Introduction H H - Feature Importance H - Fingerprint Method H - Model Results K K - Feature Importance K - Fingerprint Method K - Model Results N N- Feature Importance N - Fingerprint Method N - Model Results U U - Feature Importance U - Fingerprint Method U - Model Results Z Z - Feature Importance Z - Fingerprint Method Z - Model Results Grouped Forecasts Conclusion Introduction This post is a rehash of a previous post with the same title except for the 2.
Introduction In previous posts we have explored ideas on how to construct fundamental models for forecasting the price of corn and soybean. These models used as input parameters the stock-to-usage numbers calculated from the monthly WASDE reports together with the Dollar index, the mean value of crude in the previous month and the Ruble vs Dollar exchange rate. The aim of this report is to extend these results to a spread between two related commodities, in this case Corn and European wheat.
Introduction In previous posts we have explored ideas on how to construct fundamental models for forecasting the price of corn and soybean. These models used as input parameters the stock-to-usage numbers calculated from the monthly WASDE reports together with the Dollar index, the mean value of crude in the previous month and the Ruble vs Dollar exchange rate. The aim of this report is to extend these results to a spread between two related commodities, in this case Kansas and European wheat.
Introduction In previous posts we have explored ideas on how to construct fundamental models for forecasting the price of corn and soybean. These models used as input parameters the stock-to-usage numbers calculated from the monthly WASDE reports together with the Dollar index, the mean value of crude in the previous month and the Ruble vs Dollar exchange rate. The aim of this report is to extend these results to a spread between two related commodities, in this case corn and Soybeans.
1 Introduction 2 Modeling the Spread 3 Modeling the Ratio 4 Roll Structure 4.1 Corn Calendars 4.2 Kansas Wheat Calendars 5 Hypothetical Scenario 6 Remarks 1 Introduction In previous posts we have explored ideas on how to construct fundamental models for forecasting the price of corn and wheat. These models used as input parameters the stock-to-usage numbers calculated from the monthly WASDE reports together with the Dollar index, the mean value of crude in the previous month and the Ruble vs Dollar exchange rate.
Introduction WASDE reports get published around the 10th of every month and is a date that every commodity trader looks forward to. This is the time of the month when the USDA gives the market a glipse of their take on the current balance sheets of the main agricultural commodities of the major producing and consuming nations. A classic number that many commodity traders look at is the stock-to-usage ratio.
1 Introduction 2 Deterministic Model 3 Probabilistic Model 3.1 United States Stock-to-Usage 3.2 World Stock-to-Usage 3.3 World Stock-to-Usage without China 3.4 Mean Crude 3.5 Dollar Index 4 Ensemble Model 4.1 United States Stock-to-Usage Sensitivity 4.2 Crude Sensitivity 5 Remove Crude 6 Only Crude an United States stock-to-usage 7 Predictions 8 Comments 1 Introduction Here we explore the viability of modelling the price of soybeans as a function of stock-to-usage.
1 Introduction 2 Deterministic Model 2.1 Deterministic Model Sensitivity 2.2 Deterministic Curve Prediction 3 Probabilistic Model 3.1 United States HRW Stock-to-Usage 3.2 United States Total Wheat Stock-to-Usage 3.3 World Stock-to-Usage 3.4 World Stock-to-Usage without China 3.5 Mean Crude 3.6 Dollar Index 3.7 Ruble 4 Ensemble Model 4.1 Crude Sensitivity 4.2 United States HRW Stock-to-Usage Sensitivity 4.3 World Stock-to-Usage Sensitivity 4.
1 Introduction 2 Deterministic Model 2.1 Deterministic Model Sensitivity 2.2 Deterministic Curve Prediction 3 Probabilistic Model 3.1 World Stock-to-Usage 3.2 World Stock-to-Usage without China 3.3 Mean Crude 3.4 Dollar Index 4 Ensemble Model 4.1 United States Stock-to-Usage Sensitivity 4.2 World Stock-to-Usage Sensitivity 4.3 World Stock-to-Usage without China Sensitivity 4.4 Crude Sensitivity 5 Only Crude, US and World Stocks 6 Model predictions given USDA numbers 1 Introduction Here we explore the viability of modelling the price of corn as a function of stock-to-usage.