Coleção Insper Business and Economics Working Papers

URI permanente para esta coleçãohttps://repositorio.insper.edu.br/handle/11224/5740

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Autor: search.filters.author.HEDIBERT FREITAS LOPESData inicial: 2016

Resultados da Pesquisa

Agora exibindo 1 - 5 de 5
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    Working Paper
    Rational Sunspots
    (2016) Ascari, Guido; Banomolo, Paolo; HEDIBERT FREITAS LOPES
    The instability of macroeconomic variables is usually ruled out by rational expectations. We propose a generalization of the rational expectations framework to estimate possible temporary unstable paths. Our approach yields drifting parameters and stochastic volatility. The methodology allows the data to choose between diferent possible alternatives: determinacy, indeterminacy and instability. We apply our methodology to US inflation dynamics in the '70s through the lens of a simple New Keynesian model. When unstable RE paths are allowed, the data unambiguously select them to explain the stagflation period in the '70s. Thus, our methodology suggests that US inflation dynamics in the '70s is better described by unstable rational equilibrium paths.
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    Working Paper
    Semi-parametric inference for the means of heavy-tailed distributions
    (2016) Taddy, Matt; HEDIBERT FREITAS LOPES; Goldberg, David; Gardner, Matt
    Heavy tailed distributions present a tough setting for inference. They are also common in industrial applications, particularly with Internet transaction datasets, and machine learners often analyze such data without considering the biases and risks associated with the misuse of standard tools. This article outlines a procedure for inference about the (possibly conditional) mean of a heavy tailed distribution that combines nonparametric inference for the bulk of the support with parametric inference – motivated from extreme value theory – for the heavy tail. We are able to derive analytic posterior conditional means and variances for the expected value of a heavy tailed distributivo. We also introduce a simple and novel independence Metropolis Hastings algorithm that samples from the distribution for tail parameters via small adjustments to a parametric bootstrap, and through this algorithm are able to provide comparisons between our framework and frequentist semiparametric inference. We also provide a modeling extension that shrinks tails across distributions to an overall background tail. We illustrate on two examples: treatment effect estimation on a set of 72 A/B experiments, and the fitting of regression trees for prediction of user spending. Both use data from tens of millions of users of eBay.com.
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    Working Paper
    Cholesky Realized Stochasti Volatility Model
    (2016) Shirota, Shinichiro; Omori, Yashiro; HEDIBERT FREITAS LOPES; Piao, Haixiang
    Multivariate stochastic volatility models with leverage are expected to play important roles in financial applications such as asset allocation and risk management. However, these models suffer from two major difficulties: (1) there are too many parameters to estimate using only daily asset returns and (2) estimated covariance matrices are not guaranteed to be positive definite. Our approach takes advantage of realized covariances to attain the efficient estimation of parameters by incorporating additional information for the co-volatilities, and considers Cholesky decomposition to guarantee the positive definiteness of the covariance matrices. In this framework, we propose a flexible modeling for stylized facts of financial markets such as dynamic correlations and leverage effects among volatilities. Taking a Bayesian approach, we describe Markov Chain Monte Carlo implementation with a simple but efficient sampling scheme. Our model is applied to nine U.S. stock returns data, and the model comparison is conducted based on portfolio performances.
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    Working Paper
    Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models
    (2016) Kastner, Gregor; Frühwirth-Schnatter, Sylvia; HEDIBERT FREITAS LOPES
    We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate data set illustrates the superior performance of the new approach for real-world data.
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    Working Paper
    General Equilibrium Option Pricing under Counter-Cyclical Growth and Long-Run Risk
    (2016) Hore, Satadru; HEDIBERT FREITAS LOPES; McCulloch, Robert
    Put option prices are counter-cyclical. We build a general equilibrium model based on Duffie-Epstein preferences and Ak production function that delivers a model of put option prices that captures both time-series and cross-sectional properties of relative put option prices. When estimated with US aggregate consumption data and S&P 500 index options using Bayesian MCMC, we confirm our theory that agents have elasticity of intertemporal substitution greater than 1 which confirms the substitution effect, and put option prices reveal the underlying counter-cyclical economic state. The underlying economic dynamics, when combined with long-run risk nature of Duffie-Epstein preferences, can match the time-series and cross-section of US option prices with our theory.