Research Papers on Environmental Economics and Finance
Environmental transition alignment and portfolio performance, submitted.
Abstract: We contribute to the debate on whether using ESG/SRI criteria in investment decisions improves portfolio performance. The choice of a specific ESG metric being crucial, we focus on the Net Environmental Contribution, a robust open-source measure of environmental transition alignment. From a universe of 752 European stocks, we select subsets of stocks with high and low NEC scores, and compare the performance of equal-weighted and capitalization-weighted portfolios constructed from these subsets over the 2015-2020 period. The high-NEC portfolios outperform the low-NEC ones consistently throughout the period, and particularly during 18 months starting mid-2019, both before and during the COVID crisis.
Ecological Intuition versus Economic Reason, with Roger Guesnerie and Jean-Michel Lasry, Journal of Public Economic Theory, Volume 14, Issue 2, March 2012.
Abstract: This paper discusses the discount rate to be used in projects aimed at preserving the environment. The model has two different goods: one is the usual consumption good whose production may increase exponentially, and the other is an environmental good whose quality remains limited. The stylized world we describe is fully determined by four parameters, reflecting basic preferences, “ecological” and intergenerational concerns, and feasibility constraints. We define an ecological discount rate and examine its connections with the usual interest rate and the optimized growth rate. We discuss, in this simple world, different forms of the precautionary principle.
Precautionary Principle and the Evaluation of Environmental Policies, in The Economics of Sustainable Development, Ed. Economica, 2010
Abstract: This general readership article proposes an approach to evaluate what an economically and socially acceptable cost can be in the context of policies that aim at improving the environment. We stress the difficulty of considering a pre-determined utility function and propose an unusual methodology which is based on considering a distribution on the space of utility functions. One of our outcomes is a microeconomic foundation of the precautionary principle: the simple fact of hesitating between several utility functions invite to use the most environment-friendly one.
Abstract: We contribute to the debate on whether using ESG/SRI criteria in investment decisions improves portfolio performance. The choice of a specific ESG metric being crucial, we focus on the Net Environmental Contribution, a robust open-source measure of environmental transition alignment. From a universe of 752 European stocks, we select subsets of stocks with high and low NEC scores, and compare the performance of equal-weighted and capitalization-weighted portfolios constructed from these subsets over the 2015-2020 period. The high-NEC portfolios outperform the low-NEC ones consistently throughout the period, and particularly during 18 months starting mid-2019, both before and during the COVID crisis.
Ecological Intuition versus Economic Reason, with Roger Guesnerie and Jean-Michel Lasry, Journal of Public Economic Theory, Volume 14, Issue 2, March 2012.
Abstract: This paper discusses the discount rate to be used in projects aimed at preserving the environment. The model has two different goods: one is the usual consumption good whose production may increase exponentially, and the other is an environmental good whose quality remains limited. The stylized world we describe is fully determined by four parameters, reflecting basic preferences, “ecological” and intergenerational concerns, and feasibility constraints. We define an ecological discount rate and examine its connections with the usual interest rate and the optimized growth rate. We discuss, in this simple world, different forms of the precautionary principle.
Precautionary Principle and the Evaluation of Environmental Policies, in The Economics of Sustainable Development, Ed. Economica, 2010
Abstract: This general readership article proposes an approach to evaluate what an economically and socially acceptable cost can be in the context of policies that aim at improving the environment. We stress the difficulty of considering a pre-determined utility function and propose an unusual methodology which is based on considering a distribution on the space of utility functions. One of our outcomes is a microeconomic foundation of the precautionary principle: the simple fact of hesitating between several utility functions invite to use the most environment-friendly one.
Research Papers on Real-Time Bidding
Real-time bidding strategies with online learning, with J. Fernandez-Tapia and J.-M. Lasry.
Abstract: One critical issue in the control of Markov processes is that, in order to successfully apply dynamic programming tools, the knowledge of the statistical laws governing the system is required. When these laws are difficult to estimate beforehand using historical data, the estimation/calibration task requires to be performed at runtime (this is known as \textit{online learning}). Bayesian inference provides a useful way to address this problem by defining probability distributions for the model parameters and update them with the incoming information. It is particularly relevant in the case of most conjugate Bayesian priors as they preserve the Markovian properties of the model, thus making it possible to apply classical dynamic programming / stochastic control tools. In this study, we apply such a Bayesian approach for the control of a bidding algorithm participating in a high-frequency stream of (Vickrey) auctions, with the aim of maximizing an expected payoff depending on the state at the end of the period. This is of particular interest in real-time bidding (RTB) advertising.
On the pricing of performance-based programmatic ad-buying contracts, with J. Fernandez-Tapia and J.-M. Lasry.
Abstract: In this paper, we provide a mathematical framework for the rigorous pricing and risk management of performance-based programmatic ad-buying contracts. We mainly focus on the case of Real-Time Bidding (RTB) audience strategies, where ad inventory is purchased algorithmically through the participation to a huge number of Vickrey auctions. Our approach is based on stochastic optimal control techniques. It is a general approach in that it makes it possible to consider a broad range of practical situations. In addition to the pricing of ad-buying contracts, we obtain results on both the optimal bidding strategy and the risk associated with each contract, the latter being obtained thanks to Monte Carlo simulations. Besides the mathematical framework itself, our goal is to show that mathematical and numerical tools exist for giving a fair price to performance-based ad-buying contracts -- that are too rare in the industry, as of today -- and to assess and manage the associated risk.
Optimal Real-Time Bidding Strategies, with J. Fernandez-Tapia and J.-M. Lasry, Applied Mathematics Research eXpress, Volume 2017, Issue 1, 2017.
Abstract: The ad-trading desks of media-buying agencies are increasingly relying on complex algorithms for purchasing advertising inventory. In particular, Real-Time Bidding (RTB) algorithms respond to many auctions -- usually Vickrey auctions -- throughout the day for buying ad-inventory with the aim of maximizing one or several key performance indicators (KPI). The optimization problems faced by companies building bidding strategies are new and interesting for the community of applied mathematicians. In this article, we introduce a stochastic optimal control model that addresses the question of the optimal bidding strategy in various realistic contexts: the maximization of the inventory bought with a given amount of cash in the framework of audience strategies, the maximization of the number of conversions/acquisitions with a given amount of cash, etc. In our model, the sequence of auctions is modeled by a Poisson process and the \textit{price to beat} for each auction is modeled by a random variable following almost any probability distribution. We show that the optimal bids are characterized by a Hamilton-Jacobi-Bellman equation, and that almost-closed form solutions can be found by using a fluid limit. Numerical examples are also carried out.
Abstract: One critical issue in the control of Markov processes is that, in order to successfully apply dynamic programming tools, the knowledge of the statistical laws governing the system is required. When these laws are difficult to estimate beforehand using historical data, the estimation/calibration task requires to be performed at runtime (this is known as \textit{online learning}). Bayesian inference provides a useful way to address this problem by defining probability distributions for the model parameters and update them with the incoming information. It is particularly relevant in the case of most conjugate Bayesian priors as they preserve the Markovian properties of the model, thus making it possible to apply classical dynamic programming / stochastic control tools. In this study, we apply such a Bayesian approach for the control of a bidding algorithm participating in a high-frequency stream of (Vickrey) auctions, with the aim of maximizing an expected payoff depending on the state at the end of the period. This is of particular interest in real-time bidding (RTB) advertising.
On the pricing of performance-based programmatic ad-buying contracts, with J. Fernandez-Tapia and J.-M. Lasry.
Abstract: In this paper, we provide a mathematical framework for the rigorous pricing and risk management of performance-based programmatic ad-buying contracts. We mainly focus on the case of Real-Time Bidding (RTB) audience strategies, where ad inventory is purchased algorithmically through the participation to a huge number of Vickrey auctions. Our approach is based on stochastic optimal control techniques. It is a general approach in that it makes it possible to consider a broad range of practical situations. In addition to the pricing of ad-buying contracts, we obtain results on both the optimal bidding strategy and the risk associated with each contract, the latter being obtained thanks to Monte Carlo simulations. Besides the mathematical framework itself, our goal is to show that mathematical and numerical tools exist for giving a fair price to performance-based ad-buying contracts -- that are too rare in the industry, as of today -- and to assess and manage the associated risk.
Optimal Real-Time Bidding Strategies, with J. Fernandez-Tapia and J.-M. Lasry, Applied Mathematics Research eXpress, Volume 2017, Issue 1, 2017.
Abstract: The ad-trading desks of media-buying agencies are increasingly relying on complex algorithms for purchasing advertising inventory. In particular, Real-Time Bidding (RTB) algorithms respond to many auctions -- usually Vickrey auctions -- throughout the day for buying ad-inventory with the aim of maximizing one or several key performance indicators (KPI). The optimization problems faced by companies building bidding strategies are new and interesting for the community of applied mathematicians. In this article, we introduce a stochastic optimal control model that addresses the question of the optimal bidding strategy in various realistic contexts: the maximization of the inventory bought with a given amount of cash in the framework of audience strategies, the maximization of the number of conversions/acquisitions with a given amount of cash, etc. In our model, the sequence of auctions is modeled by a Poisson process and the \textit{price to beat} for each auction is modeled by a random variable following almost any probability distribution. We show that the optimal bids are characterized by a Hamilton-Jacobi-Bellman equation, and that almost-closed form solutions can be found by using a fluid limit. Numerical examples are also carried out.
Research Papers on Decentralized Lending
Agents' Behavior and Interest Rate Model Optimization in Defi Lending, with C. Bertucci, L. Bertucci, M. Gontier Delaunay, and M. Lesbre, submitted
Abstract: Contrasting sharply with traditional money, bond and bond futures markets, where interest rates emerge organically from participant interactions, DeFi lending platforms employ rule-based interest rates that are algorithmically set. Thus, the selection of an effective interest rate model is paramount for the success of a lending protocol. This paper delves into the modeling of agents' behaviors on lending platforms, proposing a theoretical framework for formulating optimal interest rate models. We show that, under perfect information, an optimal control model with a state constraint generates an optimal interest rate policy that has a shape similar to that of popular markets. Furthermore, we formally analyze interest rate policies based on PID controllers, which work efficiently based on fewer assumptions. Using public data of popular markets on the Ethereum blockchain, we analyze agent's behavior, build a realistic simulation environment and highlight the main tradeoffs in the design of interest rates for decentralized lending platforms.
Abstract: Contrasting sharply with traditional money, bond and bond futures markets, where interest rates emerge organically from participant interactions, DeFi lending platforms employ rule-based interest rates that are algorithmically set. Thus, the selection of an effective interest rate model is paramount for the success of a lending protocol. This paper delves into the modeling of agents' behaviors on lending platforms, proposing a theoretical framework for formulating optimal interest rate models. We show that, under perfect information, an optimal control model with a state constraint generates an optimal interest rate policy that has a shape similar to that of popular markets. Furthermore, we formally analyze interest rate policies based on PID controllers, which work efficiently based on fewer assumptions. Using public data of popular markets on the Ethereum blockchain, we analyze agent's behavior, build a realistic simulation environment and highlight the main tradeoffs in the design of interest rates for decentralized lending platforms.
