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Author: Hua Shang
Publisher:
ISBN:
Category :
Languages : en
Pages : 129
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This disertation includes three essays on linear asset pricing models. The first chapter is concerned with the effects of including a low-variance factor which leads to a small signal-to-noise ratio in an asset pricing model. We rely on local asymptotics and define the low-variance as local-to-zero by being inversely related to the sample size. When a low-variance factor is present, the commonly applied Fama-Macbeth two-pass regression procedure yields misleading results. Local asymptotic analysis and simulation evidence indicate that the beta of the low-variance factor, risk premiums corresponding to all factors and the magnitude of associated variances are all unreliably estimated. Moreover, t- and F- statistics are unable to detect whether risk premiums are significantly different from zero. Additional simulation results also reveal that Kleibergen's statistic has some ability to detect the usefulness of different factors. In the second chapter, I investigate the finite sample properties of the two-pass regression, the t-statistic, statistics proposed by Kleibergen (2009) and the specification tests when the first-pass regression slope coefficients -- betas -- are large, small and zero. In particular, I explore the effect of the number of assets on the properties of the statistics. The results reveal that most of the statistics tend to reach a conclusion that the factor should be included in the model or the model is correct more often that it should, especially when betas are small and the number of assets is large. The diagnosis of the results shows that the source of the problem lies in the large bias of the estimated risk premiums and the poor estimation of the variance-covariance matrix of the error terms in the first-pass regression. The third chapter explores an economic explanation of commodity prices by considering the macro-economic exposure of commodity returns. Through estimating the stochastic discount factor representation of the linear asset pricing model, I find that investors are compensated for exchange-rate risk. The result is robust to different estimation methods, to different data sets and over longer periods of time.
Author: Hua Shang
Publisher:
ISBN:
Category :
Languages : en
Pages : 129
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Book Description
This disertation includes three essays on linear asset pricing models. The first chapter is concerned with the effects of including a low-variance factor which leads to a small signal-to-noise ratio in an asset pricing model. We rely on local asymptotics and define the low-variance as local-to-zero by being inversely related to the sample size. When a low-variance factor is present, the commonly applied Fama-Macbeth two-pass regression procedure yields misleading results. Local asymptotic analysis and simulation evidence indicate that the beta of the low-variance factor, risk premiums corresponding to all factors and the magnitude of associated variances are all unreliably estimated. Moreover, t- and F- statistics are unable to detect whether risk premiums are significantly different from zero. Additional simulation results also reveal that Kleibergen's statistic has some ability to detect the usefulness of different factors. In the second chapter, I investigate the finite sample properties of the two-pass regression, the t-statistic, statistics proposed by Kleibergen (2009) and the specification tests when the first-pass regression slope coefficients -- betas -- are large, small and zero. In particular, I explore the effect of the number of assets on the properties of the statistics. The results reveal that most of the statistics tend to reach a conclusion that the factor should be included in the model or the model is correct more often that it should, especially when betas are small and the number of assets is large. The diagnosis of the results shows that the source of the problem lies in the large bias of the estimated risk premiums and the poor estimation of the variance-covariance matrix of the error terms in the first-pass regression. The third chapter explores an economic explanation of commodity prices by considering the macro-economic exposure of commodity returns. Through estimating the stochastic discount factor representation of the linear asset pricing model, I find that investors are compensated for exchange-rate risk. The result is robust to different estimation methods, to different data sets and over longer periods of time.
Author: Kyou Yung Kim
Publisher:
ISBN:
Category :
Languages : en
Pages : 130
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Author: Lei Zhao
Publisher:
ISBN: 9780438193239
Category : Capital assets pricing model
Languages : en
Pages : 0
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Book Description
Using more stringent test assets and more formal model diagnostic tools, the first essay demonstrates the importance of higher-order comoment risks in asset pricing by assessing the performance of the most commonly used asset pricing models with and without these risks incorporated. Specifically, we find that higher-order comoment risks help the Fama and French serial pricing kernels to be closer to the admissible pricing kernel and that the newly developed Fama and French five-factor model (Fama and French, 2015), when augmented by the quadratic and cubic terms of the market return and with momentum incorporated, requires the least adjustment to be admissible.
Author: Xiang Zhang
Publisher:
ISBN: 9788449039119
Category :
Languages : en
Pages : 121
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Book Description
This thesis consists of three essays on empirical asset pricing around three themes: evaluating linear factor asset pricing models by comparing their misspecified measures, understanding the long-run risk on consumption-leisure to investigate their pricing performances on cross-sectional returns, and evaluating conditional asset pricing models by using the methodology of dynamic cross-sectional regressions. The first chapter is ̀̀Comparing Asset Pricing Models: What does the Hansen-Jagannathan Distance Tell Us?''. It compares the relative performance of some important linear asset pricing models based on the Hansen-Jagannathan (HJ) distance using data over a long sample period from 1952-2011 based on U.S. market. The main results are as follows: first, among return-based linear models, the Fama-French (1993) five-factor model performs best in terms of the normalized pricing errors, compared with the other candidates. On the other hand, the macro-factor model of Chen, Roll, and Ross (1986) five-factor is not able to explain industry portfolios: its performance is even worse than that of the classical CAPM. Second, the Yogo (2006) non-durable and durable consumption model is the least misspecified, among consumption-based asset pricing models, in capturing the spread in industry and size portfolios. Third, the Lettau and Ludvigson (2002) scaled consumption-based CAPM (C-CAPM) model obtains the smallest normalized pricing errors pricing gross and excess returns on size portfolios, respectively, while Santos and Veronesi (2006) scaled C-CAPM model does better in explain the return spread on portfolios of U.S. government bonds. The second chapter (̀̀Leisure, Consumption and Long Run Risk: An Empirical Evaluation'') uses a long-run risk model with non-separable leisure and consumption, and studies its ability to price equity returns on a variety of portfolios of U.S. stocks using data from 1948-2011. It builds on early work by Eichenbaum et al. (1988) that explores the empirical properties of intertemporal asset pricing models where the representative agent has utility over consumption and leisure. Here we use the framework in Uhlig (2007) that allows for a stochastic discount factor with news about long-run growth in consumption and leisure. To evaluate our long-run model, we assess its performance relative to standard asset pricing models in explaining the cross-section of returns across size, industry and value-growth portfolios. We find that the long-run consumption-leisure model cannot be rejected by the J-statistic and it does better than the standard C-CAPM, the Yogo durable consumption and Fama-French three-factor models. We also rank the normalized pricing errors using the HJ distance: our model has a smaller HJ distance than other candidate models. Our paper is the first, as far as we are aware, to use leisure data with adjusted working hours as a measure of leisure i.e., defined as the difference between a fixed time endowment and the observable hours spent on working, home production, schooling, communication, and personal care (Yang (2010)). The third essay: ̀̀Empirical Evaluation of Conditional Asset Pricing Models: An Economic Perspective'' uses dynamic Fama-MacBeth cross-sectional regressions and tests the performance of several important conditional asset pricing models when allowing for time-varying price of risk. It compares the performance of conditional asset pricing models, in terms of their ability to explain the cross-section of returns across momentum, industry, value-growth and government bond portfolios. We use the new methodology introduced by Adrian et al. (2012). Our main results are as follows: first we find that the Lettau and Ludvigson (2001) conditional model does better than other models in explaining the cross-section of momentum and value-growth portfolios. Second we find that the Piazessi et al. (2007) consumption model does better than others in pricing the cross-section of industry portfolios. Finally, we find that in the case of the cross-section of risk premia on U.S. government bond portfolios the conditional model in Santos and Veronesi (2006) outperforms other candidate models. Overall, however, the Lettau and Ludvigson (2001) model does better than other candidate models. Our main contributions here is using a recently developed method of dynamic Fama-MacBeth regressions to evaluate the performance of leading conditional CAPM (C-CAPM) models in a common set of test assets over the time period from 1951-2012.
Author: Alan Picard
Publisher:
ISBN:
Category :
Languages : en
Pages : 165
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Abstract This dissertation consists of three essays. My first paper re-examines the link between idiosyncratic risk and expected returns for a large sample of firms in both developed and emerging markets. Recent studies using Fama-French three factor models have shown a negative relationship between idiosyncratic volatility and expected returns for developed markets. This relationship has not been studied to date for emerging markets. This study relates the current-month’s idiosyncratic volatility to the subsequent month’s returns for a sample of both developed and emerging markets expanding benchmark factors by including both a momentum and a systematic liquidity risk component. My second essay contributes to the important literature on the topic of the small capitalization stocks historical outperformance over large capitalization stocks by investigating the hypothesis that the small firm premium is related to macroeconomic and financial variables and that relationship is driven by the economic cycle in the United States and Canada. More specifically, this study employs recent advances in nonlinear time series models to explore the relationship between the small firm premium, and financial and macroeconomic variables in the Canadian and U.S. economies. My third paper re-examines the findings of a recent research paper that suggested that market wide liquidity may act as a leading indicator to the economic cycle. Using several liquidity measures and various macroeconomic variables to proxy for the economic conditions, the paper presents evidence that stock market liquidity could forecast business cycles: A major decrease in the overall level of market liquidity could indicate weak economic growth in the subsequent months. However, the drawback in the analysis is that the relationship is investigated in a linear approach even though it has been proven that most macroeconomic variables follow non-linear dynamics. Employing similar liquidity measures and macroeconomic proxies, and two popular econometrics models that account for non-linear behavior, this study hence re-investigates the relationship between stock market liquidity and business cycles.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 131
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My dissertation studies the asset pricing implications of non-linear models, including regime-switching models and non-linear diffusion models. The first chapter investigates empirically the effects of regime switches in stock returns and volatilities. First, the empirical results suggest that the expected excess return and the volatility are the monotonically increasing functions of the investors' belief. It implies that risk aversion is time-varying and the representative agent is more risk averse in the bear regime so that higher expected excess return and higher volatility are generated in the bear regime. The empirical work also finds that the term spread, the inflation rate, and the T-bill rate have significant business cycle patterns in the predictive regressions. For example, the term spread is positively related to the stock market returns in the bull regime, but is negatively related to the stock market returns in the bear regime. This suggests that the increasing term spread is a good news in the bad regime because it indicates that the economy is improving and will recover soon, thus the investors require a lower equity premium. In the second chapter, an econometric method is developed for pricing and estimation for a newclass of non-linear diffusion processes. These type of non-linear diffusion processes are used to model the dynamics of the VIX index under both the objective measure and the risk-neutral measure, where the latter is estimated from futures prices. The difference between the drifts under the objective measure and the risk-neutral measure is defined as a measure of the variance risk premium. The predictive regressions demonstrate that the variance risk premium estimated by the non-linear diffusion models has stronger predictive power for stock returns than the affine models. In the third chapter, a hidden Markov model is used to describe the dynamics of the realized variance of stock market returns. I investigate the relations among the variance regime, variance risk premium, and stock market returns. I find that the variance risk premium, i.e., the difference between the expected return variation under the risk-neutral and the physical measures, is higher in the high-variance regime, in which the volatility-of-volatility is high. However, the positive relation between the variance risk premium and future stock returns is entirely due to a component of variance risk premium that is orthogonal to the current realized variance and the variance regime. The results suggest that the predictive power of the variance risk premium for stock returns is more likely due to its correlation with time-varying risk aversion than with time-varying risks.
Author: Lionel Martellini
Publisher:
ISBN:
Category :
Languages : en
Pages : 390
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Author: Tae-Jin Kang
Publisher:
ISBN:
Category :
Languages : en
Pages : 174
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Author: Yongli Zhang
Publisher: ProQuest
ISBN: 9780549269489
Category :
Languages : en
Pages : 198
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G models without a monetary perspective are difficult to capture the dynamics of the real interest rates in the data of the US economy.
Author: Selale Tuzel
Publisher:
ISBN:
Category : Capital assets pricing model
Languages : en
Pages : 286
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