市场摩擦对特质风险溢价的影响效应——基于A股主板市场的实证分析

与国外发达市场相比,我国A股主板市场的市场摩擦因素对市场微观结构和资产定价的影响更大。在防范和化解系统性风险的过程中,进一步分析市场摩擦如何作用于特质风险定价效应的问题具有重要的理论和现实意义。本文通过采用多维市场摩擦指标来代理信息不对称、交易成本、买卖限制、卖空限制、风险对冲和外部冲击,检验中国股市特质风险和预期收益率的关系,并判断出市场摩擦因素间的差异性影响机制。回归发现,市场摩擦和特质风险因子(特质波动率和特质偏度)都具有定价效应。各维度市场摩擦因素降低了股票流动性,进而增强了特质波动率的负向定价效应,部分解释了“特质波动率之谜”,但市场摩擦对特质偏度因子溢价的影响较为微弱。同时,基于特质波动率和特质偏度因子的投资策略能够产生超越CAPM、三因子和五因子模型的绝对收益,并印证了市场摩擦对特质风险因子绝对收益的影响作用。     

共同风险贡献还是异质性风险传染——来自全球股市的新证据北大核心

本文基于广义动态因子模型(GDFM)识别全球股市波动率的共同因子与异质性因子,刻画波动率共同因子与异质性因子的脉冲响应曲线与风险贡献变动,运用长期方差分解网络(LVDN)方法构建全球股市异质性风险传染网络,测度极端事件期间全球股市的异质性风险传染效应,追溯风险传染的源头。结果表明:全球股市波动率共同因子与异质性因子走势间呈现出“协同效应”,在极端事件期间,发达经济体股市波动率异质性因子迅速攀升。在标准冲击下,全球股市波动率共同因子震荡周期约为7天,说明共同风险对于各经济体股市的作用机制以短期冲击效应为主。然而,全球股市波动率异质性因子脉冲响应曲线具有响应程度低与收敛速度慢的特性,在极端事件冲击下,全球股市长期因果网络节点分布具备“高度聚类”属性,在剔除过度识别因素后,运用阈值约束方法求解全球股市异质性风险波动溢出净值发现,美国股市仍然是全球股市异质性风险的主要输出方。     

银行系统性风险传染机制的研究与实证——基于资产价格波动视角

本文从资产价格波动的视角,通过对不同市场中银行资产价格变动的分析,构建相应资产价格模型,阐述银行系统性风险的传染机制理论。并采用动态面板数据模型对中国2006～2009年的数据进行检验,得出结论:银行不良资产是影响银行系统性风险的重要因素,不良资产的增加导致银行贷款规模下降。本文的研究,对进一步理解银行系统性风险传染的微观基础,防范和抑制系统性风险传染,减少银行业损失具有一定的理论和现实意义。     

信息不对称风险被定价了吗?

2021年上市公司年报披露大幕即将开启。信息不对称风险是否被定价了?市场能否从披露前股票价格波动中获取有益信息呢?本文基于价格的度量方法,即衡量信息泄露风险,捕捉非知情投资者信息不对称性的异常特质波动率,从而推断出信息不对称风险确实会被定价。     

债券定价机制中的税收效应分析

债券定价模型阐释了在假定无交易税的条件下债券资产的定价机制.然而,交易税与所得税都会影响债券资产价格.拓展的债券含税定价模型能够揭示资产定价机制中的税收效应机理,并反映资产定价机制中的税收因素与风险因素的相互作用过程.     

锚定信念、投资者行为与风险资产定价

锚定效应影响投资者信念的更新进而影响风险资产定价。本文以Ornstein-Uhlenbeck过程刻画投资者锚定信念的均值回归过程,构建基于异质锚定信念的资产定价模型,推导出持有不同锚定信念的投资者对风险资产的需求及资产均衡价格。通过数值模拟研究表明：投资者锚定信念对风险资产均衡价格的形成产生不对称作用;异质锚定信念影响投资者对风险资产持有行为,进而对市场流动性、波动性和市场效率产生影响。     

信贷资产证券化缓解银行风险承担水平了吗？——来自中国银行业的经验证据

本文选取中国2011~2018年42家商业银行年度平衡面板数据,通过GMM动态面板回归和DID双重差分模型实证分析信贷资产证券化对银行风险承担的短期影响机制和政策变量的中长期影响效应。研究发现:信贷资产证券化初始阶段对不良贷款率具有显著的正向提升作用,信贷风险未得到真正剥离或转移,信贷资产证券化发行银行可通过提升自身杠杆率水平、更多持有高风险的不良资产、扩大信贷规模等方式导致不良贷款率上升,银行经营呈现出一定的道德风险;中长期来看,信贷资产证券化由审批制向注册制转变、不良资产证券化等政策变量对缓解和降低银行不良贷款率具有显著的促进效应。     

杠杆比率、资产价格泡沫和银行信贷风险

本文引入杠杆比率对Allen和Gale(2000)基于信贷扩张资产价格泡沫模型进行了拓展,分析了杠杆比率与资产价格泡沫、银行信贷风险的关系。分析表明杠杆比率是资产价格泡沫和银行信贷风险产生的关键因素。中央银行在应对资产价格波动时,要采取信贷规模控制手段,重视信贷中杠杆比率的大小。     

金融冲击、资本流动与风险传染——基于动态随机一般均衡的分析框架

2008年金融危机造成了多国经济的外部失衡,中国正处于资本开放的进程中,应当趋利避害。本文在Obstfeld和Rogoff(1995;1996)基础上引入资本变量,建立了动态随机一般均衡的开放经济两国模型,研究包括汇率和资产价格波动在内的金融渠道的风险传染。通过数值模拟发现,外币对本币汇率和外国资产价格的波动都促成了风险的跨国传染,引起两国宏观经济变量的动态变化;资本流动性的强弱会改变风险波及两国消费物价指数和资本收益率的强度,却无法影响两国总产出、消费指数和资本投入所受到的金融渠道的风险传染。因此,在深化经济发展的过程中,要适当鼓励金融创新,提供合理规避风险的手段,要有序推进资本开放,健全金融体系。     

货币政策效应研究中VAR族模型的使用基础——基于Arrow-Debreu框架的分析

本文从货币政策的财富效应和流动性效应入手,分析了研究货币政策冲击对经济金融运行影响时使用VAR族模型的微观理论基础;并通过构建了资产定价模型,对货币政策的"财富效应"、"流动性效应"及资产定价之间的关系进行了理论研究。结果表明,货币政策的"财富效应"与"流动性效应"通过影响投资者的相对风险厌恶系数以及其自身与无风险资产收益率之间的关系来影响资产的理论价格。     

Is Volatility Risk for the British Pound Priced in U.S. Options Markets?

This paper estimates the premium for volatility risk for European currency options written on British pounds. The average annualized premium for volatility risk is neither statistically different from zero nor invariant to the option's moneyness. However, the risk premium is positively and nonproportionaly related to the level of volatility, except for out‐of‐the‐money options. Finding a zero premium for volatility risk does not undermine the assumption of a zero‐price volatility risk in many extant stochastic‐volatility option pricing models and the option pricing formulas in those models.     

Understanding the price of volatility risk in carry trades

This paper investigates the cross-sectional pricing ability of the short- and long-run components of global foreign exchange (FX) volatility for carry trade returns. We find a negative and statistically significant factor risk price for the long-run component, but no significant pricing effect due to the short-run volatility component. We also document that the dynamics of the long-run component of global FX volatility are related to US macroeconomic fundamentals. Our results are robust to various parametrizations of the volatility models used to obtain the volatility components and they are invariant to alternative asset pricing testing methodologies and sample periods.     

Computing the market price of volatility risk in the energy commodity markets

In this paper, we demonstrate the need for a negative market price of volatility risk to recover the difference between Black–Scholes [Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637–654]/Black [Black, F., 1976. Studies of stock price volatility changes. In: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, pp. 177–181] implied volatility and realized-term volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative market price of volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical volatility in both equity and commodity-energy markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a 10 year period, we estimate the volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher volatility risk premiums. Computing such a negative market price of volatility risk highlights the importance of volatility risk in understanding priced volatility in these financial markets.     

A Comparison of Option Prices Under Different Pricing Measures in a Stochastic Volatility Model with Correlation

This paper investigates option prices in an incomplete stochastic volatility model with correlation. In a general setting, we prove an ordering result which says that prices for European options with convex payoffs are decreasing in the market price of volatility risk.As an example, and as our main motivation, we investigate option pricing under the class of q-optimal pricing measures. The q-optimal pricing measure is related to the marginal utility indifference price of an agent with constant relative risk aversion. Using the ordering result, we prove comparison theorems between option prices under the minimal martingale, minimal entropy and variance-optimal pricing measures. If the Sharpe ratio is deterministic, the comparison collapses to the well known result that option prices computed under these three pricing measures are the same.As a concrete example, we specialize to a variant of the Hull-White or Heston model for which the Sharpe ratio is increasing in volatility. For this example we are able to deduce option prices are decreasing in the parameter q. Numerical solution of the pricing pde corroborates the theory and shows the magnitude of the differences in option price due to varying q.JEL Classification: D52, G13     

The pricing of volatility risk in the US equity market

We analyze whether the pricing of volatility risk depends on the asset pricing framework applied in the tests, the specified volatility proxies, and the portfolio sorts used for spanning the asset universe. For this purpose, we compare the results using a macroeconomic and fundamental based asset pricing model using three proxies of volatility and uncertainty, using size/value sorted and industry sector portfolios. Our results reveal that the marginal pricing effect of the VIX volatility factor is strong and statistically significant throughout the models and specifications, while the effect of an EGARCH-based volatility factor is mixed, mostly smaller but with the correct sign. In most cases, the EGARCH factor does not impair the pricing effect of the VIX. The portfolio sorts have a substantial impact on the volatility premiums in both model frameworks. The size of the volatility risk premium is more uniform across the models if the industry sector portfolio sort is used. Finally, the size/value portfolio sort generates larger volatility risk premiums for both models.     

Firm size and volatility analysis in the Spanish stock market

Using Spanish stock market data, this paper examines volatility spillovers between large and small firms and their impact on expected returns. By using a conditional capital asset pricing model (CAPM) with an asymmetric multivariate GARCH-M covariance structure, it is shown that there exist bidirectional volatility spillovers between both types of companies, especially after bad news. After estimating the model, a positive and significant price of risk is obtained. This result is consistent with the volatility feedback effect, one of the most popular explanations of the asymmetric volatility phenomenon, and explains why risk premiums are much more sensitive to negative return shocks coming from the whole market or other related markets.     

Modeling the business risk of financially weakened firms: A new approach for corporate bond pricing

Most structural models of default risk assume that the firm's asset return is normally distributed, with a constant volatility. By contrast, this article details the properties that the process of assets should have in the case of financially weakened firms. It points out that jump-diffusion processes with time-varying volatility provide a refined and accurate perspective on the business risk dimension of default risk. Representative Arrow-Debreu state price densities (SPD) and term structures of credit spreads are then explored. The credit curves show that the business uncertainties play a major in the pricing of corporate liabilities.     

Option Profit and Loss Attribution and Pricing: A New Framework

This paper develops a new top-down valuation framework that links the pricing of an option investment to its daily profit and loss attribution. The framework uses the Black-Merton-Scholes option pricing formula to attribute the short-term option investment risk to variation in the underlying security price and the option's implied volatility. Taking risk-neutral expectation and demanding no dynamic arbitrage result in a pricing relation that links an option's fair implied volatility level to the underlying volatility level with corrections for the implied volatility's own expected direction of movement, its variance, and its covariance with the underlying security return.     

Financial Modeling in a Fast Mean-Reverting Stochastic Volatility Environment

We present a derivative pricing and estimation methodology for a class of stochastic volatility models that exploits the observed 'bursty' or persistent nature of stock price volatility. Empirical analysis of high-frequency S&P 500 index data confirms that volatility reverts slowly to its mean in comparison to the tick-by- tick fluctuations of the index value, but it is fast mean- reverting when looked at over the time scale of a derivative contract (many months). This motivates an asymptotic analysis of the partial differential equation satisfied by derivative prices, utilizing the distinction between these time scales. The analysis yields pricing and implied volatility formulas, and the latter provides a simple procedure to 'fit the skew' from European index option prices. The theory identifies the important group parameters that are needed for the derivative pricing and hedging problem for European-style securities, namely the average volatility and the slope and intercept of the implied volatility line, plotted as a function of the log- moneyness-to-maturity-ratio. The results considerably simplify the estimation procedure. The remaining parameters, including the growth rate of the underlying, the correlation between asset price and volatility shocks, the rate of mean-reversion of the volatility and the market price of volatility risk are not needed for the asymptotic pricing formulas for European derivatives, and we derive the formula for a knock-out barrier option as an example. The extension to American and path-dependent contingent claims is the subject of future work. This revised version was published online in August 2006 with corrections to the Cover Date.     

Asymmetric Volatility,Skewness, and Downside Risk in Different Asset Classes: Evidence from Futures Markets

This study examines the cross‐sectional variation of futures returns from different asset classes. The monthly returns are positively correlated with downside risk and negatively correlated with coskewness. The asymmetric volatility effect generates negatively skewed returns. Assets with high coskewness and low downside betas provide hedges against market downside risk and offer low returns. The high returns offered by assets with low coskewness and high downside betas are a risk premium for bearing downside risk. The asset pricing model that incorporates downside risk partially explains the futures returns. The results indicate a unified risk perspective to jointly price different asset classes.