Publications

Abstract Drift and volatility are two mainsprings of asset price dynamics. While volatilities have been studied extensively in the literature, drifts are commonly believed to be impossible to estimate and largely ignored in the literature. This paper shows how to detect drift using realized autocovariance implemented on high-frequency data. We use a theoretical treatment in which the classical model for the efficient price, an Itō semimartingale possibly contaminated by microstructure noise, is enriched with drift and volatility explosions. Our theory advocates a novel decomposition for realized variance into a drift and a volatility component, which leads to significant improvements in volatility forecasting.
Keywords Volatility Forecasting, Serial Covariance, High-frequency Data, Drift
Publication HAL Google Scholar
Abstract The Geographically and Temporally Weighted Regression (GTWR) model is a well-established local technique for analyzing spatial heterogeneity and temporal dependence in georeferenced data. It is recognized for its ability to represent real-world settings. In this study, we expand upon the GTWR model by incorporating spatio-temporal noise that is colored in space and fractional in time. Under this formulation, we derive the Weighted Least Squares (WLS) estimator and formally establish its convergence rate. To evaluate the performance of the WLS estimator, we implemented a simulation study with five defined scenarios. The simulation results indicate that the model residuals exhibit small variations around zero, which suggests the accuracy of the estimator. Finally, we applied the estimator to real data on the incidence of respiratory diseases. Analyzing the residuals in this empirical application allows us to evaluate the ability of the model to capture the spatio-temporal structure of the data.
Keywords Consistency, Fractional Colored Noise, Geographically and Temporally Weighted Regression
Publication HAL Google Scholar
Abstract Not all barrels of oil are created equal: their extraction varies in both private cost and carbon intensity. Leveraging a comprehensive micro-dataset on world oil fields, alongside detailed estimates of carbon intensities and private extraction costs, this study quantifies the additional emissions and costs from having extracted the “wrong” deposits. We do so by comparing historical deposit-level supplies to counterfactuals that factor in pollution costs, while keeping annual global consumption unchanged. Between 1992 and 2018, carbon misallocation amounted to at least 11.00 gigatons of CO2-equivalent (GtCO2eq), incurring an environmental cost evaluated at $2.2 trillion (US$ 2018). This translates into a significant supply-side ecological debt for major producers of high-carbon oil. Looking forward, we estimate the gains from making deposit-level extraction socially optimal at about 9.30 GtCO2eq, valued at $1.9 trillion, along a future aggregate demand pathway coherent with the objective of net-zero emissions in 2050, and document unequal reserve stranding across oil nations.
Keywords Misallocation, Stranded assets, Carbon mitigation, Oil
Publication HAL Google Scholar