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Partial Information, Return Predictability and Identification of Linear Stochastic Differential Equations

Buccheri, Giuseppe - 2022

Asset allocation and option pricing models are often formulated by means of linear stochastic differential equations. We show that this class of models is not identifiable from information contained in discrete-time data when the expected return process is unobservable. The indeterminacy arises...

Persistent link: https://www.econbiz.de/10013296829

The empirical performance of option-based densities of foreign exchange

Craig, Ben R. (contributor); Keller, Joachim G. (contributor) - 2003 - [Elektronische Ressource]

Persistent link: https://www.econbiz.de/10002542714

The empirical performance of option based densities of foreign exchange

Craig, Ben R.; Keller, Joachim G. - 2002

Risk neutral densities (RND) can be used to forecast the price of the underlying basis for the option, or it may be … American puts and calls reported on the Chicago Mercentile Exchange for the end of the day. This process implies a risk neutral … tests wheter the densities provided from American options provide a good forecasting tool. We use a non-parametric test of …

Persistent link: https://www.econbiz.de/10001656178

l 1 - penalized likelihood smoothing of volatility processes allowing for abrupt changes

Neto, David; Sardy, Sylvain; Tseng, Paul - 2009

Persistent link: https://www.econbiz.de/10003926975

Large vector autoregressions with asymmetric priors

Carriero, Andrea; Clark, Todd E.; Marcellino, Massimiliano - 2015 - This draft: November 2015

We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors and time varying volatilities, even when the cross sectional dimension of the system N is particularly large. The algorithm is based on a simple triangularisation which allows to...

Persistent link: https://www.econbiz.de/10011389735

The stochastic volatility in mean model

Koopman, Siem Jan; Hol Uspensky, Eugenie - 2000

In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable in the mean equation. The same extension...

Persistent link: https://www.econbiz.de/10011303314