Dynamic hedging with stocastic differential utility

In this paper we study the dynamic hedging problem using three different utility specifications: stochastic differential utility, terminal wealth utility, and we propose a particular utility transformation connecting both previous approaches. In all cases, we assume Markovian prices. Stochastic differential utility, SDU, impacts the pure hedging demand ambiguously, but decreases the pure speculative demand, because risk aversion increases. We also show that consumption decision is, in some sense, independent of hedging decision. With terminal wealth utility, we derive a general and compact hedging formula, which nests as special all cases studied in Duffie and Jackson (1990). We then show how to obtain their formulas. With the third approach we find a compact formula for hedging, which makes the second-type utility framework a particular case, and show that the pure hedging demand is not impacted by this specification. In addition, with CRRA- and CARA-type utilities, the risk aversion increases and, consequently the pure speculative demand decreases. If futures price are martingales, then the transformation plays no role in determining the hedging allocation. We also derive the relevant Bellman equation for each case, using semigroup techniques.

Nonlinear pricing beyond the demand profile approach

Wilson [16] introduced a general methodology to deal with monopolistic pricing in situations where customers have private information on their tastes (‘types’). It is based on the demand profile of customers: For each nonlinear tariff by the monopolist the demand at a given level of product (or quality) is the measure of customers’ types whose marginal utility is at least the marginal tariff (‘price’). When the customers’ marginal utility has a natural ordering (i.e., the Spence and Mirrlees Condition), such demand profile is very easy to perform. In this paper we will present a particular model with one-dimensional type where the Spence and Mirrlees condition (SMC) fails and the demand profile approach results in a suboptimal solution for the monopolist. Moreover, we will suggest a generalization of the demand profile procedure that improves the monopolist’s profit when the SMC does not hold.

Adições à proposta do modelo SUSEP de risco de mercado

Seguindo a tendência mundial de um melhor gerenciamento de riscos, o regulador do mercado de seguros brasileiro, após a implementação dos demais riscos, está em fase avançada de desenvolvimento de seu modelo para aferir o risco de mercado das seguradoras. Uma vez que as discussões cessem, as empresas serão forçadas a usar um modelo que, hoje, apresenta muitas falhas, gerando uma demanda de capital adicional de seus acionistas que pode levar algumas delas ao estado de insolvência. O principal objetivo deste estudo é analisar a adequação do modelo e subsidiar a discussão a fim de aperfeiçoar o modelo final, com análises comparativas com outros modelos no país e no mundo, estudo de cenários e visões do mercado. De modo geral, as análises feitas revelam problemas sérios no modelo, como necessidade de aporte de capital em empresas extremamente lucrativas e insuficiência de garantia de segurança pelo uso puro dos fatores de choque em detrimento a uma análise estocástica. Finalmente, são sugeridas algumas soluções para minimizar o efeito da inadequação do modelo e ainda algumas sugestões para melhoria do mesmo, de forma que os acionistas não sejam prejudicados, o regulador consiga administrar adequadamente os riscos e a sociedade seja beneficiada pela solidez das companhias em quem confiou seus riscos.

Aggregate vs disaggregate data analysis: a paradox in the estimation of money demand function of Japan under the low interest rate policy

We investigate the issue of whether there was a stable money demand function for Japan in 1990's using both aggregate and disaggregate time series data. The aggregate data appears to support the contention that there was no stable money demand function. The disaggregate data shows that there was a stable money demand function. Neither was there any indication of the presence of liquidity trapo Possible sources of discrepancy are explored and the diametrically opposite results between the aggregate and disaggregate analysis are attributed to the neglected heterogeneity among micro units. We also conduct simulation analysis to show that when heterogeneity among micro units is present. The prediction of aggregate outcomes, using aggregate data is less accurate than the prediction based on micro equations. Moreover. policy evaluation based on aggregate data can be grossly misleading.

Demand expectations and the timing of stimulus policies

This paper proposes a simple macroeconomic model with staggered investment decisions. The model captures the dynamic coordination problem arising from demand externalities and fixed costs of investment. In times of low economic activity, a firm faces low demand and hence has less incentives for investing, which reinforces firms’ expectations of low demand. In the unique equilibrium of the model, demand expectations are pinned down by fundamentals and history. Owing to the beliefs that arise in equilibrium, there is no special reason for stimulus at times of low economic activity.

Bias from ignoring price dispersion in demand estimation

Consumers often pay different prices for the same product bought in the same store at the same time. However, the demand estimation literature has ignored that fact using, instead, aggregate measures such as the “list” or average price. In this paper we show that this will lead to biased price coefficients. Furthermore, we perform simple comparative statics simulation exercises for the logit and random coefficient models. In the “list” price case we find that the bias is larger when discounts are higher, proportion of consumers facing discount prices is higher and when consumers are more unwilling to buy the product so that they almost only do it when facing discount. In the average price case we find that the bias is larger when discounts are higher, proportion of consumers that have access to discount are similar to the ones that do not have access and when consumers willingness to buy is very dependent on idiosyncratic shocks. Also bias is less problematic in the average price case in markets with a lot of bargain deals, so that prices are as good as individual. We conclude by proposing ways that the econometrician can reduce this bias using different information that he may have available.

Avaliação das Opções Reais de Conversão e Abandono para Uma Mineradora de Minério de Ferro Sob Cenário de Stress

O mercado de minério de ferro tem passado por um período de stress nos últimos meses. O arrefecimento dos investimentos chineses em infraestrutura resultou em perspectivas negativas para a demanda dessa commodity. Paralelamente, a entrada em operação de novos projetos com volume de produção relevante aumentou a oferta desse produto no mercado. Essa conjuntura de fatores resultou na queda do preço do minério de ferro no mercado mundial e em um cenário de retornos reduzidos para as mineradoras. Nesse contexto, o objetivo do presente estudo é avaliar a flexibilidade gerencial, disponível aos administradores de mineradoras operacionais, de suspender ou fechar o empreendimento dependendo do preço do minério de ferro. Essas decisões serão estudadas através da Teoria das Opções Reais, onde a opção de conversão será aplicada na situação de suspensão e reabertura da mina e a opção de abandono será aplicada na situação do seu fechamento. O processo estocástico a ser seguido pelo preço do minério de ferro será o Movimento Geométrico Browniano, implementado através de um Modelo Binomial conforme proposto por Cox, Ross e Rubinstein (1979). O resultado do trabalho comprova o valor das opções reais estudadas e indica que essas opções reais têm maior valor em cenários de stress, quando o preço do minério de ferro está desvalorizado.

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

Microinsurance: income risk, social security and the demand for private onsurance by low-income families

Insurance provision against uncertainties is present in several dimensions of peoples´s lives, such as the provisions related to, inter alia, unemployment, diseases, accidents, robbery and death. Microinsurance improves the ability of low-income individuals to cope with these risks. Brazil has a fairly developed financial system but still not geared towards the poor, especially in what concerns the insurance industry. The evaluation of the microinsurance effects on well-being, and the demand for different types of microinsurance require an analysis of the dynamics of the individual income process and an assessment of substitutes and complementary institutions that condition their respective financial behavior. The evaluation of the microinsurance effects on well-being, and the demand for different types of microinsurance require an analysis of the dynamics of the individual income process and an assessment of substitutes and complementary institutions that condition their respective financial behavior. The Brazilian government provides a relatively developed social security system considering other countries of similar income level which crowds-out the demand for insurance and savings. On the other hand, this same public infrastructure may help to foster microfinance products supply. The objective of this paper is to analyze the demand for different types of private insurance by the low-income population using microdata from a National Expenditure Survey (POF/IBGE). The final objective is to help to understand the trade-offs faced for the development of an emerging industry of microinsurance in Brazil.

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.

Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures

We consider risk-averse convex stochastic programs expressed in terms of extended polyhedral risk measures. We derive computable con dence intervals on the optimal value of such stochastic programs using the Robust Stochastic Approximation and the Stochastic Mirror Descent (SMD) algorithms. When the objective functions are uniformly convex, we also propose a multistep extension of the Stochastic Mirror Descent algorithm and obtain con dence intervals on both the optimal values and optimal solutions. Numerical simulations show that our con dence intervals are much less conservative and are quicker to compute than previously obtained con dence intervals for SMD and that the multistep Stochastic Mirror Descent algorithm can obtain a good approximate solution much quicker than its nonmultistep counterpart. Our con dence intervals are also more reliable than asymptotic con dence intervals when the sample size is not much larger than the problem size.

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).