Accepted and Published papers

Working Papers

• Inference for Two-Stage Extremum Estimators - with Elysée Aristide Houndetoungan

  We present a simulation-based approach to approximate the asymptotic variance and asymptotic distribution function of two-stage estimators. We focus on extremum estimators in the second stage and consider a large class of estimators in the first stage. This class includes extremum estimators, high-dimensional estimators, and other types of estimators (e.g., Bayesian estimators). Importantly, the asymptotic distributions of both the first- and second-stage estimators may not be normal. Unlike resampling methods, our approach does not require multiple computations of the plug-in estimator. We show the effectiveness of our method using numerical simulations and empirical applications on real data.

  Paper Online Appendix Code

• Multifractal Discrete Stochastic Volatility, MDSV - with Maciej Augustyniak and Arnaud Dufay (Draft available upon request)

  Regime-switching processes are popular tools to interpret, model and forecast financial data. The Markov-switching multifractal (MSM) model has proved to be a strong competitor to the GARCH class of models for modeling the volatility of returns. In this model, volatility dynamics are driven by a latent high-dimensional Markov chain constructed by multiplying independent twostate Markov chains. We propose the multifractal discrete stochastic volatility (MDSV) model as a generalization of the MSM process and of other related high-dimensional hidden Markov models. Our model is intended to jointly model financial returns and realized volatilities, and therefore also extends existing high-dimensional Markov-switching processes to the joint setting. Our approach consists in building a high-dimensional Markov chain by the product of lower-dimensional Markov chains which have a discrete stochastic volatility representation. The properties and structure of our model are studied theoretically and it is shown that the MDSV process can be interpreted as a multi-component stochastic volatility model. An empirical study on 31 financial time series shows that the MDSV model can improve upon the realized EGARCH model in terms of fit and forecasting performance.

  Slides R Package

• Lockdowns lifting: Thanks to governments COVID-19 measures or to good weather? What do data say? ”, 2020 - with Chaffa Odjouwoni Lucien Chaffa and Idossou Marius Adom

  This paper investigates empirically the effects of climate and governments’ responses on the spread of the COVID-19. Our model is based on an accounting equation derived from the SIR model, and estimates the relationship between the growth of the daily COVID-19 confirmed cases on the one hand, and climatic variables (such as the daily average temperature and the wind speed) and governments responses to COVID-19 on the other hand. We also develop a theoretical approach to test the presence of a threshold in the effect of the temperature on the COVID-19 spread.

  Paper Code(R)

Work In Progress

• Green Portfolio Optimisation using Reinforcement Learning (with Frédéric Godin and Zaniar Ahmadi)

• Minimizing CVaR in Global dynamic Hedging under Asymetric Laplace (with Ismael Affolabi Assani)