A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and,...

This paper uses simulation-based portfolio optimization to mitigate the left tail risk of the portfolio. The contribution is twofold. (i) We propose the Markov regime-switching GARCH model with multivariate normal tempered stable innovation (MRS-MNTS-GARCH) to accommodate fat tails, volatility...

We investigate the use of a P-spline generalized additive hedonic model (GAM) for real estate prices in large U.S. cities, contrasting their predictive efficiency against commonly used linear and polynomial-based generalized linear models (GLM). Using intrinsic and extrinsic factors available...

Die gegenwärtigen Kontroversen zu den Auswirkungen eines Energieembargos gegen Russland, ausgelöst durch Russlands Krieg gegen die Ukraine, haben den Fokus auf die Verringerung der Abhängigkeit von fossilen Energieträgern und eine Neuordnung der Energieversorgung gelegt....

The use of GARCH models with stable Paretian innovations in financial modeling has been recently suggested in the literature. This class of processes is attractive because it allows for conditional skewness and leptokurtosis of financial returns without ruling out normality. This contribution...

Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors, such as GARCH models, are investigated, to determine...

We introduce a dynamic banking-macro model, which abstains from conventional mean-reversion assumptions and in which - similar to Brunnermeier and Sannikov (2010) - adverse asset-price movements and their impact on risk premia and credit spreads can induce instabilities in the banking sector. To...

The paper illustrates and evaluates a Kalman filtering method for forecasting German real GDP at monthly intervals. German real GDP is produced at quarterly intervals but analysts and decision makers often want monthly GDP forecasts. Quarterly GDP could be regressed on monthly indicators, which...

Using unobservable conditional variance as measure, latent-variable approaches, such as GARCH and stochastic-volatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of high-frequency financial market data modeling realized...

A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and,...