A Numerical method for the semilinear stochastic transport equation

Trabalho apresentado no XXXV CNMAC, Natal-RN, 2014.

Computer simulation of the stochastic transport equation

Trabalho apresentado no 37th Conference on Stochastic Processes and their Applications - July 28 - August 01, 2014 -Universidad de Buenos Aires

Numerical integration of coupled stochastic oscillators driven by Random Forces

Trabalho apresentado no International Conference on Scientific Computation And Differential Equations 2015

Convergence analysis of sampling-based decomposition methods for risk-averse multistage stochastic convex programs

We consider a class of sampling-based decomposition methods to solve risk-averse multistage stochastic convex programs. We prove a formula for the computation of the cuts necessary to build the outer linearizations of the recourse functions. This formula can be used to obtain an efficient implementation of Stochastic Dual Dynamic Programming applied to convex nonlinear problems. We prove the almost sure convergence of these decomposition methods when the relatively complete recourse assumption holds. We also prove the almost sure convergence of these algorithms when applied to risk-averse multistage stochastic linear programs that do not satisfy the relatively complete recourse assumption. The analysis is first done assuming the underlying stochastic process is interstage independent and discrete, with a finite set of possible realizations at each stage. We then indicate two ways of extending the methods and convergence analysis to the case when the process is interstage dependent.

Non-asymptotic confidence bounds for the optimal value of a stochastic program

We discuss a general approach to building non-asymptotic confidence bounds for stochastic optimization problems. Our principal contribution is the observation that a Sample Average Approximation of a problem supplies upper and lower bounds for the optimal value of the problem which are essentially better than the quality of the corresponding optimal solutions. At the same time, such bounds are more reliable than “standard” confidence bounds obtained through the asymptotic approach. We also discuss bounding the optimal value of MinMax Stochastic Optimization and stochastically constrained problems. We conclude with a small simulation study illustrating the numerical behavior of the proposed bounds.

Stochastic discount factor bounds and rare events: a review

We aim to provide a review of the stochastic discount factor bounds usually applied to diagnose asset pricing models. In particular, we mainly discuss the bounds used to analyze the disaster model of Barro (2006). Our attention is focused in this disaster model since the stochastic discount factor bounds that are applied to study the performance of disaster models usually consider the approach of Barro (2006). We first present the entropy bounds that provide a diagnosis of the analyzed disaster model which are the methods of Almeida and Garcia (2012, 2016); Ghosh et al. (2016). Then, we discuss how their results according to the disaster model are related to each other and also present the findings of other methodologies that are similar to these bounds but provide different evidence about the performance of the framework developed by Barro (2006).

Flexible information acquisition and optimal Tobin tax in tractable dynamic global games

My dissertation focuses on dynamic aspects of coordination processes such as reversibility of early actions, option to delay decisions, and learning of the environment from the observation of other people’s actions. This study proposes the use of tractable dynamic global games where players privately and passively learn about their actions’ true payoffs and are able to adjust early investment decisions to the arrival of new information to investigate the consequences of the presence of liquidity shocks to the performance of a Tobin tax as a policy intended to foster coordination success (chapter 1), and the adequacy of the use of a Tobin tax in order to reduce an economy’s vulnerability to sudden stops (chapter 2). Then, it analyzes players’ incentive to acquire costly information in a sequential decision setting (chapter 3). In chapter 1, a continuum of foreign agents decide whether to enter or not in an investment project. A fraction λ of them are hit by liquidity restrictions in a second period and are forced to withdraw early investment or precluded from investing in the interim period, depending on the actions they chose in the first period. Players not affected by the liquidity shock are able to revise early decisions. Coordination success is increasing in the aggregate investment and decreasing in the aggregate volume of capital exit. Without liquidity shocks, aggregate investment is (in a pivotal contingency) invariant to frictions like a tax on short term capitals. In this case, a Tobin tax always increases success incidence. In the presence of liquidity shocks, this invariance result no longer holds in equilibrium. A Tobin tax becomes harmful to aggregate investment, which may reduces success incidence if the economy does not benefit enough from avoiding capital reversals. It is shown that the Tobin tax that maximizes the ex-ante probability of successfully coordinated investment is decreasing in the liquidity shock. Chapter 2 studies the effects of a Tobin tax in the same setting of the global game model proposed in chapter 1, with the exception that the liquidity shock is considered stochastic, i.e, there is also aggregate uncertainty about the extension of the liquidity restrictions. It identifies conditions under which, in the unique equilibrium of the model with low probability of liquidity shocks but large dry-ups, a Tobin tax is welfare improving, helping agents to coordinate on the good outcome. The model provides a rationale for a Tobin tax on economies that are prone to sudden stops. The optimal Tobin tax tends to be larger when capital reversals are more harmful and when the fraction of agents hit by liquidity shocks is smaller. Chapter 3 focuses on information acquisition in a sequential decision game with payoff complementar- ity and information externality. When information is cheap relatively to players’ incentive to coordinate actions, only the first player chooses to process information; the second player learns about the true payoff distribution from the observation of the first player’s decision and follows her action. Miscoordination requires that both players privately precess information, which tends to happen when it is expensive and the prior knowledge about the distribution of the payoffs has a large variance.