We develop a new family of estimators of the covariance matrix that relies solely on forwardlooking information. It uses only current prices of plain-vanilla options. In an out-of-sample study we show that a minimum-variance strategy based on these fully-implied estimators outperforms several...

In this paper we develop the first estimator of the covariance matrix that relies solely on forward-looking information. This estimator only uses price information from a cross-section of plain-vanilla options. In an out-of-sample study for US blue-chip stocks we show that a minimum-variance...

In this paper we develop a new family of estimators of the covariance matrix that relies solely on forward-looking information. These estimators only use current price information from a cross-section of plain-vanilla options and employ different higher moments of the implied return...

Im Laufe des Jahres 1993 war die Metallgesellschaft Refining & Marketing (MGRM), eine US-amerikanische Tochtergesellschaft der Metallgesellschaft AG, in großem Umfang die Verpflichtung eingegangen, langfristig Öl zu Festpreisen zu liefern. Das dadurch entstehende Preisrisiko sollte über...

Seit der Nahrungsmittelpreiskrise 2007/08 ist die Volatilität von Nahrungsmittelpreisen wieder als wichtiges Thema in der politischen Diskussion aufgetaucht. Nicht nur die Beobachtung eines steigenden Preisniveaus, sondern auch der scheinbare Anstieg der Volatilität auf Schlüsselmärkten (vor...

This paper is the first to study the hedging of price risk with uncertain payment dates, a frequent problem in practice. It derives a variance-minimizing hedging strategy for two settings, the first employing linear contracts with different times to maturity and the second allowing for...

In this paper we apply statistical inference techniques to build neural network models which are able to explain the prices of call options written on the German stock index DAX. By testing for the explanatory power of several input variables serving as network inputs, some insight into the...