https://www.econbiz.de/10008584714

Sensitivity analysis of volatility: a new tool for risk management

Manganelli, Simone; Ceci, Vladimiro; Vecchiato, Walter - European Central Bank - 2002

The extension of GARCH models to the multivariate setting has been fraught with difficulties. In this paper, we suggest … to work with univariate portfolio GARCH models. We show how the multivariate dimension of the portfolio allocation … derive the sensitivity of the univariate portfolio GARCH variance to the portfolio weights, by analytically computing the …

Persistent link: https://www.econbiz.de/10005222359

It Pays to Violate: How Effective are the Basel Accord Penalties?

Veiga, B. da; Chan, F.; McAleer, M.J. - Erasmus University Rotterdam, Econometric Institute - 2009

The internal models amendment to the Basel Accord allows banks to use internal models to forecast Value-at-Risk (VaR) thresholds, which are used to calculate the required capital that banks must hold in reserve as a protection against negative changes in the value of their trading portfolios. As...

Persistent link: https://www.econbiz.de/10008484079

Sovereign credit ratings, market volatility, and financial gains

Afonso, António; Gomes, Pedro; Taamouti, Abderrahim - European Central Bank - 2014

The reaction of EU bond and equity market volatilities to sovereign rating announcements (Standard & Poor’s, Moody’s, and Fitch) is investigated using a panel of daily stock market and sovereign bond returns. The parametric volatilities are filtered using EGARCH specifications. The...

Persistent link: https://www.econbiz.de/10010753741

The ten commandments for optimizing value-at-risk and daily capital charges

McAleer, M.J. - Erasmus University Rotterdam, Econometric Institute - 2008

Credit risk is the most important type of risk in terms of monetary value. Another key risk measure is market risk, which is concerned with stocks and bonds, and related financial derivatives, as well as exchange rates and interest rates. This paper is concerned with market risk management and...

Persistent link: https://www.econbiz.de/10005056578

Risk management of precious metals

Hammoudeh, S.M.; Malik, F.; McAleer, M.J. - Erasmus University Rotterdam, Econometric Institute - 2010

compute the VaR for major precious metals using the calibrated RiskMetrics, different GARCH models, and the semi …

Persistent link: https://www.econbiz.de/10008484085

Stale information, shocks and volatility

Gropp, Reint; Kadareja, Arjan - European Central Bank - 2006

We propose a new approach to measuring the effect of unobservable private information or beliefs on volatility. Using high-frequency intraday data, we estimate the volatility effect of a well identified shock on the volatility of the stock returns of large European banks as a function of the...

Persistent link: https://www.econbiz.de/10005816226

Gold, Oil, and Stocks: Dynamic Correlations

Baruník, Jozef; Kocenda, Evžen; Vácha, Lukáš - CESifo - 2015

We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations...

Persistent link: https://www.econbiz.de/10011272625