Abstract
It has been discussed in many literatures that commodity prices tend to follow mean reversion model. This means that when there is a jump price in certain time, the price will revert to the mean price in the future. In this research, the method to determine the existence of mean-reversion of soybean price dynamics is discussed. Then, the future contract of soybeans is calculated using mean-reversion simulation and the spot-future parity theorem. Both methods are applied to the closing price of soybeans for the period of 19 September 2011 to 28 April 2016. The results show that the future contract price calculated by Model Mean-Reversion simulation under estimate the future contract price determined by the spot-future parity theorem.
Highlights
It has been discussed in many literatures that commodity prices tend to follow mean reversion model
The results show that the future contract price calculated by Model Mean-Reversion simulation under estimate the future contract price determined by the spot-future parity theorem
On the Simulation and Estimation of the Mean-Reverting OrnsteinUhlenbeck Process
Summary
Dari Tabel 1 terlihat bahwa data komoditas kedelai memiliki bentuk yang tidak simetris. Hal ini ditunjukkan dengan adanya kemencengan data kearah kanan, dan data juga menunjukkan adanya ekor gemuk (fat tail). Hal ini dapat dilihat dari nilai kurtosis yang cukup besar yaitu 7.2938, dimana nilai normalnya adalah 3. Estimasi Parameter dengan MRA dan MRG Untuk mensimulasikan proses mean Reversion, perlu dilakukan estimasi parameter dan. Dalam penaksiran ini diasumsikan bahwa parameter tersebut tetap konstan selama periode waktu estimasi. 3. Selain menggunakan sekater plot seperti langkah 2, dapat juga dilihat nilai dari pvalue. 4. Nilaiyaitu parameter yang menyatakan kecepatan kembali ke rataan, diestimasi dari negatif dari koefisien variabel , dalam hal in 5. Estimasi nilai rataan jangka panjang didapat dari hasil bagi intercept dengan variabel. 6. Volatilitas dari proses mean-reversion diestimasi dari standar error regresi, yaitu
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