Essays in financial economics

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2018.

Bibliographic Details
Main Author: Duarte, Victor (Fonseca Duarte)
Other Authors: Adrien Verdelhan.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2018
Subjects:
Online Access:http://hdl.handle.net/1721.1/118016
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author Duarte, Victor (Fonseca Duarte)
author2 Adrien Verdelhan.
author_facet Adrien Verdelhan.
Duarte, Victor (Fonseca Duarte)
author_sort Duarte, Victor (Fonseca Duarte)
collection MIT
description Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2018.
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spelling mit-1721.1/1180162019-04-10T13:16:07Z Essays in financial economics Duarte, Victor (Fonseca Duarte) Adrien Verdelhan. Sloan School of Management. Sloan School of Management. Sloan School of Management. Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2018. Cataloged from PDF version of thesis. Includes bibliographical references. This thesis consists of three chapters on asset pricing, dynamic stochastic general equilibrium and structural estimation of dynamic models. Chapter 1 introduces a global, nonlinear numerical method to solve a large class of continuous-time models in economics and finance. Using modern tools from Machine Learning, I show that the problem of solving the corresponding nonlinear partial differential equations (PDEs) can be recast as a sequence of supervised learning problems. Furthermore, I propose a setting to test and evaluate solution methods. In the context of a Neoclassical Growth Model, given any value function, the productivity function can be reverse engineered so that the Hamilton-Jacobi-Bellman (HJB) equation corresponding to the dynamic optimization problem is identically zero. This provides a testing ground for solution methods. Chapter 2 leverages the algorithm developed in chapter 1 to do structural estimation of stochastic dynamic models in economics. By extending the state space to include all model parameters, I show that we need to solve the model only once to do structural estimation. Parameters are then estimated by minimizing the distance between key empirical moments and the model-implied ones. Unlike the Simulated Method of Moments, the model-implied moments are estimated without the computation of a single moment. Instead, a neural network learns the corresponding moments using raw simulated observations. In chapter 3 I study a multi-sector production-based economy where countercyclical risk premia and capital reallocation lengthens recessions. In the model, risk-aversion increases after negative productivity shocks, and the ensuing capital reallocation propagates the reduction in aggregate productivity and aggregate consumption. The decrease in consumption keeps the risk aversion high, preventing a quick recovery to the balanced growth path. by Victor Duarte. 1. Machine Learning for Continuous-Time Economics -- 2, Gradient-Based Structural Estimation -- 3. Sectoral Reallocation and Endogenous Risk-Aversion. Ph. D. 2018-09-17T15:53:56Z 2018-09-17T15:53:56Z 2018 2018 Thesis http://hdl.handle.net/1721.1/118016 1051454211 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 104 pages application/pdf Massachusetts Institute of Technology
spellingShingle Sloan School of Management.
Duarte, Victor (Fonseca Duarte)
Essays in financial economics
title Essays in financial economics
title_full Essays in financial economics
title_fullStr Essays in financial economics
title_full_unstemmed Essays in financial economics
title_short Essays in financial economics
title_sort essays in financial economics
topic Sloan School of Management.
url http://hdl.handle.net/1721.1/118016
work_keys_str_mv AT duartevictorfonsecaduarte essaysinfinancialeconomics