分整阶数半参数估计的有限样本性质研究

随着对经济和金融时间序列长记忆性的研究,分整阶数估计已成为当前理论研究的焦点问题。以对数周期图回归和局部Whittle方法为代表的半参数分整阶数估计方法在实践中得到广泛应用,但对这两类半参数估计方法的有限样本性质的比较则鲜有涉及,影响了在实践中对估计方法的选择。利用蒙特卡洛模拟方法,在不同数据产生的过程下,这两种半参数估计方法有限样本性质的研究结果表明：在ARFIMA(0, d, 0)过程下,LW类估计量具有较好的小样本性质;在平稳ARFIMA(1, d, 0)过程下,本文建议的QGPH估计量的有限样本性质要优于其他对数周期图估计量;在非平稳过程下,MGPH的偏差最小。     

分式布朗运动模型下的金融市场风险度量--以上证指数为例

金融市场风险度量在金融风险度量乃至于整个金融风险管理过程中都具有十分重要的地位。本文基于2013年至2014年上证指数的收盘数据,通过对分式布朗运动驱动的随机微分方程进行离散化处理,用其解（分式几何布朗运动）模拟上证指数的未来走势。在此基础上,采用Monte Carlo模拟法模拟出上证指数的价值分布与损益分布。最后,在95％的置信水平下计算出上证指数的在险价值（ VaR）。相对于传统股票价格预测模型---布朗运动模型,分式布朗运动模型更符合金融问题本身;利用Monte Carlo模拟法不再借助于股票价格历史数据。故而本模型对市场金融风险的预测精度更高。     

非均匀生物介质中太赫兹光波传播特性的研究

为进一步了解太赫兹光波与生物介质的相互作用机理,采用蒙特卡罗法(MCM)对太赫兹光波在非均匀生物介质中的传播进行模拟计算,得到了太赫兹光波在非均匀生物介质表面的漫反射光强分布曲线.数据显示了漫反射光强分布与生物介质中异物的大小(R)及位置(D)的关系.     

基于Copula的股票型开放式基金的组合风险度量

本文在＂推出股指期货和融资融券＂的新政策下,结合t-EGARCH模型和Copula方法对股票型开放式基金进行分析.该模型能更好地捕捉资产间的非线性相关性,更符合现实市场.并在此基础上,利用蒙特卡洛模拟计算了景顺增长基金前十大重仓股票及其投资组合的VaR值,从而验证了模型的有效性。     

供应链延迟战略价值定量分析——蒙特卡罗下的奇异期权估价

对延迟战略建立两阶段决策模型,分半成品有无残值两种情况,从实物期权的视角运用金融学中期权定价理论对延迟战略的期权价值进行分析。将生产商传统生产方式下的收益类比为购买标的证券的收益,采用延迟战略的收益类比为标的于该证券的期权收益,并假设产品价格随机游走。通过分析发现延迟战略的收益相当于奇异期权的回报,并且半成品没有残值是存在残值的特殊情况。进一步运用蒙特卡罗模拟方法定量地对延迟战略的期权价值进行参数分析和成本一收益分析。文章将动态的风险管理和对灵活性价值的度量引入决策过程,研究结论能给延迟战略投资决策提供借鉴。     

随机利率影响下信用风险的Monte—Carlo模拟

在前人利用马尔科夫链表示公司信用等级的基础上,将信用等级和随机利率引入离散时间的信用风险模型中,从而提出随机利率影响下的新的信用风险模型。就上述模型,对不同初始信用等级、初始盈余以及不同时刻的破产概率进行Monte—Carlo模拟,并讨论了相同条件下初始盈余与破产概率、初始信用等级与破产概率以及时间长短与破产概率之间的相互关系。     

A Bayesian Nonparametric Investigation of the Predictive Effect of Exchange Rates on Commodity Prices

This study proposes a full Bayesian nonparametric procedure to investigate the predictive power of exchange rates in relation to commodity prices for three commodity-exporting countries: Canada, Australia, and New Zealand. We propose a new time-dependent infinite mixture of a normal linear regression model of the conditional distribution of the commodity price index. The mixing weights follow a set of Probit stick-breaking priors that are time-varying. We find that exchange rates have a positive predictive effect in general, but accounting for time variation does not improve forecasting performance. By contrast, the intercept in the regression and the lagged dependent variable show signs of parameter change over time in most cases, which is important in forecasting both the mean and the density of commodity prices one period ahead. The results also suggest that the variance is a large source of the time variation in the conditional distribution of commodity prices.     

Endogeneity in multiple output production: Evidence from the US hotel industry

The treatment of endogenous inputs and outputs in the estimation of multiple–output distance functions is an important issue that has been largely ignored in the hotel and tourism literature. It is well known that directional distance functions with endogeneity are difficult to estimate using maximum likelihood-based methods since the specification of additional identifiable equations for each endogenous variable is highly challenging. In this paper, we propose a limited-information approach to models of this type using a flexible reduced form for the endogenous variables. This approach allows us to easily estimate technical efficiency and does not rely on information about input or output prices, which are typically unavailable. We employ Bayesian methods and propose and use novel posterior measures of weakness and relevance of instruments in an application involving data from major US hotels. We show that controlling for endogeneity has a substantial impact for relative and absolute hotel performance.     

Post-audits for managing cyber security investments: Bayesian post-audit using Markov Chain Monte Carlo (MCMC) simulation

With increasing security spending in organizations, evaluation of the quality and effectiveness of IT security investments has become an important component in managing these projects. The academic literature, however, is largely silent on post-audit of such investments, which is a formal evaluation of IT resource allocation decisions. IT post-audits are considered a useful risk management tool for organizations and are often emphasized in security certifications and standards. To fill this research gap and contribute to practice, we suggest post-auditing of IT security investments using the generic Markov Chain Monte Carlo (MCMC) simulation approach. This approach does not place stringent conjugate assumptions and can handle high-dimensional Bayesian post-audit inference problems often associated with information security resource allocation decisions. We develop two Bayesian post-audit models using the MCMC method: (1) measuring the effectiveness of an IT security investment using posterior mean score ratios (MSR), and posterior crossover error rates (CER); and (2) measuring the effectiveness through detection of a denial of service (DOS) attack using Bayesian estimation to statistically compare the degree of divergence using the concept of entropy. We demonstrate the utility of the proposed methodology using an email intrusion detection system application.     

Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model

The rough Bergomi (rBergomi) model, introduced recently in Bayer et al. [Pricing under rough volatility. Quant. Finance, 2016, 16(6), 887–904], is a promising rough volatility model in quantitative finance. It is a parsimonious model depending on only three parameters, and yet remarkably fits empirical implied volatility surfaces. In the absence of analytical European option pricing methods for the model, and due to the non-Markovian nature of the fractional driver, the prevalent option is to use the Monte Carlo (MC) simulation for pricing. Despite recent advances in the MC method in this context, pricing under the rBergomi model is still a time-consuming task. To overcome this issue, we have designed a novel, hierarchical approach, based on: (i) adaptive sparse grids quadrature (ASGQ), and (ii) quasi-Monte Carlo (QMC). Both techniques are coupled with a Brownian bridge construction and a Richardson extrapolation on the weak error. By uncovering the available regularity, our hierarchical methods demonstrate substantial computational gains with respect to the standard MC method. They reach a sufficiently small relative error tolerance in the price estimates across different parameter constellations, even for very small values of the Hurst parameter. Our work opens a new research direction in this field, i.e. to investigate the performance of methods other than Monte Carlo for pricing and calibrating under the rBergomi model.