国际贸易大宗商品定价案例研究

当前,我国已成为世界第一大出口国和第二大进口国,为全球经济摆脱危机作出了巨大贡献。但由于大宗商品定价权缺失,巨大的购买力未能改变我国企业被动接受不利价格的尴尬地位。本文选取美国在农产品、石油定价中的成功案例以及我国在稀土、铁矿石定价中的未成功案例,从正反两方面分析了国际大宗商品定价的理论机理、实施路径和重要影响,从而为我国争夺大宗商品定价权、提升国际贸易话语权提供决策依据。     

中国大宗商品期货市场定价机制研究

大宗商品期货市场是我国资本市场的重要组成部分,其定价有效性关系到投资者套期保值和价格发现等功能的实现。本文对国际前沿研究中常用的定价因子进行全面系统梳理,并对这些因子对我国商品期货合约收益率的解释和预测能力进行检验。在此基础上,本文构建了适用于我国大宗商品期货市场的包含市场、基差以及基差动量的三因子定价模型。进一步研究表明,基于大宗商品存储理论和现货存货数据构建的投资组合收益率可以被本文三因子模型有效解释,验证了经典的存储理论在我国的适用性。此外,本文对基差与基差动量两个重要因子的经济学意义进行了阐释。本文研究为进一步厘清大宗商品期货市场定价机制提供了一定参考。     

大宗商品市场与股市、债市的互动关系初探

股票、债券与大宗商品市场之间存在着一定的互动次序：债券领先于股票,股票领先于商品,这样的顺序得益于金融市场本身的经济晴雨表功能。本文通过协整、向量误差修正模型和格兰杰因果等时间序列方法,找出其中的互动脉络,探讨如何运用这种关系进行资产配置。     

国际大宗商品价格波动对我国物价水平的风险传导

目前我国正处于经济高速发展时期,正在进行经济转型,我国一些主要的基础原材料和大宗商品无法满足经济发展的需求,因此我国对国际大宗商品的需求不断加大。国际大宗商品价格的波动相对剧烈,这会对我国经济的稳定带来不利影响。本文选取大宗商品价格指数BPI和居民消费价格指数CPI,通过协整检验和格兰杰检验研究了国际大宗商品价格对我国物价水平的影响。研究发现国际大宗商品价格是一般物价水平的格兰杰原因,且二者之间存在长期的协整关系。本文最后以实证结果为基础,提出政策建议。     

多标的资产期权定价的Monte Carl模拟方法及其应用研究

鉴于一般的偏微分解析方法和传统数值方法处理高维期权定价问题存在很大困难,本文在单标的资产价格随机模型的基础上,推导了具相关性的多标的资产价格的随机过程公式,以此构造蒙特卡罗模拟高维欧式期权定价的随机模型,给出模拟算法,并分析了影响蒙特卡罗模拟效果的几个关键因素,模拟算例的结果显示模拟效果较好.     

大宗商品市场套息收益分析

近来,大宗商品作为一种投资性资产引起了人们的广泛关注。本文通过从Bloomberg搜集1980年1月1日至2015年12月31日的日度数据,研究了国际大宗商品市场与国内大宗商品市场的套息收益,并对两者进行了比较。结果发现:首先,能源类商品的套息收益一般为正,而农作物类商品的套息收益一般为负,金属与畜牧类商品的套息收益较不确定。从收益的波动性上看,农作物类商品套息收益的波动性较大,而金属类商品的波动性较低。     

大宗商品定价金融化与河北省资源安全保障研究

近年来,全球大宗商品价格经历了新一轮波动.随着港交所成功收购LME以及沃尔克法则、巴塞尔协议Ⅲ、全面资本分析和审查(CCAR)等监管规则的实施,欧美投行纷纷收缩商品交易业务,后危机时代大宗商品市场面临重新洗牌.本文回顾并展望了国内外大宗商品定价机制及其演变轨迹,并结合河北省实际提出相关对策建议.     

基于蒙特卡罗模拟的多标的资产期权定价研究

在单标的资产价格随机模型的基础上,推导了具相关性的多标的资产价格的随机过程公式,以此构造蒙特卡罗模拟高维欧式期权定价的随机模型,给出模拟算法,并分析了影响蒙特卡罗模拟效果的几个关键因素.模拟算例的结果显示模拟效果较好.     

大宗商品金融化文献综述

大宗商品金融化一直是学术界研究的热点。现存文献较多,但是鲜有大宗商品金融化系统的文献综述。总揽各专家学者的观点,梳理大宗商品金融化的成果,总结其研究的不足尤为重要。本文通过对国际国内文献的回顾,对大宗商品金融化的定义、原因及其影响成果进行了较为系统的梳理。本文的研究以期丰富大宗商品金融化的理论研究,并为我国大宗商品的健康发展提供相应的理论指导。     

基于Copula GARCH的多资产期权定价

自从Black和Scholes在1976年开创性的提出了BS期权定价公式以来,期权定价理论得到了极大的发展,而在其后的研究中发展基于BS公式的隐含波动率对于执行价格具有类似微笑的曲线,其原因是标的资产的过程并不是BS公式所假设的几何布朗运动。Engle提出的ARCH模型和Bollerslev提出的GARCH模型能对标的资产的收益率序列进行很好的描述,因此将GARCH模型引入期权定价。在多资产期权定价研究当中,最为关键的是标的资产之间的依赖关系,关于依赖关系的最有力的就是Copula理论,而Copula的一大优势就是可以将边缘分布和联合分布分开,可以分别考虑边缘分布和数据的相关结构。本文设想将多元GARCH引入多资产期权定价中,但是一般的多元GARCH的系数过多而不易估计,另一方面模型的灵活程度较小,所以用一元的GARCH模型分别对各个标的资产的收益率序列进行建模,再用Copula将各个资产的分布联接起来,这便是Copula based MGARCH模型。接下来便可以通过Monte Carlo模拟对期权进行定价。     

Binomial option pricing with stochastic parameters: A beta distribution approach

This research extends the binomial option-pricing model of Cox, Ross, and Rubinstein (1979) and Rendleman and Barter (1979) to the case where the up and down percentage changes of stock prices are stochastic. Assuming stochastic parameters in the discrete-time binomial option pricing is analogous to assuming stochastic volatility in the continuous-time option pricing. By assuming that the up and down parameters are independent random variables following beta distributions, we are able to derive a closed-form solution to this stochastic discrete-time option pricing. We also derive an upper and a lower bounds of the option price.     

A Continuous Time Model to Price Commodity-Based Swing Options

On the commodity market there exist contracts which give the holder multiple opportunities to adjust delivery of the underlying commodity. These contracts are often named “Swing” or “take-or-pay” options. They are especially common on the electricity market.In this paper the price of a Swing option on commodities is investigated under the additional constraint of a recovery time between two different exercise times. We give an explicit characterization of the price function as the value function of a continuous stochastic impulse control problem and prove existence of an optimal control. We investigate the connection between the price function and the solution of a system of quasi-variational inequalities. Finally, we present a numerical algorithm for solving the quasi-variational inequalities, and give some numerical examples.JEL Classification: C61, C62, C63     

American option valuation under stochastic interest rates

By applying Ho, Stapleton and Subrahmanyam's (1997, hereafter HSS) generalised Geske–Johnson (1984, hereafter GJ) method, this paper provides analytic solutions for the valuation and hedging of American options in a stochastic interest rate economy. The proposed method simplifies HSS's three-dimensional solution to a one-dimensional solution. The simulations verify that the proposed method is more efficient and accurate than the HSS (1997) method. We illustrate how the price, the delta, and the rho of an American option vary between the stochastic and non-stochastic interest rate models. The magnitude of this effect depends on the moneyness of the option, interest rates, volatilities of the underlying asset price and the bond price, as well as the correlation between them. This revised version was published online in June 2006 with corrections to the Cover Date.     

The Pricing Formula for Commodity-Linked Bonds with Stochastic Convenience Yields and Default Risk

At the maturity, the owner of a commodity-linked bond has the right to receive the face value of the bond and the excess amount of spot market value of the reference commodity bundle over the prespecified exercise price. This payoff structure is an important characteristic of the commodity-linked bonds. In this paper, we derive closed pricing formulae for the commodity-linked bonds. We assume that the reference commodity price and the value of the firm (bonds' issuer) follow geometric Brownian motions and that the net marginal convenience yield and interest rate follow Ornstein–Uhlenbech processes. In the appendix, we derive pricing formulae for bonds which are the same as the above commodity-linked bonds, except that the reference commodity price in the definition of the payoff at the maturity is replaced by the value of a special asset which depends on the convenience yield. This revised version was published online in August 2006 with corrections to the Cover Date.     

Option Pricing under Stochastic Interest Rates: An Empirical Investigation

Using daily data of the Nikkei 225 index, call option prices and call money rates of the Japanese financial market,a comparison is made of the pricing performance of stock option pricing modelsunder several stochastic interest rate processes proposedby the existing term structure literature.The results show that (1) one option pricing modelunder a specific stochastic interest ratedoes not significantly outperformanother option pricing model under an alternative stochasticinterest rate, and (2) incorporating stochastic interest ratesinto stock option pricing does not contribute to the performanceimprovement of the original Black–Scholes pricing formula.     

Options as a predictor of common stock price changes

Since rather novel techniques such as neural nets allow investigation of nonlinear model specification previously untested, it may be that traditional models of price formation underperform through misspecification rather than market efficiency. This paper explores whether a multilayer backpropagation model offers exploitable profit opportunities for some limited period. Using an intradaytransaction dataset obtained from the European Options Exchange (Amsterdam), We attempted to predict the return on Philips. Two neural nets are contrasted to ordinary linear regression analysis on the basis of three benchmarks (MSE, and net realized returns). An adaptively trained 33-14-1 architecture scored best on all criteria and yielded an annualized 11% return following a simple one-period trading strategy.     

固定利率住房抵押贷款定价研究——基于隐含期权视角

由于缺乏经验,我国商业银行固定利率住房抵押贷款定价存在不合理性,既影响了固定利率住房抵押贷款业务的健康发展,又导致了借贷双方风险与收益的不匹配。在市场利率服从几何布朗运动的基础上．本文从隐含期权视角出发构建固定利率住房抵押贷款单位风险收益最大化模型,并求出最优解,为商业银行固定利率住房抵押贷款定价提供理论和技术参考。     

An empirical investigation of the factors that determine the pricing of Dutch index warrants

This paper investigates the pricing of Dutch index warrants. It is found that when using the historical standard deviation as an estimate for the volatility, the Black and Scholes model underprices all put warrants and call warrants on the FT-SE 100 and the CAC 40, while it overprices the call warrants on the DAX. When the implied volatility of the previous day is used the model prices the index warrants fairly well. When the historical standard deviation is used the mispricing of the call and the put warrants depends in a strong way on the mispricing of the previous trading day, and on the moneyness (in a non-linear way), the volatility, and the dividend yield. When the implied standard deviation of the previous trading day is used the mispricing of the call warrants is only related to the moneyness and to the estimated volatility, while the mispricing of put index warrants depends in a strong way on the moneyness, the volatility, the dividend yield and the remaining time to maturity.