Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox

Casarin, Roberto; Grassi, Stefano; Ravazzolo, Francesco; Dijk, Herman K. van

doi:10.2139/ssrn.2602418

Cited by 7 publications

(5 citation statements)

References 41 publications

(25 reference statements)

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“…In order to implement this approach, we make use of the general density combination approach developed in Billio et al (2013), Casarin et al (2015) and recently Casarin et al (2016). We extend this approach that is aimed at forecasting to a mixture of forecasting and strategy combinations.…”

Section: Density Combinations and The Mitisem Filtermentioning

confidence: 99%

Time-Varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies

et al. 2016

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract A novel dynamic asset-allocation approach is proposed where portfolios as well as portfolio strategies are updated at every decision period based on their past performance. For modeling, a general class of models is specified that combines a dynamic factor and a vector autoregressive model and includes stochastic volatility, denoted by FAVAR-SV. It is an extended version of a factor-augmented vector autoregressive model, Bernanke et al. (2005). Next, a Bayesian strategy combination is introduced in order to deal with a set of strategies. Our approach extends the mixture of the experts analysis (Jacobs et al., 1991, Jordan and Jacobs, 1994, Jordan and Xu, 1995, Peng et al., 1996, by allowing the strategic weights to be dependent between strategies as well as over time and to further allow for strategy incompleteness. Our approach results in a combination of different portfolio strategies: a model-based and a residual momentum strategy, see Blitz et al. (2011). The estimation of this modeling and strategy approach can be done using an extended and modified version of the forecast combination methodology of Casarin et al. (2016). In the approach use is made of an implied state space structure for model and strategy weights. Given the complexity of the non-linear and non-Gaussian structure a new and efficient filter is introduced based on the MitISEM approach, see Hoogerheide et al. (2012). Using US industry portfolios between 1926M7 and 2015M6 as data, our empirical results indicate that time-varying combinations of flexible models in the FAVAR-SV class and two momentum strategies lead to better return and risk features than very simple and very complex models. More specifically, the proposed model combinations help 1 to improve return features like mean returns and Sharpe ratios while combinations of two strategies help to reduce risk features like volatility and largest loss. The latter result indicates that complete densities provide useful information for risk. Terms of use: Documents in

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Section: Density Combinations and The Mitisem Filtermentioning

confidence: 99%

Time-Varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies

et al. 2016

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“…Apply an extension of the parallelized version of the sequential Monte Carlo algorithm of Billio et al (2013) and Casarin et al (2014) to the case of Dynamic Factor Models.…”

Section: On Stagementioning

confidence: 99%

“…By making use of recent advances in computing power and parallel programming technique, it is feasible to apply non-linear time-varying weights to four factor models at different points in time during the quarter. In doing so, we apply the MATLAB package DeCo (Density Combination), developed by Casarin et al (2014), which provides an efficient implementation of the algorithm in Billio et al (2013) based on CPU and GPU parallel computing.…”

Section: Introductionmentioning

confidence: 99%

Combined Density Nowcasting in an Uncertain Economic Environment

Aastveit¹,

Ravazzolo

Dijk

2014

SSRN Journal

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“…For a case where parallel computing is used to combine information from many models for improved forecasting, we refer to Casarin et al (2013).…”

Section: Importance Of Hardware Developmentsmentioning

confidence: 99%

On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14

et al. 2014

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract This paper starts with a brief description of the introduction of the likelihood approach in econometrics as presented in Cowles Foundation Monographs 10 and 14. A sketch is given of the criticisms on this approach mainly from the first group of Bayesian econometricians. Publication and citation patterns of Bayesian econometric papers are analyzed in ten major econometric journals from the late 1970s until the first few months of 2014. Results indicate a cluster of journals with theoretical and applied papers, mainly consisting of Journal of Econometrics, Journal of Business and Economic Statistics and Journal of Applied Econometrics which contains the large majority of high quality Bayesian econometric papers. A second cluster of theoretical journals, mainly consisting of Econometrica and Review of Economic Studies contains few Bayesian econometric papers. The scientific impact, however, of these few papers on Bayesian econometric research is substantial. Special issues from the journals Econometric Reviews, Journal of Econometrics and Econometric Theory received wide attention. Marketing Science shows an ever increasing number of Bayesian papers since the middle nineties. The International Economic Review and the Review of Economics and Statistics show a moderate time varying increase. An upward movement in publication patterns in most journals occurs in the early 1990s due to the effect of the 'Computational Revolution'. The paper continues using a visualization technique to connect papers and authors around important empirical subjects such as forecasting in macro models and finance, choice and and equilibrium in micro models and marketing, and around more methodological subjects as model uncertainty and sampling algorithms. The information distilled from this analysis shows names of authors who contribute substantially to particular subjects. Next, subjects are discussed where Bayesian econometrics has shown substantial advances, namely, implementing stochastic simulation methods due to the computational revolution; flexible and unobserved component model structures in macroeconomic and finance; hierarchical structures and choice models in microeconomics and marketing. Three issues are summarized where Bayesian and frequentist econometricians differ: Identification, the value of prior information and model evaluation; dynamic inference and nonstationarity; vector autoregressive versus structural modeling. A topic of debate amongst Bayesian e...

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Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox

Cited by 7 publications

References 41 publications

Time-Varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies

Time-Varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies

Combined Density Nowcasting in an Uncertain Economic Environment

On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14

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