Conditional expectation determination based on the J-process using Malliavin calculus applied to pricing American options

The aim of our paper is to elaborate a theoretical methodology based on the Malliavin calculus to calculate the following conditional expectation (P_t(X_t)|(X_s)) for s≤t where the only state variable follows a J-process [Jerbi Y. A new closed-form solution as an extension of the Black—Scholes formula allowing smile curve plotting. Quant Finance. 2013; Online First Article. doi:10.1080/14697688.2012.762458]. The theoretical results are applied to the American option pricing, consisting of an extension of the work of Bally et al. [Pricing and hedging American options by Monte Carlo methods using a Malliavin calculus approach. Monte Carlo Methods Appl. 2005;11-2:97–133], as well as the J-process (with additional parameters λ and θ) is an extension of the Wiener process. The introduction of the aforesaid parameters induces skewness and kurtosis effects, i.e. smile curve allowing to fit with the reality of financial market. In his work Jerbi [Jerbi Y. A new closed-form solution as an extension of the Black–-Scholes formula allowing smile curve plotting. Quant Finance. 2013; Online First Article. doi:10.1080/14697688.2012.762458] showed that the use of the J-process is equivalent to the use of a stochastic volatility model based on the Wiener process as in Heston's. The present work consists on extending this result to the American options. We studied the influence of the parameters λ and θ on the American option price and we find empirical results fitting with the options theory.     

Analysing truncated data with semiparametric transformation models

Left-truncation often arises when patient information, such as time of diagnosis, is gathered retrospectively. In some cases, the distribution function, say G(x), of left-truncated variables can be parameterized as G(x; θ), where θ∈Θ?R^q and θ is a q-dimensional vector. Under semiparametric transformation models, we demonstrated that the approach of Chen et al. (Semiparametric analysis of transformation models with censored data. Biometrika. 2002;89:659–668) can be used to analyse this type of data. The asymptotic properties of the proposed estimators are derived. A simulation study is conducted to investigate the performance of the proposed estimators.     

均值—条件风险价值模型有效前沿分析——以含无风险资产和持有期为研究视角

从有效投资组合的角度构建持有期下含有无风险资产的均值—条件风险价值模型,用Lagrange乘子法对该模型求解,可得到:一定条件下,新模型的有效前沿与均值—方差模型有效前沿是一致的;且当借贷利率不同时,新模型的有效前沿可以根据组合预期收益率与借贷利率的不同关系,由线段、双曲线以及射线三个部分组合而成。     

股指期货上市对现货市场的影响——来自中国的实证研究

文章运用EGARCH、多元GARCH、GRANGER非因果性检验、CHOW断点检验等计量经济方法,检验了股指期货上市对股票市场波动的效应、对现货中外联合动态关系的影响、期现之间的先行-滞后关系以及股指期货交易行为对现货波动的影响。研究表明,股指期货的引入加大了现货市场的波动,外生的波动事件通过期货交易,加大了现货市场的波动,引入股指期货后,中外股市的联合动态关系有显著改变。     

VaR与CVaR的敏感性凸性及其核估计

风险价值（VaR）和条件风险价值（CVaR）是目前两大主流风险度量工具,如何准确地对它们进行估计是风险管理实践中首要而核心的问题。近年来非参数核估计方法因模型设定灵活、方便处理变量相依结构等优点备受关注。在本文,我们在核估计的框架内讨论VaR和CVaR估计量的性质;给出投资组合VaR和CVaR对组合头寸的一阶导数向量和二阶导数矩阵的核估计公式,并用它们来讨论组合VaR和CVaR对组合头寸的敏感性和凸性。最后,我们利用中国外汇市场的实际数据做实证分析。     

集值决策信息系统属性约简的启发式算法

在集值决策信息系统中,通过引入信息量和条件信息量,对属性的重要性进行了定义.给出了分配约简（广义决策约简）的一种启发式算法：条件信息量约简算法,分析了该算法的时间复杂度,时间复杂度为O（｜A｜3｜U｜2）;最后通过例子说明,该算法能得到集值决策信息系统的一个约简.     

论原型条件分句和隐喻式条件分句

从认知的角度来看,条件分句可以分为原型奈件分句和隐喻式条件分句.在原型条件分句中,语法符号表达其原来的语法意义,二者是形式和意义的统一体;在隐喻式条件分句中,语法符号脱离了其原来的语法意义,而语法隐喻使语法符号通过隐喻模式与其语法意义得到了统一.     

基于GARCHSK的铜期货VaR估计方法研究

基于GARCHSK模型对期货收益序列的条件偏度和峰度进行动态建模,提出了“有偏”和“尖峰厚尾”分布下的VaR估计方法。通过对沪铜期货的实证研究表明,其收益分布存在明显的“有偏”和“尖峰厚尾”。基于不同分布假定下的VaR估计结果的Kupiec检验表明,基于GARCHSK的VaR估计方法能够有效提高VaR的估计精度。     

问题图式：培养学生问题解决能力的基石

问题图式具有知识的概括性、认知资源的经济性、未知变量的可推理性和广泛的迁移性等特点,是学生用以组织知识和应用知识的有效方式。而在知识学习和问题解决过程中能否归纳出问题图式和对图式进行改进是提高学生问题解决能力的关键。为形成有效的问题图式,在教学中应注重对知识进行复述、精加工和组织,以形成条件化知识;要对问题进行深层结构表征,并加强元认知能力的训练和培养及样例的学习,以加强学生问题解决过程中的图式建立、运用、综合能力,提高学生解决问题的效率。     

Polynomial Spline Estimation for a Generalized Additive Coefficient Model

Abstract.  We study a semiparametric generalized additive coefficient model (GACM), in which linear predictors in the conventional generalized linear models are generalized to unknown functions depending on certain covariates, and approximate the non-parametric functions by using polynomial spline. The asymptotic expansion with optimal rates of convergence for the estimators of the non-parametric part is established. Semiparametric generalized likelihood ratio test is also proposed to check if a non-parametric coefficient can be simplified as a parametric one. A conditional bootstrap version is suggested to approximate the distribution of the test under the null hypothesis. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed methods. We further apply the proposed model and methods to a data set from a human visceral Leishmaniasis study conducted in Brazil from 1994 to 1997. Numerical results outperform the traditional generalized linear model and the proposed GACM is preferable.