Statistical foundations for assessing the difference between the classical and weighted-Gini betas

The ‘beta’ is one of the key quantities in the capital asset pricing model (CAPM). In statistical language, the beta can be viewed as the slope of the regression line fitted to financial returns on the market against the returns on the asset under consideration. The insurance counterpart of CAPM, called the weighted insurance pricing model (WIPM), gives rise to the so-called weighted-Gini beta. The aforementioned two betas may or may not coincide, depending on the form of the underlying regression function, and this has profound implications when designing portfolios and allocating risk capital. To facilitate these tasks, in this paper we develop large-sample statistical inference results that, in a straightforward fashion, imply confidence intervals for, and hypothesis tests about, the equality of the two betas.     

Sentiment lost: the effect of projecting the pricing kernel onto a smaller filtration set

Abstract

This paper provides a theoretical analysis on the impacts of using a suboptimal information set for the estimation of the pricing kernel and, more in general, for the validity of the fundamental theorems of asset pricing. While inferring the risk-neutral measure from options data provides a naturally forward-looking estimate, extracting the real world measure from historical returns is only partially informative, thus suboptimal with respect to investors’ future beliefs. As a consequence of this disalignment, the two measures no longer share the same nullset, thus distorting the investors’ risk premium and the validity of the pricing measure. From a probabilistic viewpoint, the missing beliefs are totally unaccessible stopping times on the coarser filtration set, so that an absolutely continuous strict local martingale, once projected on it, becomes continuous with jumps. Some empirical examples complete the paper.     

A new test on the conditional capital asset pricing model

Testing the validity of the conditional capital asset pricing model(CAPM) is a puzzle in the finance literatureLewellen and Nagel[14]find that the variation in betas and in the equity premium would have to be implausibly large to explain important asset-pricing anomaliesUnfortunately, they do not provide a rigorous test statisticBased on a simulation study, the method proposed in Lewellen and Nagel[14]tends to reject the null too frequently.We develop a new test procedure and derive its limiting distribution under the null hypothesis.Also, we provide a Bootstrap approach to the testing procedure to gain a good finite sample performanceBoth simulations and empirical studies show that our test is necessary for making correct inferences with the conditional CAPM.     

Multivariate asset‐pricing model based on subordinated stable processes

In this paper, we consider a multidimensional time‐changed stochastic process in the context of asset‐pricing modeling. The proposed model is constructed from stable processes, and its construction is based on two popular concepts: multivariate subordination and Lévy copulas. From a theoretical point of view, our main result is Theorem 1, which yields a simulation method from the considered class of processes. Our empirical study shows that the model represents the correlation between asset returns quite well. Moreover, we provide some evidence that this model is more appropriate for describing stock prices than classical time‐changed Brownian motion, at least if the cumulative amount of transactions is used for a stochastic time change.     

CAPM with fuzzy returns and hypothesis testing

Over the last four decades, several estimation issues of the beta have been discussed extensively in many articles. An emerging consensus is that the betas are time-dependent and their estimates are impacted by the return interval and the length of the estimation period. These findings lead to the prominence of the practical implementation of the Capital Asset Pricing Model. Our goal in this paper is two-fold. After studying the impact of the return interval on the beta estimates, we analyze the sample size effects on the preceding estimation. Working in the framework of fuzzy set theory, we first associate the returns based on closing prices with the intraperiod volatility for the representation by the means of a fuzzy random variable in order to incorporate the effect of the interval period over which the returns are measured in the analysis. Next, we use these fuzzy returns to estimate the beta via fuzzy least square method in order to deal efficiently with outliers in returns, often caused by structural breaks and regime switches in the asset prices. A bootstrap test is carried out to investigate whether there is a linear relationship between the market portfolio fuzzy return and the given asset fuzzy return. Finally, the empirical results on French stocks suggest that our beta estimates seem to be more stable than the ordinary least square (OLS) estimates when the return intervals and the sample size change.     

Esscher transforms and consumption-based models

The Esscher transform is an important tool in actuarial science. Since the pioneering work of Gerber and Shiu (1994), the use of the Esscher transform for option valuation has also been investigated extensively. However, the relationships between the asset pricing model based on the Esscher transform and some fundamental equilibrium-based asset pricing models, such as consumption-based models, have so far not been well-explored. In this paper, we attempt to bridge the gap between consumption-based models and asset pricing models based on Esscher-type transformations in a discrete-time setting. Based on certain assumptions for the distributions of asset returns, changes in aggregate consumptions and returns on the market portfolio, we construct pricing measures that are consistent with those arising from Esscher-type transformations. Explicit relationships between the market price of risk, and the risk preference parameters are derived for some particular cases.     

多元波动率模型的一些新进展

对多个资产收益率的协方差矩阵建立动态模型是一个非常重要的问题。本文就近些年来该方面研究的一些主要进展进行了综述,特别地介绍了几种基于数据降维技术发展起来的能够适用于高维情形的多元GARCH模型,另外,对于多元波动率的模型诊断与比较方法以及条件协方差矩阵的预测等方面的研究成果也作了分析。     

投资者情绪影响下资本资产定价的区制性研究——基于股票论坛发帖的情感分析

本文首先建立一个考虑投资者情绪的资本资产定价模型,研究发现,投资者情绪是资产定价的系统性因子且对其影响具有区制性(存在三个区制)。在此基础上通过仿真揭示投资者情绪对资产定价影响存在区制性的原因在于当投资者情绪增加时,最优组合超额收益受组合效应与情绪效应的综合影响。最后基于股票论坛发帖的情感分析构建投资者情绪指标,实证检验了本文的理论模型,并发现基于普通的线性回归模型得到的投资者情绪对股指超额收益影响,一方面会在投资者情绪处于第二区制内时将其对股指超额收益影响方向弄反,另一方面会在投资者情绪处于第三区制内时低估其增加导致的股指超额收益平均增加程度。     

Building an Optimal Portfolio in Discrete Time in the Presence of Transaction Costs

Abstract

Portfolio theory covers different approaches to the construction of a portfolio offering maximum expected returns for a given level of risk tolerance where the goal is to find the optimal investment rule. Each investor has a certain utility for money which is reflected by the choice of a utility function. In this article, a risk averse power utility function is studied in discrete time for a large class of underlying probability distribution of the returns of the asset prices. Each investor chooses, at the beginning of an investment period, the feasible portfolio allocation which maximizes the expected value of the utility function for terminal wealth. Effects of both large and small proportional transaction costs on the choice of an optimal portfolio are taken into account. The transaction regions are approximated by using asymptotic methods when the proportional transaction costs are small and by using expansions about critical points for large transaction costs.     

Spurious regression due to neglected of non-stationary volatility

This paper examines the effects of permanent changes in the variance of the errors on routine applications of standard t-ratio test in regression models. It is shown the asymptotic distribution of t-ratio test is not invariant to non-stationary in variance, and the phenomenon of spurious regression will occur independently of the structure assumed for these time series. The intuition behind this is that the non-stationary volatility can increase persistency in the level of regression errors, which then leads to spurious correlation. Monte Carlo experiment evidence indicates that, in contrast to the broken level/trend case, the presence of spurious relationship critically depends on the location and magnitude of changes, regardless of the sample size. Finally, some real data sets from the Shanghai stock database are reported for illustration.     

Risk factor extraction with quantile regression method

Firm characteristics based risk factors constitute a large part of the asset pricing literature. These characteristic based factors are constructed using the extreme quantiles of the sorted portfolios based on the firm characteristic in question. Yet to date, there is no consensus on a systematic approach to determine the optimal quantile used for extracting firm characteristic based risk factors. In addition, it is a stylised fact that asset prices exhibit heteroscedastic behavior, and counting on the extreme portfolios to extract the characteristic factors can produce unexpected result. In this study, we use quantile regressions to determine the optimal quantiles used in portfolios sorts to extract characteristic based risk factors used in asset pricing. Quantile regressions are well-suited to identify the quantiles needed to extract firm characteristic based factors, especially when the firm characteristic based factors and stock returns relationship is non-linear. More over, quantile regressions presents the quantile-by-quantile risk-return coefficients, thereby verifying the behavior of the extreme quantiles used in the factor construction. By examining the relationship between common characteristic based factors and stock returns in 23 developed countries, we observed that the optimal quantiles used to construct the common factors may differ between factors, but is similar across the North American, Asia-Pacific and Europe regions.

     

Wasserstein分布鲁棒期权定价

In this paper, the option pricing problem is formulated as a distributionally robust optimization problem, which seeks to minimize the worst case replication error for a given distributional uncertainty set(DUS) of the random underlying asset returns. The DUS is defined as a Wasserstein ball centred the empirical distribution of the underlying asset returns. It is proved that the proposed model can be reformulated as a computational tractable linear programming problem. Finally, the results of the empirical tests are presented to show the significance of the proposed approach.     

Equilibruim approach of asset pricing under Lévy process

This work considers the equilibrium approach of asset pricing for Lévy process. It derives the equity premium and pricing kernel analytically for the stock price process, obtains an equilibrium option pricing formula, and explains some empirical evidence such as the negative variance risk premium, implied volatility smirk, and negative skewness risk premium by comparing the physical and risk-neutral distributions of the log return. Different from most of the current studies in equilibrium pricing under jump diffusion models, this work models the underlying asset price as the exponential of a Lévy process and thus allows nearly an arbitrage distribution of the jump component.     

Accounting for regime and parameter uncertainty in regime-switching models

As investment guarantees become increasingly complex, realistic simulation of the price becomes more critical. Currently, regime-switching models are commonly used to simulate asset returns. Under a regime switching model, simulating random asset streams involves three steps: (i) estimate the model parameters given the number of regimes using maximum likelihood, (ii) choose the number of regimes using a model selection criteria, and (iii) simulate the streams using the optimal number of regimes and parameter values. This method, however, does not properly incorporate regime or parameter uncertainty into the generated asset streams and therefore into the price of the guarantee. To remedy this, this article adopts a Bayesian approach to properly account for those two sources of uncertainty and improve pricing.     

Accounting for regime and parameter uncertainty in regime-switching models

As investment guarantees become increasingly complex, realistic simulation of the price becomes more critical. Currently, regime-switching models are commonly used to simulate asset returns. Under a regime switching model, simulating random asset streams involves three steps: (i) estimate the model parameters given the number of regimes using maximum likelihood, (ii) choose the number of regimes using a model selection criteria, and (iii) simulate the streams using the optimal number of regimes and parameter values. This method, however, does not properly incorporate regime or parameter uncertainty into the generated asset streams and therefore into the price of the guarantee. To remedy this, this article adopts a Bayesian approach to properly account for those two sources of uncertainty and improve pricing.     

Convex ordering criteria for Lévy processes

Modelling financial and insurance time series with Lévy processes or with exponential Lévy processes is a relevant actual practice and an active area of research. It allows qualitatively and quantitatively good adaptation to the empirical statistical properties of asset returns. Due to model incompleteness it is a problem of considerable interest to determine the dependence of option prices in these models on the choice of pricing measures and to establish nontrivial price bounds. In this paper we review and extend ordering results of stochastic and convex type for this class of models. We also extend the ordering results to processes with independent increments (PII) and present several examples and applications as to α-stable processes, NIG-processes, GH-distributions, and others. Criteria are given for the Lévy measures which imply corresponding comparison results for European type options in (exponential) Lévy models.     

No Arbitrage and the Growth Optimal Portfolio

Abstract

Recently, several papers have expressed an interest in applying the Growth Optimal Portfolio (GOP) for pricing derivatives. We show that the existence of a GOP is equivalent to the existence of a strictly positive martingale density. Our approach circumvents two assumptions usually set forth in the literature: 1) infinite expected growth rates are permitted and 2) the market does not need to admit an equivalent martingale measure. In particular, our approach shows that models featuring credit constrained arbitrage may still allow a GOP to exist because this type of arbitrage can be removed by a change of numéraire. However, if the GOP exists the market admits an equivalent martingale measure under some numéraire and hence derivatives can be priced. The structure of martingale densities is used to provide a new characterization of the GOP which emphasizes the relation to other methods of pricing in incomplete markets. The case where GOP denominated asset prices are strict supermartingales is analyzed in the case of pure jump driven uncertainty.     

A fast numerical approach to option pricing with stochastic interest rate,stochastic volatility and double jumps

This study proposes a pricing model through allowing for stochastic interest rate and stochastic volatility in the double exponential jump-diffusion setting. The characteristic function of the proposed model is then derived. Fast numerical solutions for European call and put options pricing based on characteristic function and fast Fourier transform (FFT) technique are developed. Simulations show that our numerical technique is accurate, fast and easy to implement, the proposed model is suitable for modeling long-time real-market changes. The model and the proposed option pricing method are useful for empirical analysis of asset returns and risk management in firms.     

Asset pricing and portfolio selection based on the multivariate extended skew-Student-<Emphasis Type="Italic">t</Emphasis> distribution

The returns on most financial assets exhibit kurtosis and many also have probability distributions that possess skewness as well. In this paper a general multivariate model for the probability distribution of assets returns, which incorporates both kurtosis and skewness, is described. It is based on the multivariate extended skew-Student-t distribution. Salient features of the distribution are described and these are applied to the task of asset pricing. The paper shows that the market model is non-linear in general and that the sensitivity of asset returns to return on the market portfolio is not the same as the conventional beta, although this measure does arise in special cases. It is shown that the variance of asset returns is time varying and depends on the squared deviation of market portfolio return from its location parameter. The first order conditions for portfolio selection are described. Expected utility maximisers will select portfolios from an efficient surface, which is an analogue of the familiar mean-variance frontier, and which may be implemented using quadratic programming.     

固定执行价格下回望看涨期权保险精算定价

利用保险精算方法,将期权定价问题转化为纯保费确定问题,根据股票价格过程的实际概率测度推导出了无风险利率为常数时,固定执行价格下回望看涨期权定价公式,验证了当标的资产的期望收益率等于无风险利率时,保险精算定价和风险中性定价的一致性.最后通过实例分析了保险精算价格和风险中性价格的差异,并利用Matlab编程得到了保险精算价格与标的资产期望收益率之间的关系.