Quantitative Asset Pricing Implications of Endogenous Solvency Constraints

We study the asset pricing implications of an economy wheresolvency constraints are endogenously determined to deter agentsfrom defaulting while allowing as much risk sharing as possible.We solve analytically for efficient allocations and for thecorresponding asset prices, portfolio holdings, and solvencyconstraints for a simple example. Then we calibrate a more generalmodel to U.S. aggregate as well as idiosyncratic income processes.We find equity premia, risk premia for long-term bonds, andSharpe ratios of magnitudes similar to the U.S. data for lowriskaversion and a lowtime-discount factor.     

Asset Pricing with Dynamic Margin Constraints

This paper provides a novel theoretical analysis of how endogenous time‐varying margin requirements affect capital market equilibrium. I find that margin requirements, when there are no other market frictions, reduce the volatility and correlation of returns as well as the risk‐free rate, but increase the market price of risk, the risk premium, and the price of risky assets. Furthermore, margin requirements generate a strong cross‐sectional dispersion of stock return volatilities. The results emphasize that a general equilibrium analysis may reverse the conclusions of a partial equilibrium analysis often employed in the literature.     

Labor Mobility: Implications for Asset Pricing

Labor mobility is the flexibility of workers to walk away from an industry in response to better opportunities. I develop a model in which labor flows make bad times worse for shareholders who are left with capital that is less productive. The model shows that firms face greater operating leverage by providing flexibility to mobile workers. I construct an empirical measure of labor mobility consistent with the model and document an economically significant cross‐sectional relation between mobility, operating leverage, and stock returns. I find that firms in mobile industries earn returns over 5% higher than those in less mobile industries.     

Networks in Production: Asset Pricing Implications

In this paper, I examine asset pricing in a multisector model with sectors connected through an input‐output network. Changes in the network are sources of systematic risk reflected in equilibrium asset prices. Two characteristics of the network matter for asset prices: network concentration and network sparsity. These two production‐based asset pricing factors are determined by the structure of the network and are computed from input‐output data. Consistent with the model predictions, I find return spreads of 4.6% and ?3.2% per year on sparsity and concentration beta‐sorted portfolios, respectively.     

Labor‐Technology Substitution: Implications for Asset Pricing

This paper studies the asset pricing implications of a firm's opportunities to replace routine‐task labor with automation. I develop a model in which firms optimally undertake such replacement when their productivity is low. Hence, firms with routine‐task labor maintain a replacement option that hedges their value against unfavorable macroeconomic shocks and lowers their expected returns. Using establishment‐level occupational data, I construct a measure of firms' share of routine‐task labor. Compared to their industry peers, firms with a higher share of routine‐task labor (i) invest more in machines and reduce more routine‐task labor during economic downturns, and (ii) have lower expected stock returns.     

Asset Pricing Implications of Nonconvex Adjustment Costs and Irreversibility of Investment

This paper derives a real options model that accounts for the value premium. If real investment is largely irreversible, the book value of assets of a distressed firm is high relative to its market value because it has idle physical capital. The firm's excess installed capital capacity enables it to fully benefit from positive aggregate shocks without undertaking costly investment. Thus, returns to equity holders of a high book‐to‐market firm are sensitive to aggregate conditions and its systematic risk is high. Simulations indicate that the model goes a long way toward accounting for the observed value premium.     

Trading Volume: Implications of an Intertemporal Capital Asset Pricing Model

We derive an intertemporal asset pricing model and explore its implications for trading volume and asset returns. We show that investors trade in only two portfolios: the market portfolio, and a hedging portfolio that is used to hedge the risk of changing market conditions. We empirically identify the hedging portfolio using weekly volume and returns data for U.S. stocks, and then test two of its properties implied by the theory: Its return should be an additional risk factor in explaining the cross section of asset returns, and should also be the best predictor of future market returns.     

On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing

We investigate the empirical implications of using various measures of payout yield rather than dividend yield for asset pricing models. We find statistically and economically significant predictability in the time series when payout (dividends plus repurchases) and net payout (dividends plus repurchases minus issuances) yields are used instead of the dividend yield. Similarly, we find that payout (net payout) yields contains information about the cross section of expected stock returns exceeding that of dividend yields, and that the high minus low payout yield portfolio is a priced factor.     

Asset Pricing with Garbage

A new measure of consumption, garbage, is more volatile and more correlated with stocks than the canonical measure, National Income and Product Accounts (NIPA) consumption expenditure. A garbage‐based consumption capital asset pricing model matches the U.S. equity premium with relative risk aversion of 17 versus 81 and evades the joint equity premium‐risk‐free rate puzzle. These results carry through to European data. In a cross‐section of size, value, and industry portfolios, garbage growth is priced and drives out NIPA expenditure growth.     

Multifactor Asset Pricing Models

Since the early 1960s, the mean-variance Capital Asset Pricing Model (CAPM) has been a dominant paradigm in modern finance. Recently, the accumulation of anomalous evidence, and a realisation that empirical tests of the model are tautologically related to the efficiency of the market index, have pushed that paradigm to a point of crisis. This paper reviews alternative asset pricing models which coexisted with the CAPM and may provide plausible substitutes. The major distinguishing feature of these models is that they predict multiple risk factors and, with the exception of the Arbitrage Pricing Theory (APT), are extensions of the CAPM.     

Asset Pricing without Garbage

This paper provides an explanation for why garbage implies a much lower relative risk aversion in the consumption‐based asset pricing model than National Income and Product Accounts (NIPA) consumption expenditure: Unlike garbage, NIPA consumption is filtered to mitigate measurement error. I apply a simple model of the filtering process that allows one to undo the filtering inherent in NIPA consumption. “Unfiltered NIPA consumption” well explains the equity premium and is priced in the cross‐section of stock returns. I discuss the likely properties of true consumption (i.e., without measurement error and filtering) and quantify implications for habit and long‐run risk models.     

Comparing Asset Pricing Models

A Bayesian asset pricing test is derived that is easily computed in closed form from the standard F‐statistic. Given a set of candidate traded factors, we develop a related test procedure that permits the computation of model probabilities for the collection of all possible pricing models that are based on subsets of the given factors. We find that the recent models of Hou, Xue, and Zhang (2015a, 2015b) and Fama and French (2015, 2016) are dominated by a variety of models that include a momentum factor, along with value and profitability factors that are updated monthly.     

Technological Growth and Asset Pricing

We study the asset‐pricing implications of technological growth in a model with “small,” disembodied productivity shocks and “large,” infrequent technological innovations, which are embodied into new capital vintages. The technological‐adoption process leads to endogenous cycles in output and asset valuations. This process can help explain stylized asset‐valuation patterns around major technological innovations. More importantly, it can help provide a unified, investment‐based theory for numerous well‐documented facts related to excess‐return predictability. To illustrate the distinguishing features of our theory, we highlight novel implications pertaining to the joint time‐series properties of consumption and excess returns.     

动态资本资产定价理论评述

本文主要讨论了动态资产定价理论的产生和发展.默顿和布里登使用贝尔曼开创的动态规划方法和伊藤随机分析技术,重新考察在由随机过程驱动的不确定环境下,个人如何连续地做出消费/投资决策,使得终身效用最大化.无须单期框架中的严格假定,他们也获得了连续时间跨期资源配置的一般均衡模型--时际资产定价模型(ICAPM)以及消费资产定价模型(CCAPM).这些工作开启了连续时间金融方法论的新时代.     

Valuation Risk and Asset Pricing

Standard representative‐agent models fail to account for the weak correlation between stock returns and measurable fundamentals, such as consumption and output growth. This failing, which underlies virtually all modern asset pricing puzzles, arises because these models load all uncertainty onto the supply side of the economy. We propose a simple theory of asset pricing in which demand shocks play a central role. These shocks give rise to valuation risk that allows the model to account for key asset pricing moments, such as the equity premium, the bond term premium, and the weak correlation between stock returns and fundamentals.     

On Comparing Asset Pricing Models

Revisiting the framework of (Barillas, Francisco, and Jay Shanken, 2018, Comparing asset pricing models, The Journal of Finance 73, 715–754). BS henceforth, we show that the Bayesian marginal likelihood-based model comparison method in that paper is unsound : the priors on the nuisance parameters across models must satisfy a change of variable property for densities that is violated by the Jeffreys priors used in the BS method. Extensive simulation exercises confirm that the BS method performs unsatisfactorily. We derive a new class of improper priors on the nuisance parameters, starting from a single improper prior, which leads to valid marginal likelihoods and model comparisons. The performance of our marginal likelihoods is significantly better, allowing for reliable Bayesian work on which factors are risk factors in asset pricing models.     

Uncertainty and Financing Constraints

Using a panel of Dutch listed firms this paper provides empirical evidence for the hypothesis that more risky firms are confronted with more severe capital market constraints than relatively less risky firms. The paper also contributes to the discussion on the usefulness of cash flow as a measure of financial constraints. We present a stochastic version of the Kaplan-Zingales (1997) model. We show that cash flow sensitivity can be used as a meaningful indicator of financing constraints if firms are classified by the degree of uncertainty they face and if the uncertainty originates from cost uncertainty.     

Investor Psychology and Asset Pricing

The basic paradigm of asset pricing is in vibrant flux. The purely rational approach is being subsumed by a broader approach based upon the psychology of investors. In this approach, security expected returns are determined by both risk and misvaluation . This survey sketches a framework for understanding decision biases, evaluates the a priori arguments and the capital market evidence bearing on the importance of investor psychology for security prices, and reviews recent models.     

Co-Kurtosis and Capital Asset Pricing

This paper examines the impact of co-kurtosis on asset pricing using a four-moment capital asset pricing model. It is shown that, in the presence of skewness and kurtosis in asset return distribution, the expected excess rate of return is related not only to the systematic variance but also to the systematic skewness and systematic kurtosis. Investors are compensated in higher expected return for bearing the systematic variance and the systematic kurtosis risks. Investors also forego the expected excess return for taking the benefit of increasing the systematic skewness.     

Diagnosing Asset Pricing Models Using the Distribution of Asset Returns

This paper develops a set of diagnostic tests which can shed light on why a particular model is failing and indicate what steps might be taken to make the model consistent with asset returns. Theoretical bounds on the moments of a stochastic discount factor are derived as a function of the moments of observed asset returns. Particular attention is paid to restrictions on moments other than the variance. These bounds can also be used to measure the information about the distribution of the discount factor contained in the moments of various asset returns. As an application of this methodology, bounds on the discount factor are estimated using size-based portfolios, and the results are used to analyze the small firm effect. Empirical results indicate, for the period 1926–1975, that moments of the returns of small firms contain information about the discount factor that is not contained in the moments of the returns of large firms and/or a proxy of the aggregate wealth portfolio. However, this difference disappears when more recent data is included.