You are invited to consult to the CFO of a US non-financial company regarding designing and implementing hedging strategy. Company is exposed a great deal to price fluctuation in commodity markets as it purchases materials for its production that are closely related to a traded commodity. This material represents 72% of the price of its final products. At current prices (!), annual sales level of the company amounts to USD 650 mln. Other components of the P&L are as follows. Direct labor cost is at the level of 13% of the final product, and overhead (OH) costs amount to USD 45.5 mln p.a.. Other direct materials (variable) costs represent 5% of annual sales. Company is unindebted.
1. Select one of the traded commodities (i.e. corn, alu, copper, cocoa, iron ore, …) to which the above hypothetical company is exposed (i.e. its material prices – 72% of sales – correlate to the traded commodity), and fetch the (spot prices!) data from the data vendor (you can freely select any you find available; e.g. Index-mundi, Yahoofinance, markets.businessinsider.com with monthly, weekly or daily frequency). This traded commodity should be quoted in USD. Create time series of the cost of the corelated material, whereby use properties of the selected commodity.
2. Then its nedeed to be create time series of the company sales, whereby you use:
a) the assumption of non-correlated commodity proces and sales, which would in real life reflect situation of fixed negotiated sales contracts with buyers (that means that in all simulated scenarios sales will amount to USD 650 mln)
b) assumption of correlation coefficient of 0.8 to the selected commodity, which would in real life reflect situation where some commodity price pass-through to buyers is possible – please note that simulated sales will not all the time be USD 650 mln (hint: consult literature about how to create correlated time-series ).