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In this paper we present a stochastic model for daily average temperature to calculate the temperature indices upon which temperature-based derivatives are written. We propose a seasonal mean and volatility model that describes the daily average temperature behavior using the mean-reverting Ornstein-Uhlenbeck process. We also use higher order continuous-time autoregressive process with lag 3 for modeling the time evolution of the temperatures after removing trend and seasonality. Our model is fitted to 11 years of data recorded, in the period 1 January 2005 to 31 December 2015, Bahir Dar, Ethiopia, obtained from Ethiopia National Meteorological Services Agency. The analytical approximation formulas are used to price heating degree days (HDD) and cooling degree days (CDD) futures. The suggested model is analytically tractable for derivation of explicit prices for CDD and HDD futures and option. The price of the CDD future is calculated, using analytical approximation formulas. Numerical examples are presented to indicate the accuracy of the method. The results show that our model performs better to predict CDD indices. 相似文献
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本文提出了一个基于温度的导数来计算温度指数的日平均温度随机模型,该模型提出了一个季节性均值及其波动率的计算方法,使用均值回归的Ornstein-Uhlenbeck过程来刻画日平均温度的变化。本文还采用连续的三阶自回归过程来模拟去除趋势和季节性影响后的温度演变过程,模型的模拟结果与从埃塞俄比亚国家气象厅获得的2005年1月1日至2015年12月31日11年间埃塞俄比亚Bahir Dar记录的数据非常吻合。验证后的近似公式很容易根据热日和冷日(heating degree days (HDD) and cooling degree days (CDD))等典型温度指数推导期货价格,也给出了数值例子来说明该方法的准确性。结果表明,本文提出的模型比其他模型能更好地预测CDD指数。 相似文献
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Tesfahun Berhane Nurilign Shibabaw Aemiro Shibabaw Molalign Adam Abera A.Muhamed 《资源与生态学报(英文版)》2018,(3)
埃塞俄比亚咖啡价格波动很大,因此对国家经济发展的影响不容小视,对咖啡价格进行预测具有理论和实践意义。为了分析咖啡价格波动,我们采用来自埃塞俄比亚商品交易所(ECX)记录的2008年6月25日至2017年1月5日期间咖啡日收盘价数据。在这里,咖啡价格的性质是非平稳的,我们在单个线性状态空间模型上应用卡尔曼滤波算法来预测咖啡价格的最优值,主要通过使用均方根误差(RMSE)来评估用于预测咖啡价格的算法的性能。基于线性状态空间模型和卡尔曼滤波算法,均方根误差(RMSE)为0.000016375,说明该算法性能良好,研究结果可靠。 相似文献
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Tesfahun Berhane Nurilign Shibabaw Aemiro Shibabaw Molalign Adam Abera A. Muhamed 《资源与生态学报(英文版)》2018,9(3):302-305
Ethiopian coffee price is highly fluctuated and has significant effect on the economy of the country. Conducting a research on forecasting coffee price has theoretical and practical importance.This study aims at forecasting the coffee price in Ethiopia. We used daily closed price data of Ethiopian coffee recorded in the period 25 June 2008 to 5 January 2017 obtained from Ethiopia commodity exchange (ECX) market to analyse coffee prices fluctuation. Here, the nature of coffee price is non-stationary and we apply the Kalman filtering algorithm on a single linear state space model to estimate and forecast an optimal value of coffee price. The performance of the algorithm for estimating and forecasting the coffee price is evaluated by using root mean square error (RMSE). Based on the linear state space model and the Kalman filtering algorithm, the root mean square error (RMSE) is 0.000016375, which is small enough, and it indicates that the algorithm performs well. 相似文献
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This paper aims at the spatiotemporal distribution of rainfall in Ethiopia and developing stochastic daily rainfall model. Particularly, in this study, we used a Markov Chain Analogue Year (MCAY) model that is, Markov Chain with Analogue year (AY) component is used to model the occurrence process of daily rainfall and the intensity or amount of rainfall on wet days is described using Weibull, Log normal, mixed exponential and Gamma distributions. The MCAY model best describes the occurrence process of daily rainfall, this is due to the AY component included in the MC to model the frequency of daily rainfall. Then, by combining the occurrence process model and amount process model, we developed Markov Chain Analogue Year Weibull model (MCAYWBM), Markov Chain Analogue Year Log normal model (MCAYLNM), Markov Chain Analogue Year mixed exponential model (MCAYMEM) and Markov Chain Analogue Year gamma model (MCAYGM). The performance of the models is assessed by taking daily rainfall data from 21 weather stations (ranging from 1 January 1984-31 December 2018). The data is obtained from Ethiopia National Meteorology Agency (ENMA). The result shows that MCAYWBM, MCAYMEM and MCAYGM performs very well in the simulation of daily rainfall process in Ethiopia and their performances are nearly the same with a slight difference between them compared to MCAYLNM. The mean absolute percentage error (MAPE) in the four models: MCAYGM, MCAYWBM, MAYMEM and MCAYLNM are 2.16%, 2.27%, 2.25% and 11.41% respectively. Hence, MCAYGM, MCAYWBM, MAYMEM models have shown an excellent performance compared to MCAYLNM. In general, the light tailed distributions: Weibull, gamma and mixed exponential distributions are appropriate probability distributions to model the intensity of daily rainfall in Ethiopia especially, when these distributions are combined with MCAYM. 相似文献
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