Optimal posting price of limit orders: learning by trading

Considering that a trader or a trading algorithm interacting with markets during continuous auctions can be modeled by an iterating procedure adjusting the price at which he posts orders at a given rhythm, this paper proposes a procedure minimizing his costs. We prove the a.s. convergence of the algorithm under assumptions on the cost function and give some practical criteria on model parameters to ensure that the conditions to use the algorithm are fulfilled (using notably the co-monotony principle). We illustrate our results with numerical experiments on both simulated data and using a financial market dataset

Optimal split of orders across liquidity pools: a stochastic algorithm approach

Evolutions of the trading landscape lead to the capability to exchange the same financial instrument on different venues. Because of liquidity issues, the trading firms split large orders across several trading destinations to optimize their execution. To solve this problem we devised two stochastic recursive learning procedures which adjust the proportions of the order to be sent to the different venues, one based on an optimization principle, the other on some reinforcement ideas. Both procedures are investigated from a theoretical point of view: we prove a.s. convergence of the optimization algorithm under some light ergodic (or "averaging") assumption on the input data process. No Markov property is needed. When the inputs are i.i.d. we show that the convergence rate is ruled by a Central Limit Theorem. Finally, the mutual performances of both algorithms are compared on simulated and real data with respect to an "oracle" strategy devised by an "insider" who knows a priori the executed quantities by every venues

Agriculture production versus biodiversity protection: what role for north-south unconditional transfers?

In developing countries, biodiversity is often threatened by the development of agricultural production because natural habitats are destroyed by the conversion of forests, wetlands or natural pastures into arable land. There is a trade-o¤ between revenues generated by agricultural production and services supplied by unspoilt natural capital (ecological services, possibility to develop biodiversity-related tourism activities). Whereas agricultural pro.ts are private, biodiversity produces various types of ben[...].

Facts, Norms and Expected Utility Functions

In this paper we want to explore an argumentative pattern that provides a normative justification for expected utility functions grounded on empirical evidence, showing how it worked in three different episodes of their development. The argument claims that we should prudentially maximize our expected utility since this is the criterion effectively applied by those who are considered wisest in making risky choices (be it gamblers or businessmen). Yet, to justify the adoption of this rule, it should be proven that this is empirically true: i.e., that a given function allows us to predict the choices of that particular class of agents. We show how expected utility functions were introduced and contested in accordance to this pattern in the 18th century and how it recurred in the 1950s when M. Allais made his case against the neobernoullians.Expected utility;Normative theory;

Optimal split of orders across liquidity pools: a stochastic algorithm approach

Evolutions of the trading landscape lead to the capability to exchange the same financial instrument on different venues. Because of liquidity issues, the trading firms split large orders across several trading destinations to optimize their execution. To solve this problem we devised two stochastic recursive learning procedures which adjust the proportions of the order to be sent to the different venues, one based on an optimization principle, the other on some reinforcement ideas. Both procedures are investigated from a theoretical point of view: we prove a.s. convergence of the optimization algorithm under some light ergodic (or "averaging") assumption on the input data process. No Markov property is needed. When the inputs are i.i.d. we show that the convergence rate is ruled by a Central Limit Theorem. Finally, the mutual performances of both algorithms are compared on simulated and real data with respect to an "oracle" strategy devised by an "insider" who knows a priori the executed quantities by every venues.Asset allocation, Stochastic Lagrangian algorithm, reinforcement principle, monotone dynamic system

Sensitivity analysis of a branching process evolving on a network with application in epidemiology

We perform an analytical sensitivity analysis for a model of a continuous-time branching process evolving on a fixed network. This allows us to determine the relative importance of the model parameters to the growth of the population on the network. We then apply our results to the early stages of an influenza-like epidemic spreading among a set of cities connected by air routes in the United States. We also consider vaccination and analyze the sensitivity of the total size of the epidemic with respect to the fraction of vaccinated people. Our analysis shows that the epidemic growth is more sensitive with respect to transmission rates within cities than travel rates between cities. More generally, we highlight the fact that branching processes offer a powerful stochastic modeling tool with analytical formulas for sensitivity which are easy to use in practice.Comment: 17 pages (30 with SI), Journal of Complex Networks, Feb 201

The REDD scheme to curb deforestation: A well-designed system of incentives?

Bioprospection is, largely, meant to help reducing deforestation and, the other way around, stopping deforestation enhances the prospects of bioprospection. The need for a global agreement to the problem of tropical deforestation has led to the REDD (Reducing Emissions from Deforestation and Degradation) scheme, which proposes that developed countries pay developing countries for CO2 emissions saved through avoided deforestation and degradation. The remaining issue at stake is to definer the rules defning payments to countries reducing their deforestation rate. This article develops a game-theoretic bargaining model, simulating the on-going negotiation process which is currently taking place within the Convention of Climate Change, after the Copenhagen agreement of December 2009. It shows that the conditions under which developing countries are left to bargain over the allocation of the global forest fund may lead to an ineffective system of incentives. Below a given level of contributions from the North, the mechanism fails to curb the deforestation. Beyond this level, it induces perverse effects: the larger the North's contribution, the larger the deforestation rate. Consequently, the mechanism is most effective only at a specifc threshold level which, given the unobservability of countries'preferences, can only be found by a repeated "trial and error" implementation process.