PRICING ASIAN OPTIONS FOR JUMP DIFFUSION

We construct a sequence of functions that uniformly converge (on compact sets) to the price of an Asian option, which is written on a stock whose dynamics follow a jump diffusion. The convergence is exponentially fast. We show that each element in this sequence is the unique classical solution of a parabolic partial differential equation (not an integro‐differential equation). As a result we obtain a fast numerical approximation scheme whose accuracy versus speed characteristics can be controlled. We analyze the performance of our numerical algorithm on several examples.     

基于期权定价理论的风险投资决策

项目评价的传统方法———净现值(NPV)法在应用于风险投资项目时,由于低估了投资价值,往往会使得投资者失去一些有价值的投资机会。结合风险投资的特性,将期权定价理论应用于风险投资决策中,并建立连续及离散两种状态下的决策模型     

房地产调控政策失效的原因探究

"国六条"出台已经一年,我国房地产价格不跌反涨,中央政府稳定房价的调控目标并未实现。从我国房地产立法滞后、存在调控时滞、房地产宏观调控中地方政府与中央政府及开发商之间的博弈、房地产市场供求矛盾突出等四个方面分析了调控房价政策失效的深层次原因。     

试论营销策略中的期权因素

营销策略由产品、价格、地点和促销构成,产品由核心产品、有形产品和附加产品组成。附加产 品在产品构成中的作用越来越重要,附加产品的性质类似于期权。通过附加产品,企业和消费者之间存 在着契约关系,可以利用期权的分析方法对附加产品进行分析,并利用期权定价模型对其进行定价。     

一种基于VxWorks的高实时性软件架构设计

重点描述基于VxWorks操作系统的高实时性嵌入式应用的架构设计,以及用于TDMA领域的移动通信机软件的开发.使用这种设计手段可实现在多任务条件下嵌入式软件的高实时性与稳定性等性能,对实时性要求高的软件设计师会有一定的参考价值.     

近代上海房地产市场波动的金融分析(1929～1935)

1929~1935年,上海房地产市场在短短的五六年间经历了暴涨和暴跌。文章通过对房地产市场的金融分析,发现导致上海房地产市场在短期内大起大落的主要原因是币值和物价的变化。由于近代币值和物价的变化,结果导致货币市场上流通量发生剧烈波动,并影响了房地产市场的投机需求,导致房地产市场大起大落。这也提醒人们在分析房地产市场价格时要注意币值及物价对供求关系的影响,注意甄别各种不同的需求及其比重。     

人民币实际汇率变动对我国进出口贸易影响:1997-2006

本文选取1997-2006年的月度数据,运用VAR方法,考虑了FDI存量和我国加工贸易特征的影响后,对贸易收支与人民币实际汇率变动关系进行了动态分析,并得出如下结论:人民币实际有效汇率的变动显著影响了我国贸易收支;短期内J曲线效应存在,时滞约为5个月;汇率变动对进出口贸易影响的同向性明显;我国贸易收支的收入效应大于价格效应等.     

国外房地产宏观调控政策借鉴与启示

世界各国在经济发展过程中几乎都面临过或正面临着“房价过高”、“市场失灵”的难题。本文对美国、英国、俄罗斯等10多个国家的房地产市场调控政策进行了总结,指出了我国房地产市场宏观调控面临的困难与挑战,并结合国外房地产宏观调控的成功经验,就我国房地产市场宏观调控政策提出了自己的观点。     

Real effects of financial reporting and disclosure on innovation

This paper reviews the literature on the real effects of financial reporting and disclosure on corporate innovation, highlighting both the possible channels of influence and the potential challenges that researchers face when attributing causal effects. We discuss the concept of innovation, emphasising the specific characteristics that make investments in innovation difficult to report. We then provide a review of the nascent work relating disclosure to innovation, which we organise around three channels: financing, compensation and learning. Finally, we discuss recent efforts aimed at increasing the quality of corporate disclosures, including disclosures of firms’ innovative activities. Throughout the paper, we highlight the trade-offs of disclosure (reduced information asymmetry and increased proprietary costs), which are particularly exacerbated in the context of corporate innovation.     

Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models

In this paper we propose two efficient techniques which allow one to compute the price of American basket options. In particular, we consider a basket of assets that follow a multi-dimensional Black–Scholes dynamics. The proposed techniques, called GPR Tree (GRP-Tree) and GPR Exact Integration (GPR-EI), are both based on Machine Learning, exploited together with binomial trees or with a closed form formula for integration. Moreover, these two methods solve the backward dynamic programing problem considering a Bermudan approximation of the American option. On the exercise dates, the value of the option is first computed as the maximum between the exercise value and the continuation value and then approximated by means of Gaussian Process Regression. The two methods mainly differ in the approach used to compute the continuation value: a single step of the binomial tree or integration according to the probability density of the process. Numerical results show that these two methods are accurate and reliable in handling American options on very large baskets of assets. Moreover we also consider the rough Bergomi model, which provides stochastic volatility with memory. Despite that this model is only bidimensional, the whole history of the process impacts on the price, and how to handle all this information is not obvious at all. To this aim, we present how to adapt the GPR-Tree and GPR-EI methods and we focus on pricing American options in this non-Markovian framework.