Corn

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 The aim of this write-up is to investigate what fundamental features can be seen as the driver of corn calendar spreads. For each calendar spread we start out with a random forest model that tries to forecast the value of the spread with input features consisting of the stock-to-usage numbers of Argentina Brazil China Russia Ukraine United States World World without China for both corn and soybeans as well as the number of days the front month contract has to expiry.

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 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.

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.

1 Introduction 2 Planting Pace 3 Events Study 4 Remarks 1 Introduction Recently it has been in the news that US corn planting has been slow compared to previous years. This will impact the corn acreage planted as well as the yields that the United States hopes to achieve. In this post we have a closer look at past cases of slow planting and the subsequent returns after the data has been made avaible on NASS.

1 Introduction 2 Deferred vs Front month prices 3 Option Strategies 4 Spread vs Stock-to-Usage 5 Remarks 1 Introduction We are interested in hedging away downside risk of bear spreads using call options on the near dated leg of a bear spread. This is a strategy that can be usefull for commodities, such as corn, which have weather markets that can drive the price action during certain parts of the year.

1 Introduction 2 Corn ZH Example 3 Shiny 1 Introduction Full carry is achieved when the price of a later dated contract can be expressed as the price of the near contract plus the full cost of carrying the underlying commodity between the months. Carrying costs include interest, insurance and storage. Carry costs change over time. For example, storage costs in a warehouse may increase while interest rates to finance the underlying may increase or decrease.

Introduction Safex YW vs Cbot Safex YW vs Zar Introduction The aim of this report is to investigate the correlations between the pairs (Safex YW, Zar) and (Safex YW, Cbot) as a function of the distance in Zar from Export Parity. The plot below shows the evolution of Safex YW since 2007. The blue ribbon is the price range defined by export (bottom) and import (top) parity.