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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

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

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

The Fine Structure of Equity-Index Option Dynamics

Andersen, Torben G.; Bondarenko, Oleg; Todorov, Viktor; … - School of Economics and Management, University of Aarhus - 2013

We analyze the high-frequency dynamics of S&P 500 equity-index option prices by constructing an assortment of implied volatility measures. This allows us to infer the underlying fine structure behind the innovations in the latent state variables driving the movements of the volatility surface....

Persistent link: https://www.econbiz.de/10010851229

Estimating Stochastic Volatility Models using Prediction-based Estimating Functions

Lunde, Asger; Brix, Anne Floor - School of Economics and Management, University of Aarhus - 2013

In this paper prediction-based estimating functions (PBEFs), introduced in Sørensen (2000), are reviewed and PBEFs for the Heston (1993) stochastic volatility model are derived. The finite sample performance of the PBEF based estimator is investigated in a Monte Carlo study, and compared to the...

Persistent link: https://www.econbiz.de/10010851259

Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading

Hounyo, Ulrich - School of Economics and Management, University of Aarhus - 2014

We propose a bootstrap method for estimating the distribution (and functionals of it such as the variance) of various integrated covariance matrix estimators. In particular, we first adapt the wild blocks of blocks bootstrap method suggested for the pre-averaged realized volatility estimator to...

Persistent link: https://www.econbiz.de/10010937808

Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange

Lunde, Asger; Olesen, Kasper V. - School of Economics and Management, University of Aarhus - 2014

analyze empirical results for a selection of existing realized measures of volatility and incorporate them in a Realized GARCH … documented, which motivates the use of a flexible and robust methodology such as the Realized GARCH. Within this framework …-of-sample results for the Realized GARCH forecasts suggest a limited added value from using “traditional” realized volatility measures …

Persistent link: https://www.econbiz.de/10010945126