Arthur Thomas

• Theoretical Econometrics
  • Bayesian Econometrics
  • Noncausal Econometrics
  • Machine Learning
• Applied Econometrics
  • Financial Econometrics
  • Energy Economics

I am currently a Teaching Fellow for Computer Science in ENSAE Paris and a CREST affiliated member. I am also Associated researcher in the Climate Economics Chair and in Chair of the Economics of Natural Gas. I am a graduate in statistical engineering from the National School for Statistics and Data Analysis (ENSAI). In 2017, to further pursue my academic interest in econometrics, I accepted a PhD scholarship from the IFP School and the University of Nantes under the supervision of Oliver Massol, Associate Professor at IFP School and City, University of London and Benoît Sèvi, Professor of Economics, University of Nantes. I defend my PhD in December 2020. The member of my committee was:

• Karim Abadir, Professor of Financial Econometrics, Imperial College London (Reviewers)
• Derek Bunn, Professor of Decision Sciences, London Business School (Examiners)
• Dimitris Korobilis, Professor of Econometrics, University of Glasgow (Reviewers)
• Valérie Mignon, Professeur des Universités, Université Paris Nanterre (Examiners)

I have presented my research in international conferences, in different fields, Operations Research, Econometrics, Finance and Energy.

Positions

• CREST-ENSAE Affiliated Member (2020- )
• Teaching Fellow for Computer Science in ENSAE Paris (2020- )
• Researcher associated in the chair of the Climate Economics Chair (2021- )
• Researcher associated in the chair of the Economics of Natural Gas (2017- )
• Researcher associated in “Les Jeunes Economètres” (2017- )

Education

• PhD in Economics University of Nantes/ IFP School 2017 – 2020
  Read More

  • Tilte: The Econometrics of Energy Demand: Identification and Forecast
  • Advisors: Benoît Sévi (Professeur, Université de Nantes)
  • Co-Advisors: Olivier Massol (Associate professor, City, University of London & IFP School)
  • Committee:
    • Karim Abadir (Professor of Financial Econometrics, Imperial College London, External Reviewer)
    • Derek Bunn (Professor of Decision Sciences, London Business School)
    • Dimitris Korobilis (Professor of Econometrics, University of Glasgow, External Reviewer)
    • Valérie Mignon (Professeure des Universités, Université Paris Nanterre)

References

Publications

• Are day-ahead prices enough to explain and predict the next day’s natural gas demand? Evidences from the French case, with Olivier Massol and Benoît Sévi (2020) forthcoming in The Energy Journal. (Link to article)
  Abstract

  The purpose of this paper is to investigate, for the first time, whether the next day’sconsumption of natural gas can be accurately forecast using a simple model that solely incorporates theinformation contained in day-ahead market data. Hence, unlike standard models that use a numberof meteorological variables, we only consider two predictors: the price of natural gas and the sparkratio measuring the relative price of electricity to gas. We develop a suitable modeling approach thatcaptures the essential features of daily gas consumption and, in particular, the nonlinearities resultingfrom power dispatching and apply it to the case of France. Our results document the existence of along-run relation between demand and spot prices and provide estimates of the marginal impacts thatthese price variables have on observed demand levels. We also provide evidence of the pivotal role ofthe spark ratio in the short run which is found to have an asymmetric and highly nonlinear impact ondemand variations. Lastly, we show that our simple model is sufficient to generate predictions thatare considerably more accurate than the forecasts published by infrastructure operators

Working papers

• Bet on a bubble asset ? An optimal portfolio allocation strategy (2022, draft available)
  Abstract

  Abstract: We discuss portfolio allocation when one asset exhibits phases of locally explosive behavior. We model the conditional distribution of such an asset through mixed causal-non-causal models which mimic well the speculative bubble behaviour. Relying on a Taylor-series-expansion of a CRRA utility function approach, the optimal portfolio(s) is(are) located on the mean-variance-skewness-kurtosis efficient surface. We analytically derive these four conditional moments and show in a Monte-Carlo simulations exercise that incorporating them into a two-assets portfolio optimization problem leads to substantial improvement in the asset allocation strategy. All performance evaluation metrics support the higher out-of-sample performance of our investment strategies over standard benchmarks such as the mean-variance and equally-weighted portfolio. An empirical illustration using the Brent oil price as the speculative asset confirms these findings.
• Identifying oil supply news shocks and their effects on the global oil market with Zakaria Moussa (2021, submitted) (Link to article)
  Abstract

  This paper uses a new empirical strategy to identify oil supply news shocks within a Non-Causal VAR model of standard global oil market variables. These shocks explain most of the movements in oil production over a long but finite time horizon. Our findings highlight the prominent role of expectations in propagating shocks. Negative oil supply news shocks cause abrupt and permanent reactions in global oil production, global economic activity and in oil inventory. However, an oil supply shock has only a limited effect on oil price. Finally, a news shock regarding oil supply shortfalls has macroeconomic consequences, causing a substantial decline in US industrial production.
• Real-time demand in U.S. natural gas price forecasting: the role of temperature data, with Benoît Sévi and Zakaria Moussa (2020, under review) (Link to article)
  Abstract

  This paper provides evidence of the pivotal role temperature data can play in forecasting natural gas prices at the Henry Hub in real time. Considering a newly constructed temperature index as an additional exogenous variable in a Bayesian vector autoregressive (BVAR) framework significantly increases forecast accuracy at horizons of up to 12 months. Our novel approach to energy price forecasting simultaneously considers both supply and demand and incorporates temperature data as a proxy of real-time demand for natural gas.
• The role of expectations in predicting the real prices of oil: a non-causal analysis (2021, draft available)
  Abstract

  This paper revisits the predictive power of convenience yield for oil by incorporating expectations into an empirical specification through the estimation of Bayesian non-causal VAR. We empirically show that expectations play a significant role in the determination of oil prices. Second, we provide empirical evidence that real-time forecasts of real oil prices can be remarkably more accurate than the no-change forecast and significantly more accurate than real-time forecasts generated by existing structural models relying on Bayesian VAR. Beyond the traditional analysis at the monthly frequency, we further investigate the forecasting accuracy of our empirical specification at the daily and weekly frequency, resulting in interesting findings for potential investment purpose.

Work in progress

• Machine learning approach in predicting α-stable noncausal processes with Fréderic Logé (2022)
• Fractional frequency domain minimum distance inference for possibly noninvertible and non causal arma models (2022)
• Who refines oil and why: disentangling investment decision from countries and companies, with Olivier Massol and Quentin Hoareau (2022)

Academic conferences

2021

• 7th RCEA Times series workshop, University of Milano-Bicocca.
• 20 eme Journée d’Econométrie, Développements Récents de l’Econométrie Appliquée
• à la Finance, EconomiX, Nanterre, France (Discussion).

2020

• Thé des économètres, Paris, France.
• 37th International Conference of the French Finance Association (AFFI), Nantes, France.
• 19èmeJournée d’Économétrie, Développements Récents de l’Econométrie Appliquée àla Finance, EconomiX, Nanterre, France.
• 2nd International Conference Environmental Economics: A Focus on Natural Re-sources,University of Orleans.

2019

• 13th International Conference on Computational and Financial Econometrics, London, UK
• INFORMS Annual meeting 2019, Seattle, USA
• 13th Annual Trans-Atlantic Infraday Conference, Washington, USA
• 18ème Journée d’Économétrie, Développements Récents de l’Econométrie Appliquée à la Finance, EconomiX, Nanterre, France.
• Séminaire CREST-ENSAI 2019, Rennes, France.
• Thé des économètres, Orléans, France.
• Workshop in Financial Econometrics, Nantes, France.
• The 3rd Commodity Markets Winter Workshop-Leibniz University, Hannover, Germany
• Workshop EDGE 2019, Rennes, France
• The 2nd International Conference The Economics of Natural Gas, University Paris-Dauphine, Paris, France.

2018

• 12th International Conference on Computational and Financial Econometrics, Pisa, Italy
• 41st edition of the IAEE international conference, Groningen, Netherland
• FAEE summer workshop, Mines ParisTech, Paris, France
• 29th European Conference On Operational Research. Valencia, Spain
• INFORMS 2018 Annual Meeting Phoenix, USA
• 11thAnnual Trans-Atlantic Infraday Conference, Washington, USA
• Commodities and Energy Market Organization in the Energy Transition Context, IFP Energies nouvelles, Reuil-Malmaison, France

Other research activities

Refereeing activities

Annals of economics and statistics , Energy Journal, Energy Economics, Journal of Banking and Finance

Conference organization

• 43rd IAEE International Conference, Paris, France.
• 37th International Conference of the French Finance Association (AFFI), Nantes, France

2021-

Teaching assistant of Deep Learning: Models and Optimization, Graduate level, ENSAE. 6h (In French)

Machine learning for econometrics, Graduate level, M2-EEET: Université Paris-Saclay, l’Université Paris Nanterre , l’IFP-School, l’Ecole des Ponts ParisTech. L’Ecole des Mines ParisTech. 18h (In French)

2020-

Coordinator of computer science courses, Graduate level, ENSAE.

Introduction to the Python Computer Language, Graduate level, ENSAE.

Introduction to the R Computer Language, Graduate level, ENSAE.

2019

Applied Econometrics, 1st year, Pantheon-Sorbonne Master In Economics, Université Paris 1 Panthéon-Sorbonne. 48h (in English)

2017-2018

Energy econometrics, Master of Science in Statistics: Risk Management and Financial Engineering Specialization, “Diplôme d’ingénieur – France’s Grandes écoles”, ENSAI. 8h (In French)

2017

Times series modeling, 1st year Bachelor Statistique et Informatique Décisionnelle (STID), Université Paris-Descartes. 54h (In French)