27 citations · 74 across the 13 of their papers we have counts for
30 papers
Fast Approximate Modular Inference for Option Price Prediction
Worapree Maneesoonthorn, David T. Frazier, Gael M. Martin
A new approximate Bayesian inferential framework is proposed that exploits multiple information sources -- daily spot returns, high-frequency spot data and option prices -- and ena…
ABC-based Forecasting in State Space Models
Chaya Weerasinghe, Ruben Loaiza-Maya, Gael M. Martin +1
Approximate Bayesian Computation (ABC) has gained popularity as a method for conducting inference and forecasting in complex models, most notably those which are intractable in som…
Solving the Forecast Combination Puzzle
David T. Frazier, Ryan Covey, Gael M. Martin +1
We demonstrate that the forecasting combination puzzle is a consequence of the methodology commonly used to produce forecast combinations. By the combination puzzle, we refer to th…
Optimal probabilistic forecasts for risk management
Yuru Sun, Worapree Maneesoonthorn, Ruben Loaiza-Maya +1
This paper explores the implications of producing forecast distributions that are optimized according to scoring rules that are relevant to financial risk management. We assess the…
Bayesian Forecasting in Economics and Finance: A Modern Review
Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn +6
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem…
Computing Bayes: From Then 'Til Now'
Gael M. Martin, David T. Frazier, Christian P. Robert
This paper takes the reader on a journey through the history of Bayesian computation, from the 18th century to the present day. Beginning with the one-dimensional integral first co…
The Impact of Sampling Variability on Estimated Combinations of Distributional Forecasts
Ryan Zischke, Gael M. Martin, David T. Frazier +1
We investigate the performance and sampling variability of estimated forecast combinations, with particular attention given to the combination of forecast distributions. Unknown pa…
Approximating Bayes in the 21st Century
Gael M. Martin, David T. Frazier, Christian P. Robert
The 21st century has seen an enormous growth in the development and use of approximate Bayesian methods. Such methods produce computational solutions to certain intractable statist…
ABC of the Future
Henri Pesonen, Umberto Simola, Alvaro Köhn-Luque +8
Approximate Bayesian computation (ABC) has advanced in two decades from a seminal idea to a practically applicable inference tool for simulator-based statistical models, which are…
Variational Bayes in State Space Models: Inferential and Predictive Accuracy
David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin
Using theoretical and numerical results, we document the accuracy of commonly applied variational Bayes methods across a range of state space models. The results demonstrate that,…
Loss-Based Variational Bayes Prediction
David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin +1
We propose a new approach to Bayesian prediction that caters for models with a large number of parameters and is robust to model misspecification. Given a class of high-dimensional…
Forecasting: theory and practice
Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos +77
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organ…
Optimal probabilistic forecasts: When do they work?
Gael M. Martin, Rubén Loaiza-Maya, David T. Frazier +2
Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Here…
Computing Bayes: Bayesian Computation from 1763 to the 21st Century
Gael M. Martin, David T. Frazier, Christian P. Robert
The Bayesian statistical paradigm uses the language of probability to express uncertainty about the phenomena that generate observed data. Probability distributions thus characteri…
Focused Bayesian Prediction
Ruben Loaiza-Maya, Gael M. Martin, David T. Frazier
We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defi…
Forecasting observables with particle filters: Any filter will do!
Patrick Leung, Catherine S. Forbes, Gael M. Martin +1
We investigate the impact of filter choice on forecast accuracy in state space models. The filters are used both to estimate the posterior distribution of the parameters, via a par…
Optimal Bias Correction of the Log-periodogram Estimator of the Fractional Parameter: A Jackknife Approach
Kanchana Nadarajah, Gael M Martin, Donald S Poskitt
We use the jackknife to bias correct the log-periodogram regression(LPR) estimator of the fractional parameter in a stationary fractionally integrated model. The weights for the ja…
Approximate Bayesian Forecasting
David T. Frazier, Worapree Maneesoonthorn, Gael M. Martin +1
Approximate Bayesian Computation (ABC) has become increasingly prominent as a method for conducting parameter inference in a range of challenging statistical problems, most notably…
Construction and Visualization of Optimal Confidence Sets for Frequentist Distributional Forecasts
David Harris, Gael M. Martin, Indeewara Perera +1
The focus of this paper is on the quantification of sampling variation in frequentist probabilistic forecasts. We propose a method of constructing confidence sets that respects the…
High-Frequency Jump Tests: Which Test Should We Use?
Worapree Maneesoonthorn, Gael M. Martin, Catherine S. Forbes
We conduct an extensive evaluation of price jump tests based on high-frequency financial data. After providing a concise review of multiple alternative tests, we document the size…
Asymptotic Properties of Approximate Bayesian Computation
David T. Frazier, Gael M. Martin, Christian P. Robert +1
Approximate Bayesian computation allows for statistical analysis in models with intractable likelihoods. In this paper we consider the asymptotic behaviour of the posterior distrib…
Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models
Gael M. Martin, Brendan P. M. McCabe, David T. Frazier +2
A computationally simple approach to inference in state space models is proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an intractable likelihood b…
Bias Correction of Semiparametric Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap
Don S. Poskitt, Gael M. Martin, Simone D. Grose
This paper investigates bootstrap-based bias correction of semiparametric estimators of the long memory parameter, , in fractionally integrated processes. The re-sampling method…
On Consistency of Approximate Bayesian Computation
David T. Frazier, Gael M. Martin, Christian P. Robert
Approximate Bayesian computation (ABC) methods have become increasingly prevalent of late, facilitating as they do the analysis of intractable, or challenging, statistical problems…