Editorial

638 KiB

Open AccessEditorial

Computational Complexity and Parallelization in Bayesian Econometric Analysis

by Nalan Baştürk, Roberto Casarin, Francesco Ravazzolo and Herman K. Van Dijk

Econometrics 2016, 4(1), 9; https://doi.org/10.3390/econometrics4010009 - 22 Feb 2016

Viewed by 5198

Challenging statements have appeared in recent years in the literature on advances in computational procedures.[...] Full article

(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)

Research

Jump to: Editorial

8743 KiB

Open AccessArticle

Bayesian Calibration of Generalized Pools of Predictive Distributions

by Roberto Casarin, Giulia Mantoan and Francesco Ravazzolo

Econometrics 2016, 4(1), 17; https://doi.org/10.3390/econometrics4010017 - 16 Mar 2016

Cited by 11 | Viewed by 7211

Abstract

Decision-makers often consult different experts to build reliable forecasts on variables of interest. Combining more opinions and calibrating them to maximize the forecast accuracy is consequently a crucial issue in several economic problems. This paper applies a Bayesian beta mixture model to derive [...] Read more.

Decision-makers often consult different experts to build reliable forecasts on variables of interest. Combining more opinions and calibrating them to maximize the forecast accuracy is consequently a crucial issue in several economic problems. This paper applies a Bayesian beta mixture model to derive a combined and calibrated density function using random calibration functionals and random combination weights. In particular, it compares the application of linear, harmonic and logarithmic pooling in the Bayesian combination approach. The three combination schemes, i.e., linear, harmonic and logarithmic, are studied in simulation examples with multimodal densities and an empirical application with a large database of stock data. All of the experiments show that in a beta mixture calibration framework, the three combination schemes are substantially equivalent, achieving calibration, and no clear preference for one of them appears. The financial application shows that the linear pooling together with beta mixture calibration achieves the best results in terms of calibrated forecast. Full article

(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)

► Show Figures

Figure 1

767 KiB

Open AccessArticle

The Evolving Transmission of Uncertainty Shocks in the United Kingdom

by Haroon Mumtaz

Econometrics 2016, 4(1), 16; https://doi.org/10.3390/econometrics4010016 - 14 Mar 2016

Cited by 10 | Viewed by 5894

Abstract

This paper investigates if the impact of uncertainty shocks on the U.K. economy has changed over time. To this end, we propose an extended time-varying VAR model that simultaneously allows the estimation of a measure of uncertainty and its time-varying impact on key [...] Read more.

This paper investigates if the impact of uncertainty shocks on the U.K. economy has changed over time. To this end, we propose an extended time-varying VAR model that simultaneously allows the estimation of a measure of uncertainty and its time-varying impact on key macroeconomic and financial variables. We find that the impact of uncertainty shocks on these variables has declined over time. The timing of the change coincides with the introduction of inflation targeting in the U.K. Full article

(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)

► Show Figures

Figure 1

337 KiB

Open AccessArticle

Timing Foreign Exchange Markets

by Samuel W. Malone, Robert B. Gramacy and Enrique Ter Horst

Econometrics 2016, 4(1), 15; https://doi.org/10.3390/econometrics4010015 - 11 Mar 2016

Cited by 3 | Viewed by 7384

Abstract

To improve short-horizon exchange rate forecasts, we employ foreign exchange market risk factors as fundamentals, and Bayesian treed Gaussian process (BTGP) models to handle non-linear, time-varying relationships between these fundamentals and exchange rates. Forecasts from the BTGP model conditional on the carry and [...] Read more.

To improve short-horizon exchange rate forecasts, we employ foreign exchange market risk factors as fundamentals, and Bayesian treed Gaussian process (BTGP) models to handle non-linear, time-varying relationships between these fundamentals and exchange rates. Forecasts from the BTGP model conditional on the carry and dollar factors dominate random walk forecasts on accuracy and economic criteria in the Meese-Rogoff setting. Superior market timing ability for large moves, more than directional accuracy, drives the BTGP’s success. We explain how, through a model averaging Monte Carlo scheme, the BTGP is able to simultaneously exploit smoothness and rough breaks in between-variable dynamics. Either feature in isolation is unable to consistently outperform benchmarks throughout the full span of time in our forecasting exercises. Trading strategies based on ex ante BTGP forecasts deliver the highest out-of-sample risk-adjusted returns for the median currency, as well as for both predictable, traded risk factors. Full article

(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)

314 KiB

Open AccessArticle

Return and Risk of Pairs Trading Using a Simulation-Based Bayesian Procedure for Predicting Stable Ratios of Stock Prices

by David Ardia, Lukasz T. Gatarek, Lennart Hoogerheide and Herman K. Van Dijk

Econometrics 2016, 4(1), 14; https://doi.org/10.3390/econometrics4010014 - 10 Mar 2016

Cited by 3 | Viewed by 6582 | Correction

Abstract

We investigate the direct connection between the uncertainty related to estimated stable ratios of stock prices and risk and return of two pairs trading strategies: a conditional statistical arbitrage method and an implicit arbitrage one. A simulation-based Bayesian procedure is introduced for predicting [...] Read more.

We investigate the direct connection between the uncertainty related to estimated stable ratios of stock prices and risk and return of two pairs trading strategies: a conditional statistical arbitrage method and an implicit arbitrage one. A simulation-based Bayesian procedure is introduced for predicting stable stock price ratios, defined in a cointegration model. Using this class of models and the proposed inferential technique, we are able to connect estimation and model uncertainty with risk and return of stock trading. In terms of methodology, we show the effect that using an encompassing prior, which is shown to be equivalent to a Jeffreys’ prior, has under an orthogonal normalization for the selection of pairs of cointegrated stock prices and further, its effect for the estimation and prediction of the spread between cointegrated stock prices. We distinguish between models with a normal and Student t distribution since the latter typically provides a better description of daily changes of prices on financial markets. As an empirical application, stocks are used that are ingredients of the Dow Jones Composite Average index. The results show that normalization has little effect on the selection of pairs of cointegrated stocks on the basis of Bayes factors. However, the results stress the importance of the orthogonal normalization for the estimation and prediction of the spread—the deviation from the equilibrium relationship—which leads to better results in terms of profit per capital engagement and risk than using a standard linear normalization. Full article

(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)

1265 KiB

Open AccessArticle

Bayesian Nonparametric Measurement of Factor Betas and Clustering with Application to Hedge Fund Returns

by Urbi Garay, Enrique Ter Horst, German Molina and Abel Rodriguez

Econometrics 2016, 4(1), 13; https://doi.org/10.3390/econometrics4010013 - 08 Mar 2016

Cited by 1 | Viewed by 7005

Abstract

We define a dynamic and self-adjusting mixture of Gaussian Graphical Models to cluster financial returns, and provide a new method for extraction of nonparametric estimates of dynamic alphas (excess return) and betas (to a choice set of explanatory factors) in a multivariate setting. [...] Read more.

We define a dynamic and self-adjusting mixture of Gaussian Graphical Models to cluster financial returns, and provide a new method for extraction of nonparametric estimates of dynamic alphas (excess return) and betas (to a choice set of explanatory factors) in a multivariate setting. This approach, as well as the outputs, has a dynamic, nonstationary and nonparametric form, which circumvents the problem of model risk and parametric assumptions that the Kalman filter and other widely used approaches rely on. The by-product of clusters, used for shrinkage and information borrowing, can be of use to determine relationships around specific events. This approach exhibits a smaller Root Mean Squared Error than traditionally used benchmarks in financial settings, which we illustrate through simulation. As an illustration, we use hedge fund index data, and find that our estimated alphas are, on average, 0.13% per month higher (1.6% per year) than alphas estimated through Ordinary Least Squares. The approach exhibits fast adaptation to abrupt changes in the parameters, as seen in our estimated alphas and betas, which exhibit high volatility, especially in periods which can be identified as times of stressful market events, a reflection of the dynamic positioning of hedge fund portfolio managers. Full article

(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)

► Show Figures

Figure 1

694 KiB

Open AccessArticle

Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

by Arnaud Dufays

Econometrics 2016, 4(1), 12; https://doi.org/10.3390/econometrics4010012 - 08 Mar 2016

Cited by 6 | Viewed by 6125

Abstract

Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only do SMC [...] Read more.

Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only do SMC algorithms draw posterior distributions of static or dynamic parameters but additionally they provide an estimate of the marginal likelihood. The tempered and time (TNT) algorithm, developed in this paper, combines (off-line) tempered SMC inference with on-line SMC inference for drawing realizations from many sequential posterior distributions without experiencing a particle degeneracy problem. Furthermore, it introduces a new MCMC rejuvenation step that is generic, automated and well-suited for multi-modal distributions. As this update relies on the wide heuristic optimization literature, numerous extensions are readily available. The algorithm is notably appropriate for estimating change-point models. As an example, we compare several change-point GARCH models through their marginal log-likelihoods over time. Full article

(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)

► Show Figures

Figure 1

1902 KiB

Open AccessArticle

Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM

by Nalan Baştürk, Stefano Grassi, Lennart Hoogerheide and Herman K. Van Dijk

Econometrics 2016, 4(1), 11; https://doi.org/10.3390/econometrics4010011 - 07 Mar 2016

Cited by 3 | Viewed by 6229

Abstract

This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel MitISEM. The basic MitISEM algorithm provides an automatic and flexible method to approximate a non-elliptical target density using adaptive mixtures of Student-t densities, where only a kernel of [...] Read more.

This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel MitISEM. The basic MitISEM algorithm provides an automatic and flexible method to approximate a non-elliptical target density using adaptive mixtures of Student-t densities, where only a kernel of the target density is required. The approximation can be used as a candidate density in Importance Sampling or Metropolis Hastings methods for Bayesian inference on model parameters and probabilities. We present and discuss four canonical econometric models using a Graphics Processing Unit and a multi-core Central Processing Unit version of the MitISEM algorithm. The results show that the parallelization of the MitISEM algorithm on Graphics Processing Units and multi-core Central Processing Units is straightforward and fast to program using MATLAB. Moreover the speed performance of the Graphics Processing Unit version is much higher than the Central Processing Unit one. Full article

(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)

► Show Figures

Figure 1

646 KiB

Open AccessArticle

Sequentially Adaptive Bayesian Learning for a Nonlinear Model of the Secular and Cyclical Behavior of US Real GDP

by John Geweke

Econometrics 2016, 4(1), 10; https://doi.org/10.3390/econometrics4010010 - 02 Mar 2016

Cited by 3 | Viewed by 5148

Abstract

There is a one-to-one mapping between the conventional time series parameters of a third-order autoregression and the more interpretable parameters of secular half-life, cyclical half-life and cycle period. The latter parameterization is better suited to interpretation of results using both Bayesian and maximum [...] Read more.

There is a one-to-one mapping between the conventional time series parameters of a third-order autoregression and the more interpretable parameters of secular half-life, cyclical half-life and cycle period. The latter parameterization is better suited to interpretation of results using both Bayesian and maximum likelihood methods and to expression of a substantive prior distribution using Bayesian methods. The paper demonstrates how to approach both problems using the sequentially adaptive Bayesian learning algorithm and sequentially adaptive Bayesian learning algorithm (SABL) software, which eliminates virtually of the substantial technical overhead required in conventional approaches and produces results quickly and reliably. The work utilizes methodological innovations in SABL including optimization of irregular and multimodal functions and production of the conventional maximum likelihood asymptotic variance matrix as a by-product. Full article

(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)

► Show Figures