Introduction In this post we indroduce another way to express a relative value trade between two different commodities. The main idea is to use a bullspread in one commodity and a bearspread in a closely related commodity. In this way we can use different calendar spreads to express our relative value point of view. This method is interesting because it enables us to use previous work on the modeling of calendar spreads using fundamental parameters to give possible ranges where the relative value spread should be given the underlying fundamentals.
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 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.
Introduction In this write-up, we explore how the front month price and time to expiry of the front-month contract can be used to model the C UZ spread.
Seasonalality Using a similar methodology to the Calendar Spread Seasonal Entries and Exits post, we study the roll adjusted seaonal behaviour of the C UZ spread.
The plot below shows the continuous and roll adjusted C UZ spreads since 2000.
1 Introduction 2 Nomenclature 3 Curve Shape 3.1 Backwardation and Contango 3.2 The driver of long-term returns 4 Relative Value commodity Universe 4.1 Calendar Spreads 4.1.1 Bull Spreads 4.1.2 Bear Spreads 4.2 Inter-Commodity Spreads 4.2.1 Convergence Trades 4.2.2 Divergence Trades 1 Introduction In this document, we give some detail about the relative value commodity universe in which the Polar Star Limited fund invests.
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 Roll Adjusted Prices 3 The Role of Roll Yield 4 Sugar Spread Volatility vs Roll Yield 5 Remarks 1 Introduction The raw-refined sugar spread, SB vs QW, is one of our bread and butter processing margin trades. Raw sugar in the form of sugar cane or beets have to be refined to get the normal white sugar we are all used to. In the Sugar Trading Manual - Cost of production the author adds a fixed cost of USD 65/t which equates to their estimate of the world average cost of upgrading raw sugar to refined sugar in autonomous refinerries.
Introduction Traditionally we identify possible inter-commodity and calendar spreads based on whether the spread is stretched or suppressed compared to historical levels. As a reference we normally use the period closest to expiration of the first contract, say the last forty days before exipry, to compare the spreads. This method can be enhanced by studying the seasonal tendancies of the different spreads. Intuitively this makes to most sense for cyclical commodities such as the agricultural and meats markets.
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.