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Stock market asymmetries : a copula diffusion

Stefanova, Denitsa - 2012

Persistent link: https://www.econbiz.de/10009724340

Partially censored posterior for robust and efficient risk evaluation

Borowska, Agnieszka; Hoogerheide, Lennart; Koopman, Siem Jan - 2019

application is accurate prediction of ﬁnancial risk measures, where the area of interest is the left tail of the predictive … introduced to further decrease the numerical standard errors of the Value-at-Risk and Expected Shortfall estimators. The third …

Persistent link: https://www.econbiz.de/10012057160

A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting

Wang, Chao; Gerlach, Richard - In: Journal of forecasting 43 (2024) 1, pp. 40-57

Persistent link: https://www.econbiz.de/10014443184

The Stochastic Volatility in Mean Model with Time-Varying Parameters : An Application to Inflation Modeling

Chan, Joshua - 2015

This paper generalizes the popular stochastic volatility in mean model of Koopman and Hol Uspensky (2002) to allow for … time-varying parameters in the conditional mean. The estimation of this extension is nontrival since the volatility appears … inflation. The estimation results show substantial time-variation in the coefficient associated with the volatility, high …

Persistent link: https://www.econbiz.de/10013026159

An MCMC Approach to Multivariate Density Forecasting : An Application to Liquidity

Krueger, Fabian - 2013

We analyze the construction of multivariate forecasting densities based on conditional models for each variable, given the other variables; a joint predictive density is obtained by iteratively simulating from the conditional models. This idea has been pursued in the context of missing data...

Persistent link: https://www.econbiz.de/10013093948

Chapter 1 Bayesian Forecasting

Geweke, John; Whiteman, Charles - 2006

Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences about unknown ones. In Bayesian forecasting, one simply takes a subset of...

Persistent link: https://www.econbiz.de/10014023705

A non-linear forecast combination procedure for binary outcomes

Lahiri, Kajal; Yang, Liu - 2015

We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating...

Persistent link: https://www.econbiz.de/10010475341

An alternative Bayesian approach to structural breaks in time series models

Hauwe, Sjoerd van den; Paap, Richard; Dijk, Dick van - 2011

We propose a new approach to deal with structural breaks in time series models. The key contribution is an alternative dynamic stochastic specification for the model parameters which describes potential breaks. After a break new parameter values are generated from a so-called baseline prior...

Persistent link: https://www.econbiz.de/10011383033

An Alternative Bayesian Approach to Structural Breaks in Time Series Models

van den Hauwe, Sjoerd - 2011

Persistent link: https://www.econbiz.de/10013130370

Real-Time Learning, Macroeconomic Uncertainty, and the Variance Risk Premium

Bianchi, Daniele - 2015

The variance risk premium represents the compensation paid to index option sellers for the risk of losses following … upward movements in realized market return volatility. Common wisdom connects these spikes with elevated uncertainty on … produce a sizable and volatile variance risk premium. These shocks coincide with major events such as the LTCM/Russian crisis …

Persistent link: https://www.econbiz.de/10013034741