Essays on Asset Pricing and Empirical Estimation

Essays on Asset Pricing and Empirical Estimation
Author: Pooya Nazeran
Publisher:
Total Pages: 138
Release: 2011
Genre:
ISBN:

Abstract: A considerable portion of the asset pricing literature considers the demand schedule for asset prices to be perfectly elastic (flat). As argued, asset prices are determined using information about future payoff distribution, as well as the discount rate; consequently, an asset would be priced independent of its available supply. Furthermore, such a flat demand curve is considered to be a consequence of the Efficient Market Hypothesis. My dissertation evaluates and questions the factuality of these assertions. I approach this problem from both an empirical and a theoretical perspective. The general argument is that asset prices do respond to supply-shocks; and changes in aggregate demand, stemming from preference changes, new international investments, or quantitative easing by the Fed, can result in price changes. Hence, asset prices are determined by both demand and supply factors. In the first essay, "Downward Sloping Asset Demand: Evidence from the Treasury Bills Market," I report on my empirical study which establishes the existence of a downward sloping demand curve (DSDC) in the T-bill market. In the second essay, "Asset Pricing: Inelastic Supply," I examine the theoretical issues concerning a downward sloping demand curve. I begin by clarifying a common confusion in the literature, namely, that many asset pricing models imply a flat demand curve. I show that the prominent asset pricing models, including Capital Asset Pricing Model (CAPM), Arbitrage Pricing Theory (APT) and Consumption Capital Asset Pricing Model (CCAPM), all have an underlying DSDC. I further show that, while these models imply the relevance of supply, they are inconvenient as a vehicle for the estimation and analysis of the DSDC in the data. For those purposes, I develop an asset pricing framework based on the stochastic discount factor framework, specifically designed with a DSDC at its heart. I end the essay with a discussion of the framework's implications and applications. In the third essay I develop on the Factor-Augmented Vector-Autoregression (FAVAR) literature, proposing a bias-corrected method. As implemented in the literature, the Principal Component Analysis stage of FAVAR introduces a classical-error-in-variable problem which leads to bias. I propose an instrument-based method for bias correction.

Empirical Testing of Asset Pricing Models

Empirical Testing of Asset Pricing Models
Author: Bruce Neal Lehmann
Publisher:
Total Pages: 52
Release: 1992
Genre: Assets (Accounting)
ISBN:

This essay reviews the extensive literature on empirical testing of asset pricing models. It briefly describes the kinds of asset pricing models typically tested in the literature and explicates their econometric implications, both in terms of the estimation of relevant parameters and tests of their implied restrictions. Pertinent aspects of the available data on security prices and macroeconomic variables are discussed as well. The essay concludes with the examination of selected aspects of the current empirical state of asset pricing theory

Essays in Asset Pricing

Essays in Asset Pricing
Author: Michael Shane O'Doherty
Publisher:
Total Pages: 159
Release: 2011
Genre: Stock price forecasting
ISBN:

Using a variety of test portfolios, the optimal pool of models consistently outperforms the best individual model on both statistical and economic grounds.

Essays in Empirical Asset Pricing

Essays in Empirical Asset Pricing
Author: Irina Pimenova
Publisher:
Total Pages: 206
Release: 2018
Genre:
ISBN:

In this dissertation, I revisit two problems in empirical asset pricing. In Chapter 1, I propose a methodology to evaluate the validity of linear asset pricing factor models under short sale restrictions using a regression-based test. The test is based on the revised null hypothesis that intercepts obtained from regressing excess returns of test assets on factor returns, usually referred to as alphas, are non-positive. I show that under short sale restrictions a much larger set of models is supported by the data than without restrictions. In particular, the Fama-French five-factor model augmented with the momentum factor is rejected less often than other models. In Chapter 2, I investigate patterns of equity premium predictability in international capital markets and explore the robustness of common predictive variables. In particular, I focus on predictive regressions with multiple predictors: dividend-price ratio, four interest rate variables, and inflation. To obtain precise estimates, two estimation methods are employed. First, I consider all capital markets jointly as a system of regressions. Second, I take into account uncertainty about which potential predictors forecast excess returns by employing spike-and-slab prior. My results suggest evidence in favor of predictability is weak both in- and out-of-sample and limited to a few countries. The strong predictability observed on the U.S. market is rather exceptional. In addition, my analysis shows that considering model uncertainty is essential as it leads to a statistically significant increase of investors' welfare both in- and out-of-sample. On the other hand, the welfare increase associated with considering capital markets jointly is relatively modest. However, it leads to reconsider the relative importance of predictive variables because the variables that are statistically significant predictors in the country-specific regressions are insignificant when the capital markets are studied jointly. In particular, my results suggest that the in-sample evidence in favor of the interest rate variables, that are believed to be among the most robust predictors by the literature, is spurious and is mostly driven by ignoring the cross-country information. Conversely, the dividend-price ratio emerges as the only robust predictor of future stock returns.

Essays in Asset Pricing and Machine Learning

Essays in Asset Pricing and Machine Learning
Author: Jason Yue Zhu
Publisher:
Total Pages:
Release: 2021
Genre:
ISBN:

In this thesis we study two applications of machine learning to estimate models that explains asset prices by harnessing the vast quantity of asset and economic information while also capturing complex structure among sources of risk. First we show how to build a cross-section of asset returns, that is, a small set of basis or test assets that capture complex information contained in a given set of characteristics and span the Stochastic Discount Factor (SDF). We use decision trees to generalize the concept of conventional sorting and introduce a new approach to robustly recover the SDF, which endogenously yields optimal portfolio splits. These low-dimensional investment strategies are well diversified, easily interpretable, and reflect many characteristics at the same time. Empirically, we show that traditional cross-sections of portfolios and their combinations, especially deciles and long-short anomaly factors, present too low a hurdle for model evaluation and serve as the wrong building blocks for the SDF. Constructed from the same pricing signals, our cross-sections have significantly higher (up to a factor of three) out-of-sample Sharpe ratios and pricing errors relative to the leading reduced-form asset pricing models. In the second part of the thesis, I present deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors and identifies the key factors that drive asset prices.