"精确到"与"精确度"

新教材引入了二分法求函数的近似值,这就涉及到近似计算中的两个概念:"精确到"与"精确度".教材上用的是精确度,一般如精确度为0.1,是指函数f(x)在区间[a,b]上,有f(a)·f(b)<0,则在(a,b)上存在零点,如∣a-b∣<0.1,在区间上任取一点,包括两个端点,其精确度即为0.1.而"精确到",以精确到0.1为例,即我们平常所说的精确到小数点后一位,是指与精确值的误差不超过±0.05.     

习题"磨"炼结构"谋"法

借用一句"成事在天、谋事在人"的谚语.数学解题乃是习题磨炼、结构谋法.数学结构,是指数学中的概念、公式、图形、程序以及一切数学法则、定律、定理的内在本质的形式化,在数学教学中,引导学生关注式的结构、图形的结构和程序结构的层次性、相似性、独立性、关联性,可以深刻体会数学思想,感悟数学本质,明确思维方向,从而优化解题策略,缩短思考时间,提高解题能力.     

"数老哥"与"形老弟"

在数学王国里,住蔷一对亲兄弟"数老哥"与"形老弟",他们本来形影不离,亲密无间,可就在前些时候,教学王国的一些居民风言风语地说他们不是亲兄弟,说他们之间根本就设有什么关系,数就是数,形就是形.听了这些流言蜚语,"数老哥"与"形老弟"受不了,相互之闻竟然猜疑起来,好长时间都不来往.     

折叠纸片"搭台" 解析几何"唱戏"

近几年的高考数学试题,设置了一些数学学科内的综合题,它们的新颖性、综合性值得我们重视.在知识网络交汇处设计试题是高考命题改革的一个方向.以"折叠纸片"为依托的解析几何题正是在这种背景下"闪亮登场".这类题目将探究解析几何有关问题融于操作实验中,形态鲜活,旨在倡导探索、创造、发现,体现了新课程改革的新理念--研究性学习,具有素质教育的良好导向功能.……     

"问题链·导学"PK"讲授式"

最近在苏中一所四星高中听了两节高一数学新授课,两个教师围绕<向量的加法>展开了两种不同教学模式的同课异构,听后感触颇多,于是写下一些心得体会与大家分享.让我们先来看看两位教师教学设计的基本流程.     

浅谈高考数学复习中的"背"与"避"

本人在教学过程中,该背什么,回避什么,有一点体会,供同仁参考. 　　1 背函数问题中几个常用超越式的相关结论,避只用初等数学方法求解.……     

意外的"落水"成功的"过河"——"过河模式"下的一堂"直线与圆锥曲线的位置关系"课

"过河式"模型(图1)是在一线教学实践中为有效设计和达成三维目标而提出的一个简单、直观、形象的教学学习活动模式,比"三角式"模式(图2)具有更多的优点,能让教师结合课堂教学的具体内容,来有效分析、设计和达成三维目标,更有效地促进一线教师在新课程理念下对课堂教学的反思、实践和研究,实现数学课堂教学的"由惑到悟".     

不等式"搭台"导数"唱戏"

用导数解决函数的单调性问题一直是全国各地市高考及高考模拟试题的重点,利用导数证明不等式便是近年高考最热衷的题型之一,此类问题的特点为:问题以不等式形式呈现,而"主角"往往却是导数,因此构造函数成为证明不等式的良好"载体".构造函数的依据是不等式关系中隐含的易于判断的函数关系在通过转化变换之后与某些函数结构特征吻合.……     

"上课"教师不要表现得太"聪明"

经常参加听课,往往有种感觉:授课教师为了表现自己高超的教学艺术,常对教学过程及每一问题都一一作了精心设计安排,让学生都掉进了教师精心设计的"圈套".无疑,在这样的课堂中,学生成了学习的旁观者,课堂民主成了假民主,主体参与成了虚假的被动配合,独立思考成了"牵引着赶路"在2007年4月宁波数学高级研修班上,有幸也参加了此会议,东北师范大学史宁中校长的报告中有一句话:老师,上课时不要表现得太聪明,才能让学生显得更聪明.让我感慨不已:在新课程积极创导学生主体参与、培养创新能力的课堂中,老师是否可以抛弃一些虚假的"聪明",放下一点架子,在上课中表现得"愚钝"一些,参与到课堂学生的学习讨论之中呢?     

铸题成"模"以"模"解题

笛卡儿说:"我所解决的每一个问题都将成为一个范例,以用于解决其它问题."其实在中学数学中有许多含有较多信息量的基本图形、分式及解题思想方法,在解决问题时经反复运用,使得它们之间的联结得以加强,从而形成一个个知识模块.这些知识模块再经过反复运用,从显意识不同程度地转入潜意识贮存在记忆系统中,当遇到相似条件或图形时,便能迅速联想起与之相应的知识模块,从而敏锐地进行识别、分析,形成对问题的综     

基于实际波动率的组合选择实证研究

本文对证券组合三因素的7种预测方法进行了实证研究和敏感性检验,得出结论:若以周作为组合持有期,则不论何种收益预测方法,基于实际波率的ARFIMA方法在组合持有期上均取得了正的超额收益;基于实际波动率的ARFIMA法在组合选择的各种方法中是最优的.     

Medium‐term horizon volatility forecasting: A comparative study

In this paper, volatility is estimated and then forecast using unobserved components‐realized volatility (UC‐RV) models as well as constant volatility and GARCH models. With the objective of forecasting medium‐term horizon volatility, various prediction methods are employed: multi‐period prediction, variable sampling intervals and scaling. The optimality of these methods is compared in terms of their forecasting performance. To this end, several UC‐RV models are presented and then calibrated using the Kalman filter. Validation is based on the standard errors on the parameter estimates and a comparison with other models employed in the literature such as constant volatility and GARCH models. Although we have volatility forecasting for the computation of Value‐at‐Risk in mind the methodology presented has wider applications. This investigation into practical volatility forecasting complements the substantial body of work on realized volatility‐based modelling in business. Copyright © 2007 John Wiley & Sons, Ltd.     

Bayesian modeling and forecasting of Value‐at‐Risk via threshold realized volatility

This study proposes a threshold realized generalized autoregressive conditional heteroscedastic (GARCH) model that jointly models daily returns and realized volatility, thereby taking into account the bias and asymmetry of realized volatility. We incorporate this threshold realized GARCH model with skew Student‐t innovations as the observation equation, view this model as a sharp transition model, and treat the realized volatility as a proxy for volatility under this nonlinear structure. Through the Bayesian Markov chain Monte Carlo method, the model can jointly estimate the parameters in the return equation, the volatility equation, and the measurement equation. As an illustration, we conduct a simulation study and apply the proposed method to the US and Japan stock markets. Based on quantile forecasting and volatility estimation, we find that the threshold heteroskedastic framework with realized volatility successfully models the asymmetric dynamic structure. We also investigate the predictive ability of volatility by comparing the proposed model with the traditional GARCH model as well as some popular asymmetric GARCH and realized GARCH models. This threshold realized GARCH model with skew Student‐t innovations outperforms the competing risk models in out‐of‐sample volatility and Value‐at‐Risk forecasting.     

Large deviations of realized volatility

This paper studies large and moderate deviation properties of a realized volatility statistic of high frequency financial data. We establish a large deviation principle for the realized volatility when the number of high frequency observations in a fixed time interval increases to infinity. Our large deviation result can be used to evaluate tail probabilities of the realized volatility. We also derive a moderate deviation rate function for a standardized realized volatility statistic. The moderate deviation result is useful for assessing the validity of normal approximations based on the central limit theorem. In particular, it clarifies that there exists a trade-off between the accuracy of the normal approximations and the path regularity of an underlying volatility process. Our large and moderate deviation results complement the existing asymptotic theory on high frequency data. In addition, the paper contributes to the literature of large deviation theory in that the theory is extended to a high frequency data environment.     

On theoretical pricing of options with fuzzy estimators

In this paper we present an application of a new method of constructing fuzzy estimators for the parameters of a given probability distribution function, using statistical data. This application belongs to the financial field and especially to the section of financial engineering. In financial markets there are great fluctuations, thus the element of vagueness and uncertainty is frequent. This application concerns Theoretical Pricing of Options and in particular the Black and Scholes Options Pricing formula. We make use of fuzzy estimators for the volatility of stock returns and we consider the stock price as a symmetric triangular fuzzy number. Furthermore we apply the Black and Scholes formula by using adaptive fuzzy numbers introduced by Thiagarajah et al. [K. Thiagarajah, S.S. Appadoo, A. Thavaneswaran, Option valuation model with adaptive fuzzy numbers, Computers and Mathematics with Applications 53 (2007) 831–841] for the stock price and the volatility and we replace the fuzzy volatility and the fuzzy stock price by possibilistic mean value. We refer to both cases of call and put option prices according to the Black & Scholes model and also analyze the results to Greek parameters. Finally, a numerical example is presented for both methods and a comparison is realized based on the results.     

Time endogeneity and an optimal weight function in pre-averaging covariance estimation

We establish a central limit theorem for a class of pre-averaging covariance estimators in a general endogenous time setting. In particular, we show that the time endogeneity has no impact on the asymptotic distribution if certain functionals of observation times are asymptotically well-defined. This contrasts with the case of the realized volatility in a pure diffusion setting. We also discuss an optimal choice of the weight function in the pre-averaging.     

Realized volatility with stochastic sampling

A central limit theorem for the realized volatility of a one-dimensional continuous semimartingale based on a general stochastic sampling scheme is proved. The asymptotic distribution depends on the sampling scheme, which is written explicitly in terms of the asymptotic skewness and kurtosis of returns. Conditions for the central limit theorem to hold are examined for several concrete examples of schemes. Lower bounds for mean squared error and for asymptotic conditional variance are given, which are attained by using a specific sampling scheme.     

Option pricing and hedging in incomplete market driven by Normal Tempered Stable process with stochastic volatility

This paper develops a distribution class, termed Normal Tempered Stable, by subordinating a drifted Brownian motion through a strictly increasing Tempered Stable process that generalizes the Variance Gamma and the Normal Inverse Gaussian and is used to model the logarithm asset returns. The newly added parameter is to create subclasses for all the distributions discovered in financial market. The empirical test suggests that time series of Technology stock returns in US market reject both the Variance Gamma distribution and the Normal Inverse Gaussian distribution and admit instead another subclass of the Normal Tempered Stable distribution. Furthermore, we introduce stochastic volatilities into the Normal Tempered Stable process and derive explicit formulae for option pricing and hedging by means of the characteristic function based methods. To answer the question of how well different models work in practice, we investigate four models adopting data on daily equity option prices and obtain several findings from the numerical results. To sum up, the Normal Tempered Stable process with stochastic volatility is able to adequately capture implied volatility dynamics and seen as a superior model relative to the jump-diffusion stochastic volatility model, based on the construction methodology that incorporates more sophisticated and flexible jump structure and the systematic and realistic treatment of volatility dynamics. The Normal Tempered Stable model turns out to have the competitive performance in an efficient manner given that it only requires three parameters.     

A mathematical analysis of the Gumbel test for jumps in stochastic volatility models

This article gives an exhaustive mathematical analysis of the Gumbel test for additive jump components based on extreme value theory. The Gumbel test was first introduced by Lee and Mykland in 2008 from an economical point of view. They consider a continuous-time stochastic volatility model with a general continuous volatility process and observe it under a high-frequency sampling scheme. The test statistics based on the maximum of increments converges to the Gumbel distribution under the null hypothesis of no additive jump component and to infinity otherwise. Our article presents a moment method based technique that provides some deeper mathematical insights into the convergence and divergence case of the test statistics. In the non-jump case we are able to prove the convergence to the Gumbel distribution under greatly weak assumptions: The volatility process has to be merely pathwise Hölder continuous with an arbitrary random Hölder exponent and we have no restrictions concerning an additional drift term. Therefore, for example, we are allowing for long and short-range dependence. In the case of existing additive jumps, we give divergence results in a general semimartingale setting and investigate the speed of divergence depending on the jump activity. As a by-product of our analysis we also deduce an optimal pathwise estimator for the spot volatility process. Moreover, we provide a detailed simulation study that compares the power of the Gumbel test with the power of the jump test proposed by Barndorff–Nielsen and Shephard in 2006 for Hölder exponents close to zero. Finally, both tests are applied to a real dataset.     

Transaction activity and bitcoin realized volatility

We study the predictive value of transaction activity in the bitcoin network for the realized volatility of bitcoin returns constructed by high-frequency data. As an alternative modeling approach to the popular linear heterogeneous autoregressive model, we provide out-of-sample forecasts for realized volatility of bitcoin returns employing machine learning algorithms, and in particular by Random Forests. Our findings reveal that on-blockchain transaction activity does improve the out-of-sample forecast accuracy at all the forecast horizons considered.