Valuation and incentive effects of hurdle rate executive stock options

Traditional executive stock options are often criticized for inherently weak links between pay and performance. Hurdle rate executive stock options represent a viable improvement. However, valuing these options presents extraordinary analytic difficulties. With a constant dividend yield the strike price becomes a path-dependent function of the stock price and exact analytic valuation is intractable. To solve this problem, we apply the Monte Carlo valuation approach developed by Longstaff and Schwartz (Rev Financ Stud 4:113–147, 2001) to estimate the value of path-dependent American options. We also extend the methodology to incorporate the theoretical framework by Ingersoll (J Bus 79:453–487, 2006) to permit subjective valuation influenced by an executive’s risk aversion.

房地产开发企业违约概率压力测试研究——现金流蒙特卡洛模拟方法在银行中的应用

本文采用蒙特卡洛模拟方法,根据现金净额是否为负这一标准来判断房地产开发企业是否违约,在对企业的现金流进行随机模拟的基础上来计算企业的违约概率。压力测试的场景为房价下降,利率上升。压力传导途径为房价与利率变动导致企业销售收入变动,销售收入的改变导致企业的现金流量表发生变化。房价和利率对销售收入的冲击是随机的,企业的现金流也是随机的,本文通过随机模拟估算了企业的现金流为负的频率,以此作为企业违约的概率。压力测试表明,当房价下降幅度到达15%附近时,房地产开发商的违约概率开始急剧上升。     

基于新安江模型的降雨不确定性传播

基于模糊隶属度理论,结合蒙特卡罗方法,定量分析了降雨不确定性在新安江模型中的传播特性及其对流量过程的影响。用模糊隶属度函数表示降雨量的不确定性,用蒙特卡罗方法把3 h雨量随机解集为1 h数据,以表征降雨时程分布的不确定性影响。应用新安江模型对褒河流域进行径流模拟。结果表明,雨量量级大小的不确定性在降雨不确定性中占据主导位置,降雨时程分布的影响次之。     

An analytic hierarchy process for ranking operating costs of low cost and full service airlines

This paper develops an application of the analytic hierarchy process to rank the operating cost components of full service and low cost airlines. It takes into account the financial balance sheets and answers to a questionnaire submitted to the managers of selected airlines. The results suggest that the analytic hierarchy process can be appropriately used to obtain the ranking of the costs taking into account different views: financial, management and operative. Rental, office equipment and other supplies costs show the highest importance in the cost ranking, both for full services and low cost airlines. The robustness of the results is tested by Monte Carlo analysis.     

粒子滤波算法(PF)在疲劳驾驶检测系统中的应用研究

目前,实现定位跟踪的算法有很多,如卡尔曼滤波算法、扩展卡尔曼滤波算法、粒子滤波算法等。由于我们实际生活中的系统基本上都是非线性的,因此本文研究的是专门用于非线性非高斯系统跟踪的粒子滤波算法(PF)的基本原理及其具体应用。     

Bayesian semiparametric stochastic volatility modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. An empirical example compares the new model to standard parametric stochastic volatility models.     

Does garbage pricing increase the immoral disposal of household waste?

Some empirical studies have attempted to clarify the mechanism of illegal dumping by examining the degree to which per-bag pricing plays a role. However, previous research on the behaviour of avoiding paying a charge for waste collection has tended to neglect so-called ‘immoral disposal,’ which is less risky than illegal dumping because there is no legal penalty. In this study, we define immoral disposal as the dumping of waste in a manner that is immoral but not illegal. To detect the existence of immoral disposal, we apply a spatial econometric approach, namely an extended panel spatial Durbin model, to identify the actual spillover effect of garbage pricing in neighbouring municipalities on immoral disposal from the total waste. A major finding of this study is that immoral disposal exists in unit-based pricing, two-tiered pricing, and fixed pricing.     

Pseudolikelihood estimation of the stochastic frontier model

Stochastic frontier analysis is a popular tool to assess firm performance. Almost universally it has been applied using maximum likelihood (ML) estimation. An alternative approach, pseudolikelihood (PL) estimation, which decouples estimation of the error component structure and the production frontier, has been adopted in both the non-parametric and panel data settings. To date, no formal comparison has yet to be conducted comparing these methods in a standard, parametric cross-sectional framework. We produce a comparison of these two competing methods using Monte Carlo simulations. Our results indicate that PL estimation enjoys almost identical performance to ML estimation across a range of scenarios and performance metrics, and for certain metrics, outperforms ML estimation when the distribution of inefficiency is incorrectly specified.     

Regression analysis on serial dilution data from virus validation robustness studies

To ensure the safety of plasma-derived medicinal products, the Dutch Blood Supply Foundation (Sanquin) performs virus validation experiments. Data from these experiments are based on serial dilution assays. Regression analysis on assay data faces several problems: only a small number of data points are available, data contain censoring and are subject to sampling error. Furthermore, the process variability inherent to the experiments is not evident. In this paper we address these problems by introducing a regression model for serial dilution data and by analyzing how validation experiments and simulation techniques can help elucidate various sources of variability the experiments are subject to. These are then incorporated into the regression model.