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 In this write-up we study the historical optimal hedging ratio for the KW vs CA spread. This work follows from previous research on the optimal hedging ratio of the C vs S spread.
The table below gives the contract specifications of KW and CA. Notice that the tons per contract is basically equivalent, i.e. when sizing up the positions on a ton for ton basis we make use of a one to one ratio.
Introduction When a simple question does not have a simple answer it might point to something interesting that is worth threading out. The simple question we attempt to solve deals with how to size up a relative value futures pairs trade so that it gives the best risk adjusted return. A couple examples we will consider include
Ton for ton Equal notional exposure Equal volatility adjusted notional exposure Equal (at the money) implied volatility adjusted notional exposure Hedging ratio from cointegration test Machine Learning solution We also explore the possible evolution of the hegding ratio as a function of fundamentals or seasonal input features, or even the shape of the futures curves of the underlying commodities.
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.
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 AC vs AK 3 BO vs IJ 4 BO vs SH 5 C vs CA 6 C vs CT 7 C vs CUA 8 C vs DL 9 C vs EP 10 C vs FC 11 C vs KW 12 C vs LC 13 C vs LH 14 C vs MW 15 C vs RR 16 C vs S 17 C vs SB 18 C vs W 19 C vs YW 20 CC vs QC 21 CL vs CO 22 CO vs CL 23 China RS crush 24 China S crush 25 DA vs CHE 26 DF vs KC 27 EP vs CA 28 HO vs BO 29 HO vs XB 30 IJ vs RS 31 KO vs BO 32 KO vs PAL 33 KO vs SH 34 KW vs CA 35 KW vs MW 36 KW vs RR 37 LH vs LC 38 PAL vs SH 39 PAL vs ZRO 40 RS crush 41 RS vs BO 42 RS vs KO 43 S vs AK 44 SB vs CB 45 SB vs DL 46 SB vs QW 47 SH vs ZRO 48 SM vs AE 49 SM vs S 50 W vs CA 51 W vs KW 52 W vs MW 53 W vs S 54 XB vs CUA 55 XB vs DL 56 XB vs SB 57 YW vs WZ 58 ZRR vs AE 59 cattle roi 60 crack 321 61 crack 532 62 crush 63 Remarks 1 Introduction Following the results of the Sugar Spread and Curve Structure post we determine similar plots that show the returns and roll yields of a collection of different inter-commodity spreads we are interested in.
1 Intro and summary 2 BO vs IJ 3 BO vs SH 4 BO vs SH 5 C vs CRD 6 C vs CT 7 C vs CUA 8 C vs DL 9 C vs EP 10 C vs FC 11 C vs KW 12 C vs LC 13 C vs LH 14 C vs MW 15 C vs RR 16 C vs S 17 C vs SB 18 C vs W 19 C vs XB 20 C vs YW 21 CRD vs YW 22 cattle roi 23 China S crush 24 CC vs QC 25 CO vs CL 26 crack 321 27 crack 532 28 crush 29 DAE vs XB 30 DF vs KC 31 EP vs CA 32 HO vs BO 33 HO vs XB 34 IJ vs RS 35 RS vs S 36 KO vs BO 37 KW vs EB 38 KW vs MW 39 KW vs RR 40 LH vs LC 41 LS vs LC 42 QK vs CA 43 RS crush 44 RS Oil vs BO 45 RS vs BO 46 RS vs KO 47 S vs SY 48 SB vs DL 49 SB vs QW 50 SB vs CB 51 W vs CA 52 W vs EB 53 W vs KW 54 W vs MW 55 W vs S 56 XB vs DL 57 XB vs SB 58 YW vs WZ 59 SM vs AE 60 S vs AK 61 S vs AK 62 KO vs PAL 63 KO vs SH 1 Intro and summary This document shows how we think about the sizing of all the relative trades.