Energy futures prices: term structure models with Kalman filter estimation

We present a class of multi-factor stochastic models for energy futures prices, similar to the interest rate futures models recently formulated by Heath. We do not postulate directly the risk-neutral processes followed by futures prices, but define energy futures prices in terms of a spot price, not directly observable, driven by several stochastic factors. Our formulation leads to an expression for futures prices which is well suited to the application of Kalman filtering techniques together with maximum likelihood estimation methods. Based on these techniques, we perform an empirical study of a one- and a two-factor model for futures prices for natural gas.     

期货市场中持仓量及参与者对资产价格的影响研究

本文通过建立一个期货市场的均衡模型,提出在具有套保需求和有限风险承受能力的前提下,期货价格能够预测未来资产价格变动的方向,持仓量能够辅助预测未来资产价格变动的剧烈程度;此外,市场中不知情投机者具有风险调整市场收益的作用,不知情套保者的参与能够稳定市场。对于持仓量是否能够辅助预测未来资产价格变动的剧烈程度,本文利用中国商品期货市场数据进行了实证检验,结果表明与理论研究的结论一致。     

Determination of the Probability Distribution Measures from Market Option Prices Using the Method of Maximum Entropy in the Mean

Abstract

We consider the problem of recovering the risk-neutral probability distribution of the price of an asset, when the information available consists of the market price of derivatives of European type having the asset as underlying. The information available may or may not include the spot value of the asset as data. When we only know the true empirical law of the underlying, our method will provide a measure that is absolutely continuous with respect to the empirical law, thus making our procedure model independent. If we assume that the prices of the derivatives include risk premia and/or transaction prices, using this method it is possible to estimate those values, as well as the no-arbitrage prices. This is of interest not only when the market is not complete, but also if for some reason we do not have information about the model for the price of the underlying.     

On the simulation of portfolios of interest rate and credit risk sensitive securities

We discuss extensions of reduced-form and structural models for pricing credit risky securities to portfolio simulation and valuation. Stochasticity in interest rates and credit spreads is captured via reduced-form models and is incorporated with a default and migration model based on the structural credit risk modelling approach. Calculated prices are consistent with observed prices and the term structure of default-free and defaultable interest rates. Three applications are discussed: (i) study of the inter-temporal price sensitivity of credit bonds and the sensitivity of future portfolio valuation with respect to changes in interest rates, default probabilities, recovery rates and rating migration, (ii) study of the structure of credit risk by investigating the impact of disparate risk factors on portfolio risk, and (iii) tracking of corporate bond indices via simulation and optimisation models. In particular, we study the effect of uncertainty in credit spreads and interest rates on the overall risk of a credit portfolio, a topic that has been recently discussed by Kiesel et al. [The structure of credit risk: spread volatility and ratings transitions. Technical report, Bank of England, ISSN 1268-5562, 2001], but has been otherwise mostly neglected. We find that spread risk and interest rate risk are important factors that do not diversify away in a large portfolio context, especially when high-quality instruments are considered.     

A dimension and variance reduction Monte-Carlo method for option pricing under jump-diffusion models

We develop a highly efficient MC method for computing plain vanilla European option prices and hedging parameters under a very general jump-diffusion option pricing model which includes stochastic variance and multi-factor Gaussian interest short rate(s). The focus of our MC approach is variance reduction via dimension reduction. More specifically, the option price is expressed as an expectation of a unique solution to a conditional Partial Integro-Differential Equation (PIDE), which is then solved using a Fourier transform technique. Important features of our approach are (1) the analytical tractability of the conditional PIDE is fully determined by that of the Black–Scholes–Merton model augmented with the same jump component as in our model, and (2) the variances associated with all the interest rate factors are completely removed when evaluating the expectation via iterated conditioning applied to only the Brownian motion associated with the variance factor. For certain cases when numerical methods are either needed or preferred, we propose a discrete fast Fourier transform method to numerically solve the conditional PIDE efficiently. Our method can also effectively compute hedging parameters. Numerical results show that the proposed method is highly efficient.     

Eurodollar futures pricing in log-normal interest rate models in discrete time

We demonstrate the appearance of explosions in three quantities in interest rate models with log-normally distributed rates in discrete time. (1) The expectation of the money market account in the Black, Derman, Toy model, (2) the prices of Eurodollar futures contracts in a model with log-normally distributed rates in the terminal measure and (3) the prices of Eurodollar futures contracts in the one-factor log-normal Libor market model (LMM). We derive exact upper and lower bounds on the prices and on the standard deviation of the Monte Carlo pricing of Eurodollar futures in the one factor log-normal Libor market model. These bounds explode at a non-zero value of volatility, and thus imply a limitation on the applicability of the LMM and on its Monte Carlo simulation to sufficiently low volatilities.     

Stochastic Models for Oil Prices and the Pricing of Futures on Oil

Abstract

In this article, we investigate and compare the performance of various one-factor diffusion models in their ability to capture the behaviour of Brent crude oil prices. New proposed models, which have a three-quarters power in the diffusion term, are found to outperform all other popular models tested. Analytic solutions for futures prices under the new models are found and used to calibrate market prices. Results from the calibration show that one of the new three-quarters models with a mean-reverting property outperforms other popular models in fitting and forecasting futures prices.     

基于状态转移模型的条件期望与方差——从2状态到N状态的推广

基于状态转移模型计算的条件期望与方差,可以应用到金融领域,计算和度量市场在不同状态下的收益与风险.Nielson基于2状态转移模型,计算了2状态下股市的收益率的条件期望与方差.然而,实际研究中,常需要用到3状态、甚至多状态的状态转移模型.因此,基于Nielson的研究,从2状态推广到了$N$状态.基于$N$状态转移模型计算了条件期望、条件方差及无条件期望、无条件方差,该结果更具普遍性且形式更为简洁.最后,采用计算期望与方差的方法,分析中国股市收益率与波动率.实证结果表明,中国股市除存在牛市、熊市外,还存在政策市,且其具有`` 低风险,高收益"的特点.利用$N$状态转移模型计算的期望与方差可以更合理地度量金融市场在不同情况下的收益与风险.     

Calibration and Filtering for Multi Factor Commodity Models with Seasonality: Incorporating Panel Data from Futures Contracts

We construct a general multi-factor model for estimation and calibration of commodity spot prices and futures valuation. This extends the multi-factor long-short model in Schwartz and Smith (Manag Sci 893–911, 2000) and Yan (Review of Derivatives Research 5(3):251–271, 2002) in two important aspects: firstly we allow for both the long and short term dynamic factors to be mean reverting incorporating stochastic volatility factors and secondly we develop an additive structural seasonality model. In developing this non-linear continuous time stochastic model we maintain desirable model properties such as being arbitrage free and exponentially affine, thereby allowing us to derive closed form futures prices. In addition the models provide an improved capability to capture dynamics of the futures curve calibration in different commodities market conditions such as backwardation and contango. A Milstein scheme is used to provide an accurate discretized representation of the s.d.e. model. This results in a challenging non-linear non-Gaussian state-space model. To carry out inference, we develop an adaptive particle Markov chain Monte Carlo method. This methodology allows us to jointly calibrate and filter the latent processes for the long-short and volatility dynamics. This methodology is general and can be applied to the estimation and calibration of many of the other multi-factor stochastic commodity models proposed in the literature. We demonstrate the performance of our model and algorithm on both synthetic data and real data for futures contracts on crude oil.     

Price limits and capital requirements of futures clearinghouses

Futures clearinghouses need capital to provide liquidity in case of default by clearing members. Price limits truncate observed futures prices and prevent observation of clearinghouses’ true default risk exposure. We show how to estimate the true default risk exposure from observed futures prices and model capital requirements using an option pricing model, which accounts for non-normality of and truncation in observed futures returns. We apply the model to the clearinghouse associated with the Winnipeg Commodity Exchange, compare required capital levels with actual capital levels and show that ignoring non-normality of futures returns causes overall capital requirements to be significantly underestimated.     

郑州白糖期货的因果图分析

主要探讨郑州白糖期货价、纽约白糖期货价和郑州白糖现货价格三者之间的动态关系,利用图模型方法、多维的多元线性回归等方法来分析它们之间的相互影响关系.又由于三者之间的关系受到牛市、熊市等市场因素的影响,故在熊市、牛市和震荡市三种情况下分别探讨三者的关联性.结果显示:不论市场是熊市还是牛市或者是震荡市,郑州白糖期货价都受到纽约白糖期货价的影响作用,郑州白糖现货价都受到郑州白糖期货价的影响;在市场为牛市时,纽约白糖期货价对郑州白糖现货价有显著影响.     

基于高频数据的中国国债期货价格发现能力研究

期货的价格发现能力是近几年国际学术界关注的热点问题,但目前理论界相关研究主要集中于商品期货和股指期货,尚缺乏专门针对中国国债期货价格发现方面的研究。随着中国5年期国债期货于2013年9月上市交易,深入研究中国市场结构下的国债期货价格发现能力有助于从微观视角掌握与其它期货品种内在运行规律的差异性。本文运用中国5年期国债期货上市交易后的5分钟高频数据,采取向量误差修正(VECM)模型和Granger因果关系检验等计量分析方法检验中国国债期货与现货价格之间的关系,并创新性地使用共同因子贡献法和信息份额法分析我国国债期货市场与现货市场对价格发现功能的贡献程度。结果表明,中国国债期货价格与现货价格之间存在长期协整关系。中国国债期货价格是现货价格的Granger成因,且两者之间存在单向的价格引导关系。同时通过实证得出中国国债期货市场在对价格发现的贡献程度上占主导地位的结论。     

期权标的资产波动率的重构方法

在B lack-Scho les公式中,波动率σ是一个非常重要的参数.并且在诸如股票、利率、股指期货等标的资产的交易市场中,人们往往希望知道标的资产未来价格的波动率,从而知道该资产的未来风险结构.但一般来说,由于事件还没有发生,人们对σ的未来走向很难预测.但可以运用B lack-Scho les的理论框架,从期权市场获取的信息去重构标的资产价格的波动率.论文使用的是基于T ikhonov正则化的数值微分方法,利用Dup ire公式去重构标的资产的未来预期波动率.相对于其他方法,该算法更加快速有效,并且能识别标的资产的预期风险突变.     

基于DCC-GARCH模型的金属期货市场与外汇、货币市场的动态相关性研究

在本文中主要是把DCC-GARCH模型,引入到金属期货市场分别与外汇市场和货币市场之间的动态相关性领域进行研究。通过对LME铜分别与RMB/USD、USD/EU、JPY/USD三个汇率之间的动态相关性研究,研究结果表明:金属期货市场与外汇市场之间有一定的动态相关性,但是不是很强烈;通过对LME铜分别与RMB-USD、USD-EU、JPY-/USD三个利差变化率之间的动态相关性研究,结果表明:金属期货市场与货币市场之间的动态相关性并不明显。     

基于二次优化BP神经网络的期货价格预测

针对BP算法存在的不足,结合神经网络、遗传算法和主成分分析的优点,提出基于二次优化BP神经网络的期货价格预测算法.初次优化采用主成分分析法对网络结构进行优化,第二次优化采用自适应遗传算法对网络参数进行优化,将经过二次优化后建立的BP神经网络模型用于期货价格预测.经仿真检验,用新方法建立的模型对期货价格进行预测,在预测的精度和速度方面都优于单纯BP神经网络模型.     

Modeling futures price dynamics on the RTS and MICEX indices

Reasons for the substantial differences of historical and theoretical futures prices on RTS and MICEX indices are investigated. A model is proposed that considers the observed differences for the modeling of futures prices within the risk assessment of a portfolio of derivatives using the Monte Carlo method.     

天然气期货和现货价格间的相关性研究

基于天然气期货价格与现货价格序列间具有强非线性特征,本文将GARCH模型和Copula函数思想进行结合,同时考虑了天然气期货和现货价格间的时变相关结构,构建了时变Copula(GARCH-Normal、GARCH-GED和GARCH-t)模型,利用美国纽约商品交易所(NYMEX)Henry Hub交易中心天然气期货价格和现货价格数据进行实证研究。实证结果表明:GARCH-GED模型能够准确地拟合天然气期货与现货价格时间序列;时变SJC-Copula函数能够更好的描述天然气期货价格与现货价格间的相关性;天然气期货与现货价格间的相关性不是对称的,上尾的相关性小于下尾相的相关性。     

中美燃料油期货价格引导研究

本文利用ADF单整检验、EG协整检验、误差修正模型、引导关系检验等方法研究中美燃料油期货价格之间的互动关系,结果表明:我国燃料油期货在价格和价格收益方面均与国际燃料油市场存在明显的即时引导关系;国际燃料油期货市场上的价格变动对国内燃料油期货价格以及价格收益的变动具有单向的滞后引导作用;我国燃料油期货市场的价格和收益方面的信息对于国际市场几乎不产生影响,我国尚不具备制定价格的话语权。     

中国螺纹钢期货市场价格发现功能研究

螺纹钢期货价格发现功能研究对我国钢铁行业提高竞争力,争取钢铁成品和铁矿石定价权,引导螺纹钢期货市场健康发展具有重要作用。本文在向量误差修正模型(VEC)中引入剔除残差相关性的最小二乘算法,构建了用于测度期现货市场价格发现功能的永久短暂PT和信息份额IS共同因子模型,弥补了现有VEC模型由于求得的期现货残差序列相关性较大,导致PT和IS模型测算的信息贡献度存在较大差异的不足。在此基础上,利用2011年1月至2014年11月中国螺纹钢期现货市场933个日交易数据,验证了模型的有效性。     

波动率风险溢价:基于香港权证市场的实证

波动率风险溢价包含了关于投资者风险厌恶的重要信息,它的估计是金融计量学文献关注的一个核心问题。本文基于香港权证市场数据和GARCH扩散随机波动率(SV)模型,对香港证券市场的波动率风险溢价进行了估计研究。采用香港恒生指数和指数权证数据,通过建立基于有效重要性抽样的极大似然(EIS-ML)方法联合估计了GARCH扩散模型的客观与风险中性测度,进而得到了香港证券市场的波动率风险溢价。研究结果发现,在香港证券市场上,市场投资者对波动率风险进行了定价,即存在波动率风险溢价,且波动率风险溢价在绝大多数情形下为正,说明市场投资者总体表现为风险爱好。