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Author: St?phane Chr?tien
Publisher: LAP Lambert Academic Publishing
ISBN: 9783846583357
Category :
Languages : en
Pages : 136
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Book Description
Many financial models are evaluated using the stochastic discount factor (SDF) approach because of its simplicity, flexibility and universality. The two essays of this work exploit these characteristics to re-examine two long-standing asset pricing topics: consumption-based and performance measurement models. The first essay develops a methodology to understand and compare the sources of pricing errors in models based on SDF moments. The method allows a new investigation of preference-based explanations of the risk-free rate, term premium and risk premium puzzles. The second essay presents a method to measure performance evaluation by developing bounds on admissible performance measures that are free from inference errors. The bounds are furthermore used in ranking mutual funds and as a diagnostic instrument for evaluating candidate performance measures. Each essay carefully establishes the empirical relevancy of the proposed methodologies. These extensions of the SDF framework provide important new insights and have numerous finance applications for academic researchers and practitioners.
Author: St?phane Chr?tien
Publisher: LAP Lambert Academic Publishing
ISBN: 9783846583357
Category :
Languages : en
Pages : 136
Get Book Here
Book Description
Many financial models are evaluated using the stochastic discount factor (SDF) approach because of its simplicity, flexibility and universality. The two essays of this work exploit these characteristics to re-examine two long-standing asset pricing topics: consumption-based and performance measurement models. The first essay develops a methodology to understand and compare the sources of pricing errors in models based on SDF moments. The method allows a new investigation of preference-based explanations of the risk-free rate, term premium and risk premium puzzles. The second essay presents a method to measure performance evaluation by developing bounds on admissible performance measures that are free from inference errors. The bounds are furthermore used in ranking mutual funds and as a diagnostic instrument for evaluating candidate performance measures. Each essay carefully establishes the empirical relevancy of the proposed methodologies. These extensions of the SDF framework provide important new insights and have numerous finance applications for academic researchers and practitioners.
Author: Hongxiang Xu
Publisher:
ISBN:
Category :
Languages : en
Pages : 106
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Book Description
This thesis uses high-frequency data to estimate the stochastic discount factor. The high-frequency data used is sampled at one-second frequency. The fundamental equation of asset pricing is based on the continuous-time no-arbitrage theory. For empirical estimation, I apply the general method of moments to estimate the market price of risk for the risk factors, which consist of exchange-traded funds (ETFs). In Chapter 1, I estimate a one-factor model using the ETF SPY (an SPDR ETF that tracks S&P 500 index) as the risk factor. The estimated risk prices are significant over 2/3 of the sample, and the time series shows plausible patterns of the overall riskiness of the market. An additional factor using IWM (the Russell 2000 ETF that tracks the performance of the small-cap equity market) as the second factor is incorporated into the model in Chapter 2 to arrive at a two-factor model. Adding IWM improves the performance of the model and the estimation precision substantially: the risk price of SPY is almost always significant and the risk price of IWM is significant for about 2/3 of the sample. In Chapter 3 I extend the two-factor model by adding a third factor. Adding a third factor improves the performance of the model to a modest extent, but the large-cap factor SPY followed by the small-cap factor IWM are predominant.
Author: Pooya Nazeran
Publisher:
ISBN:
Category :
Languages : en
Pages : 138
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Book Description
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.
Author: Jason Yue Zhu
Publisher:
ISBN:
Category :
Languages : en
Pages :
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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.
Author: Bosung Jang
Publisher:
ISBN:
Category : Assets (Accounting)
Languages : en
Pages : 140
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Book Description
This dissertation studies how asset prices are related to various macroeconomic and financial factors. In the first chapter, I examine the influence of external financing costs on growth and asset prices. Using U.S. high-tech firm data and the aggregate financing cost measure of Eisfeldt and Muir (2016), I find that an increase in financing cost can have negative effects on R&D by reducing equity finance. This result suggests that financing cost can have substantial impacts on long-run productivity through the R&D channel. Motivated by this idea, I construct a general equilibrium model where financing costs affect innovation activities and future productivity. My model endogenously generates long-run risk and matches key features of macroeconomic and asset price data. The model produces a sizable equity premium, doing a good job of matching macro moments in the data. Furthermore, a large risk premium of R&D-intensive stocks is justified in the model as in the data. In addition, as a higher financing cost forecasts lower productivity growth in the model, this prediction is supported by empirical evidence. In the second chapter, I investigate whether heterogeneity between domestic and foreign households can help explain the cross-section of stock returns. For this analysis, I apply Yogo’s (2006) durable consumption model to a two-country setting using Korean stock market data. In Korea, U.S. investors have been a dominant foreign investor group, given that the total share of foreigners is considerably large. By incorporating the stochastic discount factor of the U.S. into the model, I find that it plays a significant role in pricing assets. In particular, our model is successful in accounting for the expected excess return of relatively high book-to-market equity groups, producing lower pricing errors than the Fama-French 3 factor model. In the third chapter, I study the effects of debt maturity choice on stock returns and financial structure. I construct a model where firms can issue both short-term and long-term bonds, subject to collateral constraints. I also assume that, when they run financial deficits, firms use equity finance paying issuance costs. The model performs well in matching empirical facts about stock returns and the financial structure of firms. In addition, the model provides an interesting implication that firms substitute between leverage and maturity. In the literature, theoretical explanations for the substitution relationship have been mainly based on conflicts between stakeholders. Without hinging on the contract-theoretic approach, my model replicates the theoretical prediction.
Author: Seung Min Yae
Publisher:
ISBN: 9781267611055
Category :
Languages : en
Pages : 109
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Book Description
Prevailing theories suggest that the quality of information about the macroeconomy is an important determinant of the equity premium. However, the empirical relationship between information quality and the equity premium has been obscured by the difficulty of measuring time-varying information quality, which is dynamically intertwined with uncertainty. By solving a nonlinear filtering problem, I extract the time series of information quality from data on professional forecasters, while differentiating information quality from uncertainty, volatility, and heterogeneity. Surprisingly, I find that the equity premium increases with information quality in contrast to standard asset pricing theory. In the U.S. stock market, a one-standard-deviation increase in information quality predicts a 3% increase in quarterly excess market returns with R2 up to 7.4% (and out-of-sample R2 up to 6.1%). Furthermore, information quality outperforms all the predictors studied in Goyal and Welch (2008) both in-sample and out-of-sample, while improving the return predictability by the dividend-price ratio. This strong evidence of return predictability is very robust to various specifications. Also, information quality is negatively correlated with the real risk-free rate, as opposed to the predictions of standard asset pricing theory. I propose a stylized consumption-based model of ambiguity aversion to future signals. This new model can simultaneously account for all these empirical findings and the equity premium puzzle, unlike the existing preferences: CRRA, Epstein-Zin, and ambiguity aversion to the mean growth rate, introduced by Ju and Miao (2012). Three components of the model-implied stochastic discount factor—information quality, uncertainty, and aggregate signal—explain up to 83% of the cross-sectional variation in the average returns on size, book-to-market, and momentum-sorted portfolios.
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: Ngoc-Khanh Tran
Publisher:
ISBN:
Category :
Languages : en
Pages : 244
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This thesis consists of three chapters in asset pricing. Chapter 1 considers an international asset pricing setting with traded and non-traded out puts. It shows that output fluctuations in nontraded industries are a central risk factor driving asset prices in all countries. This is because nontraded industries entail a growth risk that is mostly non-diversifiable, and constitute the largest component of gross domestic product (GDP) of a country. Supportive empirical evidences include; (i) the effect of an industry's growth volatility on the interest rate increases significantly with its non-tradability and (ii) carry trade strategies employing currency portfolios sorted on nontraded output growth volatility earns a sizable mean return and Sharpe ratio for US investors. Chapter 2 considers heterogeneous-agent setting in which agents differ in risk preference, time preference and/or expectations. It shows that, because of equilibrium risk sharing, the precautionary savings motive in the aggregate can vastly exceed that of even the most prudent actual agent in the economy. Consequently, a low real interest rate, resulting from large aggregate savings, can prevail with reasonable risk aversions for all agents. However, as savings rates become extremely sensitive to output fluctuation when savings motive is large, tie same mechanism that produces realistically low interest rates tends to make them unrealistically volatile. A powerful isomorphism allows differences in time preference and expectations to be swept away in the analysis, yielding an equivalent economy whose agents differ merely in risk aversion. Chapter 3 considers a novel tractable and structural pricing framework. It shows that any risk-neutral statistical distribution of state variables can be consistently tied to the economic contents of the underlying pricing model. It establishes this structural linkage by requiring that the economy's stochastic discount factor (SDF) be a proper but unspecified function of the state variables. Consequently, the structural content of the economy as characterized by the SDF can he determined from state variables dynamics through a simple linear differential equation. As a result, state variables' distribution in physical measure can also be recovered,
Author: Eric Marius Pondi Endengle
Publisher:
ISBN:
Category :
Languages : en
Pages :
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This thesis has three chapters in which I develop tools for comparisons and dynamic analysis of linear asset pricing models. In the first chapter, I introduce the notion of dynamically useless factors: factors that may be useless (uncorrelated with the assets returns) at some periods of time, while relevant at other periods of time. This notion bridges the literature on classical empirical asset pricing and the literature on useless factors, where both assume that the relevance of a factor remains constant through time. In this new framework, I propose a modified Fama-Macbeth procedure to estimate the time-varying risk premia from conditional linear asset pricing models. At each date, my estimator consistently estimates the conditional risk premium for every useful factor and is robust to the presence of the dynamically useless ones. I apply this methodology to the Fama-French five-factor model and find that, with the exception of the market, all the factors of this model are dynamically useless, although they remain useful 90 percent of the time. In the second chapter, I infer the time-varying parameters of a potentially misspecified stochastic discount factor (SDF) model. I extend the model of Gospodinov et al. (2014) to the framework of conditional SDF models, as the coefficients and the covariances are allowed to vary over time. The proposed misspecification-robust inference is able to eliminate the negative effects of potential useless factors, while maintaining the relevance of the useful ones. Empirically, I analyze the dynamical relevance of each factor in seven common asset pricing models from 1963 to 2016. The Fama-French's three-factor model (FF3) and five factor model (FF5) have been the overall best SDFs in the last 50 years. However, since 2000, the best SDF is CARH (FF3 + momentum factor), followed by FF5 as the second best. Apart from traded factors, the results bring a nuance on non-traded factors. We analyze the relevance, for linear pricing, of a human capital model inspired by Lettau & Ludvigson (2001) and Gospodinov et al. (2014). The third chapter proposes a method for ranking Fama-French linear factor models according to investors' preference for higher-order moments. I show that adding a new Fama- French factor to a prior Fama-French model systematically leads to a better model, only when the preference for higher-order moments is moderate (in absolute value). When the preference for higher-order moments is important or extreme, the four-factor model of Carhart (1997) has a better pricing ability than all the Fama-French models. An analysis of models with non-traded factors confirms the relevance, for linear pricing, of the human capital model analyzed in the second chapter. However, I show that this relevance is effective only for investors with null or very low preferences for higher-order moments.
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.
