Essays on the Predictability and Volatility of Asset Returns PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Essays on the Predictability and Volatility of Asset Returns PDF full book. Access full book title Essays on the Predictability and Volatility of Asset Returns by Stefan A. Jacewitz. Download full books in PDF and EPUB format.
Author: Stefan A. Jacewitz Publisher: ISBN: Category : Languages : en Pages :
Book Description
This dissertation collects two papers regarding the econometric and economic theory and testing of the predictability of asset returns. It is widely accepted that stock returns are not only predictable but highly so. This belief is due to an abundance of existing empirical literature finding often overwhelming evidence in favor of predictability. The common regressors used to test predictability (e.g., the dividend-price ratio for stock returns) are very persistent and their innovations are highly correlated with returns. Persistence when combined with a correlation between innovations in the regressor and asset returns can cause substantial over-rejection of a true null hypothesis. This result is both well documented and well known. On the other hand, stochastic volatility is both broadly accepted as a part of return time series and largely ignored by the existing econometric literature on the predictability of returns. The severe effect that stochastic volatility can have on standard tests are demonstrated here. These deleterious effects render standard tests invalid. However, this problem can be easily corrected using a simple change of chronometer. When a return time series is read in the usual way, at regular intervals of time (e.g., daily observations), then the distribution of returns is highly non-normal and displays marked time heterogeneity. If the return time series is, instead, read according to a clock based on regular intervals of volatility, then returns will be independent and identically normally distributed. This powerful result is utilized in a unique way in each chapter of this dissertation. This time-deformation technique is combined with the Cauchy t-test and the newly introduced martingale estimation technique. This dissertation finds no evidence of predictability in stock returns. Moreover, using martingale estimation, the cause of the Forward Premium Anomaly may be more easily discerned.
Author: Stefan A. Jacewitz Publisher: ISBN: Category : Languages : en Pages :
Book Description
This dissertation collects two papers regarding the econometric and economic theory and testing of the predictability of asset returns. It is widely accepted that stock returns are not only predictable but highly so. This belief is due to an abundance of existing empirical literature finding often overwhelming evidence in favor of predictability. The common regressors used to test predictability (e.g., the dividend-price ratio for stock returns) are very persistent and their innovations are highly correlated with returns. Persistence when combined with a correlation between innovations in the regressor and asset returns can cause substantial over-rejection of a true null hypothesis. This result is both well documented and well known. On the other hand, stochastic volatility is both broadly accepted as a part of return time series and largely ignored by the existing econometric literature on the predictability of returns. The severe effect that stochastic volatility can have on standard tests are demonstrated here. These deleterious effects render standard tests invalid. However, this problem can be easily corrected using a simple change of chronometer. When a return time series is read in the usual way, at regular intervals of time (e.g., daily observations), then the distribution of returns is highly non-normal and displays marked time heterogeneity. If the return time series is, instead, read according to a clock based on regular intervals of volatility, then returns will be independent and identically normally distributed. This powerful result is utilized in a unique way in each chapter of this dissertation. This time-deformation technique is combined with the Cauchy t-test and the newly introduced martingale estimation technique. This dissertation finds no evidence of predictability in stock returns. Moreover, using martingale estimation, the cause of the Forward Premium Anomaly may be more easily discerned.
Author: Ruojun Wu Publisher: ISBN: Category : Bayesian statistical decision theory Languages : en Pages : 137
Book Description
This dissertation studies the effect of parameter uncertainty on the return predictability and volatility of the stock market. The first two chapters focus on the decomposition of market volatility, and the third chapter studies the return predictability. When facing imperfect information, the investors tend to form a learning scheme that encompasses both historical data and prior beliefs. In the variance decomposition framework, the introducing of learning directly impacts the way that return forecasts are revised and consequently the relative component of market volatility based on these forecasts, namely the price movements from revision on future discount rates and those from future cash flows. According to the empirical study in Chapter 1, the former is not necessarily the major driving force of market volatility, which provides an alternative view on what moves stock prices. Learning is modeled and estimated by Bayesian method. Chapter 2 follows the topic in Chapter 1 and studies the role of persistent state variables in return decomposition in order to provide more robust inference on variance decomposition. In Chapter 3 we propose to utilize theoretical constraints to help predict market returns when in sample data is very noisy and creates model uncertainty for the investors. The constraints are also incorporated by Bayesian method. We show in the out-of-sample forecast experiment that models with theoretical constraints produce better forecasts.
Author: Chengbo Fu Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This dissertation consists of three essays on stock market volatility. In the first essay, we show that investors will have the information in the idiosyncratic volatility spread when using two different models to estimate idiosyncratic volatility. In a theoretical framework, we show that idiosyncratic volatility spread is related to the change in beta and the new betas from the extra factors between two different factor models. Empirically, we find that idiosyncratic volatility spread predicts the cross section of stock returns. The negative spread-return relation is independent from the relation between idiosyncratic volatility and stock returns. The result is driven by the change in beta component and the new beta component of the spread. The spread-relation is also robust when investors estimate the spread using a conditional model or EGARCH method. In the second essay, the variance of stock returns is decomposed based on a conditional Fama-French three-factor model instead of its unconditional counterpart. Using time-varying alpha and betas in this model, it is evident that four additional risk terms must be considered. They include the variance of alpha, the variance of the interaction between the time-varying component of beta and factors, and two covariance terms. These additional risk terms are components that are included in the idiosyncratic risk estimate using an unconditional model. By investigating the relation between the risk terms and stock returns, we find that only the variance of the time-varying alpha is negatively associated with stock returns. Further tests show that stock returns are not affected by the variance of time-varying beta. These results are consistent with the findings in the literature identifying return predictability from time-varying alpha rather than betas. In the third essay, we employ a two-step estimation method to separate the upside and downside idiosyncratic volatility and examine its relation with future stock returns. We find that idiosyncratic volatility is negatively related to stock returns when the market is up and when it is down. The upside idiosyncratic volatility is not related to stock returns. Our results also suggest that the relation between downside idiosyncratic volatility and future stock returns is negative and significant. It is the downside idiosyncratic volatility that drives the inverse relation between total idiosyncratic volatility and stock returns. The results are consistent with the literature that investor overreact to bad news and underreact to good news.
Author: Gregory William Eaton Publisher: ISBN: Category : Languages : en Pages : 304
Book Description
I examine the effects of stock liquidity on asset values and whether aggregate stock liquidity and other forecasting instruments predict stock market returns. In the first chapter, I use tick-size reductions in equity markets as sources of exogenous variation in liquidity to examine the causal effect of transaction costs on firm value. In contrast to the prevailing view, I find that increased liquidity has a marginal or, in some cases, negative impact on firm value. The second chapter evaluates the predictive content of aggregate liquidity for economic activity and stock returns. We decompose illiquidity into a component capturing aggregate volatility and a volatility-adjusted component and find strong evidence that the component of illiquidity uncorrelated with volatility forecasts stock market returns. The third chapter provides new evidence on the stock return forecasting performance of alternative corporate payout yields. We find that the net payout yield forecasts stock returns and generally outperforms the commonly used dividend yield. Additionally, we show that the choice of cash flow used to construct the payout yield is economically significant. An agent relying on the incorrect payout measure as a forecasting instrument is willing to pay an economically significant amount to switch to the optimal policy.
Author: Young Il Kim Publisher: ISBN: Category : Assets (Accounting) Languages : en Pages : 176
Book Description
Abstract: My dissertation addresses two main issues regarding asset returns: econometric modeling of asset returns in chapters 2 and 3 and puzzling features of the standard consumption-based asset pricing model (C-CAPM) in chapters 4 and 5. Chapter 2 develops a new theoretical derivation for the GARCH-skew-t model as a mixture distribution of normal and inverted-chi-square in order to represent the three important stylized facts of financial data: volatility clustering, skewness and thick-tails. The GARCH-skew-t is same as the GARCH-t model if the skewness parameter is shut-off. The GARCH-skew-t is applied to U.S. excess stock market returns, and the equity premium is computed based on the estimated model. It is shown that skewness and kurtosis can have significant effect on the equity premium and that with sufficiently negatively skewed distribution of the excess returns, a finite equity premium can be assured, contrary to the case of the Student t in which an infinite equity premium arises. Chapter 3 provides a new empirical guidance for modeling a skewed and thick-tailed error distribution along with GARCH effects based on the theoretical derivation for the GARCH-skew-t model and empirical findings on the Realized Volatility (RV) measure, constructed from the summation of higher frequency squared (demeaned) returns. Based on an 80-year sample of U.S. daily stock market returns, it is found that the distribution of monthly RV conditional on past returns is approximately the inverted-chi-square while monthly market returns, conditional on RV and past returns are normally distributed with RV in both mean and variance. These empirical findings serve as the building blocks underlying the GARCH-skew-t model. Thus, the findings provide a new empirical justification for the GARCH-skew-t modeling of equity returns. Moreover, the implied GARCH-skew-t model accurately represents the three important stylized facts for equity returns. Chapter 4 provides a possible solution to asset return puzzles such as high equity premium and low riskfree rate based on parameter uncertainty. It is shown that parameter uncertainty underlying the data generating process can lead to a negatively skewed and thick-tailed distribution that can explain most of the high equity premium and low riskfree rate even with the degree of risk aversion below 10 in the CRRA utility function. Chapter 5 investigates a possible link between stock market volatility and macroeconomic risk. This chapter studies why U.S. stock market volatility has not changed much during the "great moderation" era of the 1980s in contrast to the prediction made by the standard C-CAPM. A new model is developed such that aggregate consumption is decomposed into stock and non-stock source of income so that stock dividends are a small part of consumption. This new model predicts that the great moderation of macroeconomic risk must have originated from declining volatility of shocks to the relatively large non-stock factor of production while shocks to the relatively small stock assets have been persistently volatile during the moderation era. Furthermore, the model shows that the systematic risk of holding equity is positively associated with the stock share of total wealth.
Author: Shunlin Liang Publisher: ISBN: Category : Industrial management Languages : en Pages :
Book Description
This dissertation consists of two essays on disaster risk and equity return predictability. The first essay proposes new measures of firm-level and market level disaster risk from deviation of put-call symmetry, which is free from being contaminated by the asymmetry between option traders and equity investors. Compared with other known measures of disaster risk, the market-level disaster risk measure robustly predicts aggregate market returns, with out-of-sample (R^2=6.86%) for the next twelve months. The cross-sectional analysis shows that firm-level disaster risk also explains variations in expected stock returns. Stocks with high firm-level disaster risk earn an annual four-factor subsequent alpha 8.0% higher than stocks with low firm-level disaster risk. I explore potential mechanisms giving rise to these asset pricing facts. The second essay finds that the investor’s learning of higher moments can account for the time-variation, size, and volatility of equity premium. I estimate the investor’s belief on skewness and kurtosis of consumption and dividend growth, and assume investor’s Bayesian learning about a skew student’s t-distribution with unknown fixed parameters. The predictive regressions show that more negative skewness and higher kurtosis predict higher subsequent market excess returns, which implies the investor’s learning generates the time variation of equity premium although the true distribution is static. The calibrated asset pricing model shows that the investor’s learning also explains the size and volatility of the equity premium observed in the data when the investor has a preference for early resolution of uncertainty.
Author: Stefan A. Jacewitz
Author: Stefan A. Jacewitz
Author: Ruojun Wu
Author: Yuzhao Zhang
Author: Amit Goyal
Author: Shu Yan
Author: Bradley Steele Paye
Author: Chengbo Fu
Author: Gregory William Eaton
Author: Young Il Kim
Author: Shunlin Liang