Matching Items (13)
Description
Schennach (2007) has shown that the Empirical Likelihood (EL) estimator may not be asymptotically normal when a misspecified model is estimated. This problem occurs because the empirical probabilities of individual observations are restricted to be positive. I find that even the EL estimator computed without the restriction can fail to be asymptotically normal for misspecified models if the sample moments weighted by unrestricted empirical probabilities do not have finite population moments. As a remedy for this problem, I propose a group of alternative estimators which I refer to as modified EL (MEL) estimators. For correctly specified models, these estimators have the same higher order asymptotic properties as the EL estimator. The MEL estimators are obtained by the Generalized Method of Moments (GMM) applied to an exactly identified model. The simulation results provide promising evidence for these estimators. In the second chapter, I introduce an alternative group of estimators to the Generalized Empirical Likelihood (GEL) family. The new group is constructed by employing demeaned moment functions in the objective function while using the original moment functions in the constraints. This designation modifies the higher-order properties of estimators. I refer to these new estimators as Demeaned Generalized Empirical Likelihood (DGEL) estimators. Although Newey and Smith (2004) show that the EL estimator in the GEL family has fewer sources of bias and is higher-order efficient after bias-correction, the demeaned exponential tilting (DET) estimator in the DGEL group has those superior properties. In addition, if data are symmetrically distributed, every estimator in the DGEL family shares the same higher-order properties as the best member.
ContributorsXiang, Jin (Author) / Ahn, Seung (Thesis advisor) / Wahal, Sunil (Thesis advisor) / Bharath, Sreedhar (Committee member) / Mehra, Rajnish (Committee member) / Tserlukevich, Yuri (Committee member) / Arizona State University (Publisher)
Created2013
Description
This paper examines dealers' inventory holding periods and the associated price markups on corporate bonds from 2003 to 2010. Changes in these measures explain a large part of the time series variation in aggregate corporate bond prices. In the cross-section, holding periods and markups overshadow extant liquidity measures and have significant explanatory power for individual bond prices. Both measures shed light on the credit spread puzzle: changes in credit spread are positively correlated with changes in holding periods and markups, and a large portion of credit spread changes is explained by them. The economic effects of holding periods and markups are particularly sharp during crisis periods.
ContributorsQian, Zhiyi (Author) / Wahal, Sunil (Thesis advisor) / Bharath, Sreedhar (Committee member) / Coles, Jeffrey (Committee member) / Mehra, Rajnish (Committee member) / Arizona State University (Publisher)
Created2012
Description
This report will provide an analysis of frontier market equity-based investment funds with respect to bivariate correlation analysis, global integration analysis, and US optimized portfolio statistics. My analysis has indicated strong diversification benefits of including frontier market equities in a US portfolio, given its low correlation to US equity concentrated portfolios especially portfolios that would consist of midcap and smallcap stocks. With the drawbacks of the bivariate correlation test, an additional global integration analysis has been included to reaffirm the value frontier markets offer in the form of integration. Integration is a second layer of the diversification analysis. I find that when analyzing frontier markets (FM) against developed markets (DM) there exhibits significantly less integration as compared to emerging markets against developed markets. This analysis goes one step further and quantifies integration of specific frontier market funds against the broader emerging and developed markets. This study finds that iShares MSCI frontier 100 ETF (Ticker: FM) exhibits the least integration amongst Guggenheim Frontier Markets ETF (Ticker: FRN), Templeton Frontier Markets A (Ticker: TFMAX), and Morgan Stanley Frontier Emg (Ticker: MFMIX). Lastly, this analysis covers the inadequacy with using Sharpe ratios and minimum volatility parameters to achieve portfolio optimization under a Monte-Carlo style 1000 portfolio simulation with frontier market funds in a broader US equity portfolio but finds better results when using a US equity and US bond combination portfolio. Overall, this analysis of frontier markets and frontier market funds has shown there still exists significant diversification benefits to US Investors when they engage in FM investments, specifically through diversified FM investment funds.
ContributorsHardy, Gunner Laine (Author) / Pruitt, Seth (Thesis director) / Brada, Josef (Committee member) / W.P. Carey School of Business (Contributor) / Economics Program in CLAS (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis aims to promote financial literacy in the community. It was driven by the realization that there was a lack of basic financial knowledge among people at ASU and beyond. The people involved in the reason for the guide had all heard of bonds and understood the basic concepts, but lacked the knowledge of the finite details. The research starts with an overview of the United States bond market and focuses on the creation of a short simple guide. The goal is that anyone can read the guide and have a basic understanding of bonds, talk to financial managers, and do some basic investing. The easy guide is basically a two-page crash course on investing in bonds. Anyone can take a class or watch a video on bonds, but how do they actually start investing in them? This thesis works to answer this question by providing knowledge of real world application. The goal is to take knowledge beyond a book or video and learn from actively investing in a safe and clear way. Bonds are a very useful tool in investing and provide safe returns. The investing proposed is one that would be an alternative to putting money into a savings account. The guide recommends a good starting point of a way to invest in bonds (Specifically the US Treasury). At the same time does some analysis on other investing options for more advanced investors. The work includes an analysis of five bond portfolios and the calculations of finding their actual returns after loads and other fees.
ContributorsIrwin, Carter E. (Author) / Pruitt, Seth (Thesis director) / Schreindorfer, David (Committee member) / W.P. Carey School of Business (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
This dissertation studies how forecasting performance can be improved in big data. The first chapter with Seung C. Ahn considers Partial Least Squares (PLS) estimation of a time-series forecasting model with data containing a large number of time series observations of many predictors. In the model, a subset or a whole set of the latent common factors in predictors determine a target variable. First, the optimal number of the PLS factors for forecasting could be smaller than the number of the common factors relevant for the target variable. Second, as more than the optimal number of PLS factors is used, the out-of-sample explanatory power of the factors could decrease while their in-sample power may increase. Monte Carlo simulation results also confirm these asymptotic results. In addition, simulation results indicate that the out-of-sample forecasting power of the PLS factors is often higher when a smaller than the asymptotically optimal number of factors are used. Finally, the out-of-sample forecasting power of the PLS factors often decreases as the second, third, and more factors are added, even if the asymptotically optimal number of the factors is greater than one. The second chapter studies the predictive performance of various factor estimations comprehensively. Big data that consist of major U.S. macroeconomic and finance variables, are constructed. 148 target variables are forecasted, using 7 factor estimation methods with 11 information criteria. First, the number of factors used in forecasting is important and Incorporating more factors does not always provide better forecasting performance. Second, using consistently estimated number of factors does not necessarily improve predictive performance. The first PLS factor, which is not theoretically consistent, very often shows strong forecasting performance. Third, there is a large difference in the forecasting performance across different information criteria, even when the same factor estimation method is used. Therefore, the choice of factor estimation method, as well as the information criterion, is crucial in forecasting practice. Finally, the first PLS factor yields forecasting performance very close to the best result from the total combinations of the 7 factor estimation methods and 11 information criteria.
ContributorsBae, Juhui (Author) / Ahn, Seung (Thesis advisor) / Pruitt, Seth (Committee member) / Kuminoff, Nicolai (Committee member) / Ferraro, Domenico (Committee member) / Arizona State University (Publisher)
Created2021
Description
This dissertation consists of three essays studying the relationship between corporate finance and monetary policy and macroeconomics. In the first essay, I provide novel estimations of the monetary policy’s working capital channel size by estimating a dynamic stochastic macro-finance model using firm-level data. In aggregate, I find a partial channel —about three-fourths of firms’ labor bill is borrowed. But the strength of this channel varies across industries, reaching as low as one-half for retail firms and as high as one for agriculture and construction. These results provide evidence that monetary policy could have varying effects across industries through the working capital channel. In the second essay, I study the effects of the Unconventional Monetary Policy (UMP) of purchasing corporate bonds on firms’ decisions in the COVID-19 crisis. Specifically, I develop a theoretical model which predicts that the firm’s default probability plays a crucial role in transmitting the effects of COVID-19 shock and the UMP. Using the model to evaluate two kinds of heterogeneities (size and initial credit risk), I show that large firms and high-risk firms are more affected by COVID-19 shock and are more responsive to the UMP. I then run cross-sectional regressions, whose results support the theoretical predictions suggesting that the firm’s characteristics, such as assets and operating income, are relevant to understanding the UMP effects. In the third essay, I document that capital utilization and short-term debt are procyclical. I show that a strong positive relationship exists at the aggregate and firm levels. It persists even when I control the regressions for firm size, profits, growth, and business cycle effects. In addition, the Dynamic Stochastic General Equilibrium (DSGE) model shows that in the presence of capital utilization, positive real and financial shocks cause the firm to change its financing of the equity payout policy from earnings to debt, increasing short-term debt.
ContributorsGalindo Gil, Hamilton (Author) / Pruitt, Seth (Thesis advisor) / Schreindorfer, David (Thesis advisor) / Bessembinder, Hendrik (Committee member) / Mehra, Rajnish (Committee member) / Arizona State University (Publisher)
Created2022
Description
My creative project is a Python program designed to simulate a $100,000 stock portfolio using real data about the stock market. It runs continuously on my computer and executes the main body of the code once per day at 11:00 am AZ time. It will pull prices from the internet for all stocks in the S&P 500 between 07/01/2023 and now. Each day, the program outputs two .csv files showing the makeup of the portfolio and an aggregated list of all transactions that have taken place. The financial decisions are made using Modern Portfolio Theory and the Efficient Frontier model, balancing risk and maximizing the Sharpe ratio to create the most mathematically optimal portfolio. There is a lot of documentation available to users to show the process of the code through daily executions, how to install required packages, and ultimately how to use the program. It was designed as a simulation for this project but has the potential to be expanded beyond its current bounds and eventually become a legitimate algorithm trading bot.
ContributorsAmazeen, Andrew (Author) / Sopha, Matt (Thesis director) / Pruitt, Seth (Committee member) / Barrett, The Honors College (Contributor) / School of Accountancy (Contributor) / Department of Information Systems (Contributor)
Created2024-05
Description
This dissertation applies the Bayesian approach as a method to improve the estimation efficiency of existing econometric tools. The first chapter suggests the Continuous Choice Bayesian (CCB) estimator which combines the Bayesian approach with the Continuous Choice (CC) estimator suggested by Imai and Keane (2004). Using simulation study, I provide two important findings. First, the CC estimator clearly has better finite sample properties compared to a frequently used Discrete Choice (DC) estimator. Second, the CCB estimator has better estimation efficiency when data size is relatively small and it still retains the advantage of the CC estimator over the DC estimator. The second chapter estimates baseball's managerial efficiency using a stochastic frontier function with the Bayesian approach. When I apply a stochastic frontier model to baseball panel data, the difficult part is that dataset often has a small number of periods, which result in large estimation variance. To overcome this problem, I apply the Bayesian approach to a stochastic frontier analysis. I compare the confidence interval of efficiencies from the Bayesian estimator with the classical frequentist confidence interval. Simulation results show that when I use the Bayesian approach, I achieve smaller estimation variance while I do not lose any reliability in a point estimation. Then, I apply the Bayesian stochastic frontier analysis to answer some interesting questions in baseball.
ContributorsChoi, Kwang-shin (Author) / Ahn, Seung (Thesis advisor) / Mehra, Rajnish (Committee member) / Park, Sungho (Committee member) / Arizona State University (Publisher)
Created2014
Description
Merton (1987) predicts that idiosyncratic risk can be priced. I develop a simple equilibrium model of capital markets with information costs in which the idiosyncratic risk premium depends on the average level of idiosyncratic volatility. This dependence suggests that the idiosyncratic risk premium varies over time. I find that in U.S. markets, the covariance between stock-level idiosyncratic volatility and the idiosyncratic risk premium explains future stock returns. Stocks in the highest quintile of the covariance between the volatility and risk premium earn an average 3-factor alpha of 70 bps per month higher than those in the lowest quintile.
ContributorsXie, Daruo (Author) / Wahal, Sunil (Thesis advisor) / Mehra, Rajnish (Thesis advisor) / Arizona State University (Publisher)
Created2015
Description
This dissertation is a collection of three essays relating household financial obligations to asset prices. Financial obligations include both debt payments and other financial commitments.
In the first essay, I investigate how household financial obligations affect the equity premium. I modify the standard Mehra-Prescott (1985) consumption-based asset pricing model to resolve the equity risk premium puzzle. I focus on two channels: the preference channel and the borrowing constraints channel. Under reasonable parameterizations, my model generates equity risk premiums similar in magnitudes to those observed in U.S. data. Furthermore, I show that relaxing the borrowing constraint shrinks the equity risk premium.
In the Second essay, I test the predictability of excess market returns using the household financial obligations ratio. I show that deviations in the household financial obligations ratio from its long-run mean is a better forecaster of future market returns than alternative prediction variables. The results remain significant using either quarterly or annual data and are robust to out-of-sample tests.
In the third essay, I investigate whether the risk associated with household financial obligations is an economy-wide risk with the potential to explain fluctuations in the cross-section of stock returns. The multifactor model I propose, is a modification of the capital asset pricing model that includes the financial obligations ratio as a ``conditioning down" variable. The key finding is that there is an aggregate hedging demand for securities that pay off in periods characterized by higher levels of financial obligations ratios. The consistent pricing of financial obligations risk with a negative risk premium suggests that the financial obligations ratio acts as a state variable.
In the first essay, I investigate how household financial obligations affect the equity premium. I modify the standard Mehra-Prescott (1985) consumption-based asset pricing model to resolve the equity risk premium puzzle. I focus on two channels: the preference channel and the borrowing constraints channel. Under reasonable parameterizations, my model generates equity risk premiums similar in magnitudes to those observed in U.S. data. Furthermore, I show that relaxing the borrowing constraint shrinks the equity risk premium.
In the Second essay, I test the predictability of excess market returns using the household financial obligations ratio. I show that deviations in the household financial obligations ratio from its long-run mean is a better forecaster of future market returns than alternative prediction variables. The results remain significant using either quarterly or annual data and are robust to out-of-sample tests.
In the third essay, I investigate whether the risk associated with household financial obligations is an economy-wide risk with the potential to explain fluctuations in the cross-section of stock returns. The multifactor model I propose, is a modification of the capital asset pricing model that includes the financial obligations ratio as a ``conditioning down" variable. The key finding is that there is an aggregate hedging demand for securities that pay off in periods characterized by higher levels of financial obligations ratios. The consistent pricing of financial obligations risk with a negative risk premium suggests that the financial obligations ratio acts as a state variable.
ContributorsJahangiry, Pedram (Author) / Mehra, Rajnish (Thesis advisor) / Wahal, Sunil (Committee member) / Reffett, Kevin (Committee member) / Arizona State University (Publisher)
Created2017