Bertrand TAVIN

"In this article, we propose a methodology for measuring the information flows that underpin option price movements and for analyzing the distribution of these flows. We develop a framework in which information flows can be measured in terms of the relative entropy between the risk-neutral distributions obtained from implied volatility data at different dates. We set up a numerical methodology to compute such quantities using an empirical market dataset that corresponds to options written on the S&P 500 index. This methodology uses Normal Inverse Gaussian distributions for the log-return of the index. We apply our method to six years of daily data, from 2015 to 2021, and find that options with short maturities capture a greater share of new information. We also use a mixture of two exponential distributions to analyze the distribution of the information flows obtained. In this mixture, one component corresponds to frequent small values and the other to less frequent high values."

The aim of this paper is to study the performance of carbon-based portfolios when all emissions scopes are accounted for. We formalize low-carbon mean-variance portfolio strategies by integrating a carbon penalty to a constrained mean-variance optimization framework. We resort to direct and indirect emissions, split between Scopes 1–2 and Scope 3, across geographical zones (Europe and US) and data providers (Refinitiv and Carbon4 Finance). Our results show that it is possible to cut emission intensities in half at least with virtually no loss in Sharpe ratio for reasonable levels of the carbon constraint. These results are valid across various choices of risk aversions, and irrespective of emissions data provider. For a sustainability-aware investor, these low-carbon portfolios are associated to a higher level of welfare. We find that the corresponding allocations are shifted towards assets with higher returns while keeping the portfolio's volatility unchanged. Our results add to the literature contending that sustainable investing is not costly.

This paper considers the problem of measuring the exposure to dependence risk carried by a portfolio with an arbitrary number of two-asset derivative contracts. We develop a worst-case risk measure com- puted over a set of dependence scenarios within a divergence restricted region. The set of dependence scenarios corresponds to Bernstein copulas obtained by simulating random doubly stochastic matrices. We then devise a method to compute hedging positions when a limited number of hedging instruments are available for trading. In an empirical study, we show how the proposed method can be used to re- veal an exposure to dependence risk where usual sensitivity methods fail to reveal it. We also illustrate the ability of the proposed method to generate parsimonious hedging strategies in order to reduce the exposure to dependence risk of a given portfolio.

Our first aim in this paper is to introduce a futures-based model able of capturing the main features displayed by Crude Oil futures and options contracts, such as the Samuelson volatility effect and the volatility smile. We calculate the joint characteristic function of two futures contracts in the model in analytic form and use it to price calendar spread options. In an empirical application we show that the model, in contrast to simpler nested models, can be successfully calibrated to market prices of vanilla and calendar spread options. Our second aim is to use this model to analyze the dependence structure of Crude Oil futures contracts. To this end, we propose analytical expressions giving the copula and copula density directly in terms of the joint characteristic function. These tools allow us to perform an in-depth analysis for pairs of futures, and we observe a phenomenon we call the Samuelson correlation effect.

The aim of this paper is to investigate model risk aspects of variance swaps and forward-start options in a realistic market setup where the underlying asset price process exhibits stochastic volatility and jumps. We devise a general framework in order to provide evidence of the model uncertainty attached to variance swaps and forward-start options. In our study, both variance swaps and forward-start options can be valued by means of analytic methods. We measure model risk using a set of 21 models embedding various dynamics with both continuous and discontinuous sample paths. To conduct our empirical analysis, we work with two major equity indices (S&P 500 and Eurostoxx 50) under different market situations. Our results evaluate model risk between 50 and 200 basis points, with an average value slightly above 100 basis points of the contract notional.