摘要翻译:
我们研究了商品现货价格和期货估价的一般多因素模型。我们在两个重要方面对Schwartz and Smith(2000)和Yan(2002)的多因子多空模型进行了扩展:第一,我们考虑了长期和短期动态因子的均值回归,并引入了随机波动因子;第二,我们建立了一个加性结构季节性模型。然后给出了模型的Milstein离散化非线性随机波动率状态空间表示,在观测方程中考虑了期货和期权合约。然后,我们发展了一种基于先进的序贯蒙特卡罗算法的数值方法,利用粒子马尔可夫链蒙特卡罗对模型进行校正,并对多空动态和波动因子的潜在过程进行滤波。在这方面,我们探索和发展了一种新的方法,基于自适应Rao-Blackwellized版本的粒子马尔可夫链蒙特卡罗方法。在这样做时,我们准确地处理了状态空间模型中的非线性,从而将其引入滤波框架。我们对石油商品的合成数据和实际数据进行了分析。
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英文标题:
《Calibration and filtering for multi factor commodity models with
seasonality: incorporating panel data from futures contracts》
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作者:
Gareth W. Peters, Mark Briers, Pavel V. Shevchenko and Arnaud Doucet
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最新提交年份:
2011
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分类信息:
一级分类:Quantitative Finance 数量金融学
二级分类:Computational Finance 计算金融学
分类描述:Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法,包括蒙特卡罗,偏微分方程,格子和其他数值方法,并应用于金融建模
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一级分类:Quantitative Finance 数量金融学
二级分类:Statistical Finance 统计金融
分类描述:Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类:Statistics 统计学
二级分类:Computation 计算
分类描述:Algorithms, Simulation, Visualization
算法、模拟、可视化
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一级分类:Statistics 统计学
二级分类:Methodology 方法论
分类描述:Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计,调查,模型选择,多重检验,多元方法,信号和图像处理,时间序列,平滑,空间统计,生存分析,非参数和半参数方法
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英文摘要:
We examine a general multi-factor model for commodity spot prices and futures valuation. We extend the multi-factor long-short model in Schwartz and Smith (2000) and Yan (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. Then a Milstein discretized non-linear stochastic volatility state space representation for the model is developed which allows for futures and options contracts in the observation equation. We then develop numerical methodology based on an advanced Sequential Monte Carlo algorithm utilising Particle Markov chain Monte Carlo to perform calibration of the model jointly with the filtering of the latent processes for the long-short dynamics and volatility factors. In this regard we explore and develop a novel methodology based on an adaptive Rao-Blackwellised version of the Particle Markov chain Monte Carlo methodology. In doing this we deal accurately with the non-linearities in the state-space model which are therefore introduced into the filtering framework. We perform analysis on synthetic and real data for oil commodities.
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PDF链接:
https://arxiv.org/pdf/1105.5850