Ambiguity,Learning, and Asset Returns

We propose a novel generalized recursive smooth ambiguity model which permits a three‐way separation among risk aversion, ambiguity aversion, and intertemporal substitution. We apply this utility model to a consumption‐based asset‐pricing model in which consumption and dividends follow hidden Markov regime‐switching processes. Our calibrated model can match the mean equity premium, the mean risk‐free rate, and the volatility of the equity premium observed in the data. In addition, our model can generate a variety of dynamic asset‐pricing phenomena, including the procyclical variation of price–dividend ratios, the countercyclical variation of equity premia and equity volatility, the leverage effect, and the mean reversion of excess returns. The key intuition is that an ambiguity‐averse agent behaves pessimistically by attaching more weight to the pricing kernel in bad times when his continuation values are low.     

Nonparametric Stochastic Discount Factor Decomposition

Stochastic discount factor (SDF) processes in dynamic economies admit a permanent‐transitory decomposition in which the permanent component characterizes pricing over long investment horizons. This paper introduces an empirical framework to analyze the permanent‐transitory decomposition of SDF processes. Specifically, we show how to estimate nonparametrically the solution to the Perron–Frobenius eigenfunction problem of Hansen and Scheinkman, 2009. Our empirical framework allows researchers to (i) construct time series of the estimated permanent and transitory components and (ii) estimate the yield and the change of measure which characterize pricing over long investment horizons. We also introduce nonparametric estimators of the continuation value function in a class of models with recursive preferences by reinterpreting the value function recursion as a nonlinear Perron–Frobenius problem. We establish consistency and convergence rates of the eigenfunction estimators and asymptotic normality of the eigenvalue estimator and estimators of related functionals. As an application, we study an economy where the representative agent is endowed with recursive preferences, allowing for general (nonlinear) consumption and earnings growth dynamics.     

定价核之谜与概率权重函数

采用期权及标的资产价格数据, 基于离散时间EGARCH模型和连续时间GARCH扩散模型分别估计了客观与风险中性密度, 进而推导了经验定价核. 在此基础上, 基于等级依赖期望效用模型, 在标准的效应函数形式下构建了相应的概率权重函数. 采用香港恒生指数及其指数权证价格数据进行实证研究, 结果表明: (1) 经验定价核不是单调递减的, 而是展现出驼峰(非单调性), 即“定价核之谜”;(2) 经验概率权重函数展现S型, 表明市场投资者低估尾部概率事件, 高估中、高概率事件;(3) “定价核之谜”可以由具有标准效用函数与S型概率权重函数的等级依赖期望效用模型解释。     

Efficient Semiparametric Estimation of the Fama–French Model and Extensions

This paper develops a new estimation procedure for characteristic‐based factor models of stock returns. We treat the factor model as a weighted additive nonparametric regression model, with the factor returns serving as time‐varying weights and a set of univariate nonparametric functions relating security characteristic to the associated factor betas. We use a time‐series and cross‐sectional pooled weighted additive nonparametric regression methodology to simultaneously estimate the factor returns and characteristic‐beta functions. By avoiding the curse of dimensionality, our methodology allows for a larger number of factors than existing semiparametric methods. We apply the technique to the three‐factor Fama–French model, Carhart's four‐factor extension of it that adds a momentum factor, and a five‐factor extension that adds an own‐volatility factor. We find that momentum and own‐volatility factors are at least as important, if not more important, than size and value in explaining equity return comovements. We test the multifactor beta pricing theory against a general alternative using a new nonparametric test.     

EXPECTATIONS‐BASED REFERENCE‐DEPENDENT PREFERENCES AND ASSET PRICING

This paper explores the quantitative asset‐pricing implications of expectations‐based reference‐dependent preferences, as introduced by Koszegi and Rabin (2009, American Economic Review, 99(3), 909–936), in an otherwise traditional Lucas‐tree model. I find that the model easily succeeds in matching the historical equity premium and its variability when the preference parameters are calibrated in line with micro evidence. The equity premium is high because expectations‐based loss aversion makes uncertain fluctuations in consumption more painful. Additionally, loss aversion introduces variation in returns because unexpected cuts in consumption are particularly painful, and the agent wants to postpone such cuts to let his reference point decrease. This variation generates strong predictability. However, it also causes counterfactually high volatility in the risk‐free rate, which I address by allowing for variation in expected consumption growth, heteroskedasticity in consumption growth, time‐variant disaster risk, and sluggish belief updating.     

The Role of Automatic Stabilizers in the U.S. Business Cycle

Most countries have automatic rules in their tax‐and‐transfer systems that are partly intended to stabilize economic fluctuations. This paper measures their effect on the dynamics of the business cycle. We put forward a model that merges the standard incomplete‐markets model of consumption and inequality with the new Keynesian model of nominal rigidities and business cycles, and that includes most of the main potential stabilizers in the U.S. data and the theoretical channels by which they may work. We find that the conventional argument that stabilizing disposable income will stabilize aggregate demand plays a negligible role in the dynamics of the business cycle, whereas tax‐and‐transfer programs that affect inequality and social insurance can have a larger effect on aggregate volatility. However, as currently designed, the set of stabilizers in place in the United States has had little effect on the volatility of aggregate output fluctuations or on their welfare costs despite stabilizing aggregate consumption. The stabilizers have a more important role when monetary policy is constrained by the zero lower bound, and they affect welfare significantly through the provision of social insurance.     

基于养老金入市的中国通胀指数债券定价与模拟

随着我国人口老龄化程度的不断加剧和养老金缺口的不断扩大,以及通货膨胀上行的压力,养老基金如何投资实现保值增值已经成为关系国家发展和社会稳定的重要课题。在此背景下本文构建了一个两因子连续时间定价模型,求解出风险中性测度下通胀指数债券的理论价格,并通过数值模拟分析了通胀指数债券对通货膨胀的抵御作用,以及名义利率、通货膨胀率、波动率和债券期限等因素对通胀指数债券价格的影响。研究表明：通胀指数债券价格与通货膨胀率、波动率正相关,与利率负相关,且波动率对通胀指数债券的影响系数要大于通货膨胀率,更大于利率;当预期通货膨胀率高于利率时,通胀指数债券会溢价发行,而且期限越长价格越高。本文的研究为养老基金多元化投资规避通胀风险、实现保值增值提供了可能,也为国家推进金融衍生工具的创新提供依据。     

“马钢”可分离转债定价实证分析

以国内首支认股权和债券分离交易的可转换公司债券——马钢可分离转债为样本,从实证角度对其进行了定价分析。考虑了这次可分离转债的特点,采用历史波动率、GARCH(1,1)模型波动率和隐含波动率3种不同估计方法来计算波动率,利用修正的B-S权证定价模型进行转债权证部分定价,并和市场真实价格进行比较,详细分析比较3种波动率的定价效果。在运用修正的B-S权证定价模型时,考虑了公司总股本价值因可分离债券发行的增加,同时采用公司股权波动率进行计算,检验了修正B-S权证定价模型对马钢认股权证定价的有效性,实证证明采用隐含波动率或GARCH模型具有较好的定价效果。     

Parametric Inference and Dynamic State Recovery From Option Panels

We develop a new parametric estimation procedure for option panels observed with error. We exploit asymptotic approximations assuming an ever increasing set of option prices in the moneyness (cross‐sectional) dimension, but with a fixed time span. We develop consistent estimators for the parameters and the dynamic realization of the state vector governing the option price dynamics. The estimators converge stably to a mixed‐Gaussian law and we develop feasible estimators for the limiting variance. We also provide semiparametric tests for the option price dynamics based on the distance between the spot volatility extracted from the options and one constructed nonparametrically from high‐frequency data on the underlying asset. Furthermore, we develop new tests for the day‐by‐day model fit over specific regions of the volatility surface and for the stability of the risk‐neutral dynamics over time. A comprehensive Monte Carlo study indicates that the inference procedures work well in empirically realistic settings. In an empirical application to S&P 500 index options, guided by the new diagnostic tests, we extend existing asset pricing models by allowing for a flexible dynamic relation between volatility and priced jump tail risk. Importantly, we document that the priced jump tail risk typically responds in a more pronounced and persistent manner than volatility to large negative market shocks.     

一类高新技术企业专利权价值的实物期权评估方法——基于跳扩散过程和随机波动率的美式期权的建模与模拟

由于经典的Black-Scholes期权定价模型的假设忽略了突发事件对资产价格的影响和"波动率微笑"对期权价值的影响而与实际情形往往存在偏差,因此学者们对Black-Scholes模型的改进则主要分别集中在带跳扩散过程的期权定价模型与具随机波动率的期权定价模型等两个方面,然而却少见将这两种模型结合起来的研究。本文首先在带跳扩散过程的期权定价模型与具随机波动率的期权定价模型的研究工作的基础上,建立了一种同时带跳扩散过程和具随机波动率的美式期权定价模型,并通过伊藤引理推导出了资产价格、随机波动率和期权满足的偏微分方程;然后,利用特征函数法和傅里叶变换导出了资产价格的随机分布,进而通过马尔科夫链方法给出了基于跳扩散过程和随机波动率的美式期权的数值解;最后,运用已建立的带跳扩散过程和随机波动率的美式期权定价模型对高新技术企业项目投资的专利权价值进行实物期权定价评估的案例研究,并对跳扩散强度参数和随机波动率参数进行敏感性分析,研究结果表明:将项目收益跳扩散过程和市场环境随机波动率加入到专利权实物期权定价模型中,可以有效避免专利权的期权价值被高估。     

时变风险厌恶下的期权定价——基于上证50ETF期权的实证研究

传统的随机波动率（SV）期权定价是在投资者具有常数风险偏好假设下进行的.但近年来越来越多的研究表明,市场参与者具有时变风险厌恶特征.基于此,本文对时变风险厌恶条件下的期权定价问题进行深入研究.首先,对传统的（非仿射）常数风险厌恶SV（CRA-SV）期权定价模型进行扩展,构建时变风险厌恶SV（TVRA-SV）期权定价模型对期权进行定价,并分析时变风险厌恶对期权价格的影响;其次,采用标的资产与期权数据信息,建立基于连续粒子滤波的极大似然估计方法,对定价模型的客观与风险中性参数进行联合估计;最后,采用我国期权市场上的上证50ETF期权数据,对构建的定价模型进行实证检验.结果表明：TVRA-SV期权定价模型相比传统的CRA-SV期权定价模型具有更好的数据拟合效果,能够更充分地刻画标的上证50ETF收益率在客观与风险中性测度下的波动性;TVRA-SV期权定价模型相比传统的Black-Scholes（B-S）期权定价模型和CRA-SV期权定价模型都具有明显更高的定价精确性。     

Estimating Long Memory in Volatility

We consider semiparametric estimation of the memory parameter in a model that includes as special cases both long‐memory stochastic volatility and fractionally integrated exponential GARCH (FIEGARCH) models. Under our general model the logarithms of the squared returns can be decomposed into the sum of a long‐memory signal and a white noise. We consider periodogram‐based estimators using a local Whittle criterion function. We allow the optional inclusion of an additional term to account for possible correlation between the signal and noise processes, as would occur in the FIEGARCH model. We also allow for potential nonstationarity in volatility by allowing the signal process to have a memory parameter d^*1/2. We show that the local Whittle estimator is consistent for d^*∈(0,1). We also show that the local Whittle estimator is asymptotically normal for d^*∈(0,3/4) and essentially recovers the optimal semiparametric rate of convergence for this problem. In particular, if the spectral density of the short‐memory component of the signal is sufficiently smooth, a convergence rate of n^2/5−δ for d^*∈(0,3/4) can be attained, where n is the sample size and δ>0 is arbitrarily small. This represents a strong improvement over the performance of existing semiparametric estimators of persistence in volatility. We also prove that the standard Gaussian semiparametric estimator is asymptotically normal if d^*=0. This yields a test for long memory in volatility.     

FINANCIAL FRAGILITY,LIQUIDITY, AND ASSET PRICES

We define a financial system to be fragile if small shocks have disproportionately large effects. In a model of financial intermediation, we show that small shocks to the demand for liquidity cause either high asset‐price volatility or bank defaults or both. Furthermore, as the liquidity shocks become vanishingly small, the asset‐price volatility is bounded away from zero. In the limit economy, with no shocks, there are many equilibria. However, if banks face idiosyncratic liquidity shocks, then the only equilibria that are robust to the introduction of small aggregate risk involve stochastic consumption as well as volatile asset, prices. (JEL: D5, D8, G2)     

Advance Selling in the Presence of Speculators and Forward‐Looking Consumers

This article examines the pricing policy of a monopolist seller who may sell in advance of consumption in a market that comprises of myopic consumers, forward‐looking consumers, and speculators. The latter group has no consumption value for the goods and is in the market with the sole objective of making a profit by reselling the purchased goods shortly after. Consumers, although homogeneous in terms of their valuations, are different with respect to their perspectives. We show that in an “upward” market where the expected valuation increases over time, the optimal pricing policy is an ex ante “static” one where the seller “prices into the future” and prices the myopic consumers out of the advance market. However, in a “downward” market where the expected valuation decreases over time, the seller adopts a dynamic pricing strategy except for the case when higher initial sales can trigger more demand subsequently and when the downward trend is not too high. In this case, the seller prefers an ex ante “static” pricing strategy and deliberately prices lower initially to sell to speculators. We identify the conditions under which the seller benefits from the existence of speculators in the market. Moreover, although the presence of entry costs is ineffective as an entry deterrence, we determine the conditions under which exit costs can rein in speculative purchase.     

Transform Analysis and Asset Pricing for Affine Jump‐diffusions

In the setting of ‘affine’ jump‐diffusion state processes, this paper provides an analytical treatment of a class of transforms, including various Laplace and Fourier transforms as special cases, that allow an analytical treatment of a range of valuation and econometric problems. Example applications include fixed‐income pricing models, with a role for intensity‐based models of default, as well as a wide range of option‐pricing applications. An illustrative example examines the implications of stochastic volatility and jumps for option valuation. This example highlights the impact on option ‘smirks’ of the joint distribution of jumps in volatility and jumps in the underlying asset price, through both jump amplitude as well as jump timing.     

Pricing and Operational Performance in Discretionary Services

In many services, for example, website or landscape design, the value or quality derived by a customer depends upon the service time, and this valuation differs across customers. Customers procure the service based on the expected value to be delivered, prices charged, and the timeliness of service. We investigate the performance of the optimal pricing scheme as well as two commonly used pricing schemes (fixed fee and time‐based pricing) for such services on important dimensions such as revenue, demand served, and utilization. We propose a novel model that captures the above features and wherein both service rate and demand are endogenous and functions of the pricing scheme. In particular, service time is an outcome of the pricing scheme adopted and the heterogeneous valuations of customers, unlike in the queueing‐based pricing literature. We find that the service system may benefit from a greater variance in consumer valuations, and the performance of pricing schemes is impacted by the shape of the distribution of customers' valuation of service time and the responsiveness desired by customers. Both the fixed fee and time‐based schemes do well relative to the optimal pricing scheme in terms of revenue in many plausible scenarios, but there are substantial differences between the pricing schemes in some important operational metrics. For instance, the fixed fee scheme serves more customers and has higher utilization than the time‐based scheme. We also explore variants of the fixed and time‐based schemes that have better revenue performance and show that the two‐part tariff which is a combination of fixed and time‐based pricing can do as well as the optimal scheme in terms of revenue.     

Joint Inventory and Pricing Coordination with Incomplete Demand Information

In retailing operations, retailers face the challenge of incomplete demand information. We develop a new concept named K‐approximate convexity, which is shown to be a generalization of K‐convexity, to address this challenge. This idea is applied to obtain a base‐stock list‐price policy for the joint inventory and pricing control problem with incomplete demand information and even non‐concave revenue function. A worst‐case performance bound of the policy is established. In a numerical study where demand is driven from real sales data, we find that the average gap between the profits of our proposed policy and the optimal policy is 0.27%, and the maximum gap is 4.6%.     

Hedging through index-based price contracts in commodity-based supply chains

This paper studies the optimal hedging strategy of risk-neutral firms in supply chain settings. We model a retailer procuring goods through index-based price contracts from two commodity processors. The processors’ input commodity prices are random and correlated. The retailer faces price-sensitive demand curves; therefore, it controls product demand through retail pricing in the final product market. We characterize the optimal contracting terms for the processors and show that they prefer to hedge part of their exposure to the commodity price risk. The optimal contract for processor comprises a processing margin independent of the commodity price volatility and a hedge ratio that is a function of the commodity price volatility and the products substitution factor. We uncover a new rationale for hedging in settings where downstream firms have pricing power; both processors and the retailer benefit from the retailer’s pricing power when their margins are linked to input prices; an index-based price contract is a means to link the processors’ and the retailer’s margins. We further investigate how different market parameters affect the optimal hedge ratios and extend our model to settings with random market sizes and asymmetric substitution for final products.     

UPSTREAM VOLATILITY IN THE SUPPLY CHAIN: THE MACHINE TOOL INDUSTRY AS A CASE STUDY

Cyclicality is a well‐known and accepted fact of life in market‐driven economies. Less well known or understood, however, is the phenomenon of amplification as one looks “upstream” in the industrial supply chain. We examine the amplification phenomenon and its implications through the lens of one upstream industry that is notorious for the intensity of the business cycles it faces: the machine tool industry. Amplification of demand volatility in capital equipment supply chains, e. g., machine tools, is particularly large relative to that seen in distribution and component parts supply chains. We present a system dynamics simulation model to capture demand volatility amplification in capital supply chains. We explore the lead‐time, inventory, production, productivity, and staffing implications of these dynamic forces. Several results stand out. First, volatility hurts productivity and lowers average worker experience. Second, even though machine tool builders can do little to reduce the volatility in their order streams through choice of forecast rule, a smoother forecasting policy will lead companies to retain more of their skilled work force. This retention of skilled employees is often cited as one of the advantages that European and Japanese companies have had relative to their U. S. competitors. Our results suggest some insights for supply chain design and management: downstream customers can do a great deal to reduce the volatility for upstream suppliers through their choice of order forecast rule. In particular, companies that use smoother forecasting policies tend to impose less of their own volatility upon their supply base and may consequently enjoy system‐wide cost reduction.     

Pareto‐Improving Contracts for Express Package Delivery Services

We address the problem of an express package delivery company in structuring a long‐term customer contract whose terms may include prices that differ by day‐of‐week and by speed‐of‐service. The company traditionally offered speed‐of‐service pricing to its customers, but without day‐of‐week differentiation, resulting in customer demands with considerable day‐of‐week seasonality. The package delivery company hoped that using day‐of‐week and speed‐of‐service price differentiation for contract customers would induce these customers to adjust their demands to become counter‐cyclical to the non‐contract demand. Although this usually cannot be achieved by pricing alone, we devise an approach that utilizes day‐of‐week and speed‐of‐service pricing as an element of a Pareto‐improving contract. The contract provides the lowest‐cost arrangement for the package delivery company while ensuring that the customer is at least as well off as he would have been under the existing pricing structure. The contract pricing smoothes the package delivery company's demand and reduces peak requirements for transport capacity. The latter helps to decrease capital costs, which may allow a further price reduction for the customer. We formulate the pricing problem as a biconvex optimization model, and present a methodology for designing the contract and numerical examples that illustrate the achievable savings.