备兑权证定价方法的务实改进与实现

为使备兑权证的定价更加贴近现实,文章以t分布代替原假设标的股票收益率服从的正态分布,以随机波动率代替历史波动率,通过GARCH模型来消除金融时间序列的异方差性,并考虑交易费用、红利等因素对权证价格的影响,在此优化了传统的定价模型。然后用Monte Carlo模拟的定价思路,并利用对偶方差减少技术提高其效率,最后编辑程序在Eviews中成功运行得出权证的定价.     

具有自回归误差的变系数模型的变量选择

基于局部线性估计方法研究误差具有自回归结构的变系数回归模型,讨论利用估计的残差和LASSO方法对误差部分进行定阶和估计的统计推断问题.通过蒙特卡洛模拟方法对比研究,揭示具有良好性质的局部多项式估计方法的有效性.     

蒙特卡罗方法在核类本科专业的教学初探

蒙特卡罗方法是以概率统计理论为基础、通过计算机编程而实现的一种数值计算方法,在核科学与技术等领域有着广泛的应用。新时期下,初步尝试面向核类本科专业开设蒙特卡罗方法这门课程,将概率统计思想和编程实践训练融入到蒙特卡罗方法教学中,教学取得了一定成效。     

Relating alternative forms of contingency fit to the appropriate methods to test them

At the core of contingency theory, a major theory in management accounting, is the concept of fit. We critically discuss forms of fit as presented in overview articles from the management accounting field, highlighting forms of fit that have not appeared in prior overview articles (matching fit with hetero-performance on the fit line and/or asymmetric effects of mis-fit on performance). We also address some confusing arguments in the literature concerning the moderation form of fit and what has been referred as the mediation form of fit. In a second step, we reevaluate the appropriateness of statistical techniques used to test sub-forms of fit, highlighting the difficulties in differentiating conclusively between them. Specifically, we present polynomial regression analysis (PRA) in conjunction with the response surface methodology (RSM) as a powerful methodological alternative and discuss its ability to differentiate between the sub-forms of fit. We also discuss the strengths and weaknesses of structural equation modeling (SEM) to test for forms of fit.     

Structural Changes in Volatility of Foreign Exchange Rates after theAsian Financial Crisis

Regime-shift models of daily returns are estimated for the foreign exchange rates of the Asian currencies that suffered from drastic devaluation during the Asian financial crisis in 1997, and the change points are detectedfor their volatility structures. Furthermore, how the persistence in the volatility of their exchange rates changed after the crisis is examined.     

USING TIME-SERIES TO MEASURE UNCERTAINTY IN ENVIRONMENTAL INPUT–OUTPUT ANALYSIS

Environmental Input-Output Analysis (EIOA) is a tool for environmental analysis of broad classes of sectoral activities, taking into account indirect effects in other sectors in the supply chain. The core of EIOA is an input–output table (IOT) and national accounting matrix including environmental accounts (NAMEA) for a fixed base-year. We evaluate the uncertainty in EIOA using a time series of current-price IOT and NAMEA for 13 years from 1990 to 2002. We find annual variations in the current-price IOT and NAMEA, which may represent either realistic changes in production or measurement error. We assume the changes are errors and apply a regression analysis to remove the trends from the underlying data and estimate the uncertainty in the raw IOT. We then calculate the emissions for various final users and sectors to estimate the uncertainties from typical EIOA investigations. Using Monte Carlo analysis, we then investigate how well the variations in the current-price IOT and NAMEA over time may represent uncertainties. The results of this work have several implications for both statistical offices and the analyst. Statistical offices can provide details on data sources, methodologies, and estimates of annual variations. Analysts can incorporate this uncertainty information to understand the implications of uncertainty on their calculations and ultimately the policy recommendations derived from their studies.     

Primal–dual quasi-Monte Carlo simulation with dimension reduction for pricing American options

The pricing of American options is one of the most challenging problems in financial engineering due to the involved optimal stopping time problem, which can be solved by using dynamic programming (DP). But applying DP is not always practical, especially when the state space is high dimensional. However, the curse of dimensionality can be overcome by Monte Carlo (MC) simulation. We can get lower and upper bounds by MC to ensure that the true price falls into a valid confidence interval. During the recent decades, progress has been made in using MC simulation to obtain both the lower bound by least-squares Monte Carlo method (LSM) and the upper bound by duality approach. However, there are few works on pricing American options using quasi-Monte Carlo (QMC) methods, especially to compute the upper bound. For comparing the sample variances and standard errors in the numerical experiments, randomized QMC (RQMC) methods are usually used. In this paper, we propose to use RQMC to replace MC simulation to compute both the lower bound (by the LSM) and the upper bound (by the duality approach). Moreover, we propose to use dimension reduction techniques, such as the Brownian bridge, principal component analysis, linear transformation and the gradients based principle component analysis. We perform numerical experiments on American–Asian options and American max-call options under the Black–Scholes model and the variance gamma model, in which the options have the path-dependent feature or are written on multiple underlying assets. We find that RQMC in combination with dimension reduction techniques can significantly increase the efficiency in computing both the lower and upper bounds, resulting in better estimates and tighter confidence intervals of the true price than pure MC simulation.     

Randomized quasi-Monte Carlo methods in pricing securities

Today quasi-Monte Carlo methods are used successfully in computational finance and economics as an alternative to the Monte Carlo method. One drawback of these methods, however, is the lack of a practical way of error estimation. To address this issue several researchers introduced the so-called randomized quasi-Monte Carlo methods in the last decade. In this paper we will present a survey of randomized quasi-Monte Carlo methods, and compare their efficiencies with the efficiency of the Monte Carlo method in pricing certain securities. We will also investigate the effects of Box–Muller and inverse transformation techniques when they are applied to low-discrepancy sequences.     

同期相关面板数据退势单位根检验的小样本性质

本文基于SUR回归将时间序列的两种单位根检验（ADF—GLS检验）推广到面板数据，得到了同期相关面板数据退势单位根检验，称为SUR—ADF—GLS检验。通过蒙特卡洛试验研究发现，SUR—ADF—GLS检验具有良好的小样本性质。并且，SUR—ADF—GLS检验关于面板数据的同期相关性结构存在着较强的“依存性”。