 # 1.7.2 Example: Industrial Metals

###### 1.7.2 Example: Industrial Metals

Suppose today’s date is June 30, 2000. A US metals merchant has a portfolio of unsold physical positions in several industrial metals. We wish to calculate the portfolio’s 1-week 90% USDvalue-at-risk. Measure time t in weeks. Specify the random vector

[1.12] where accumulated values are in USD and reflect the value of a ton of metal accumulated from time 0 to time 1. Accumulated value might reflect price changes, cost of financing, warehousing, and insurance. For simplicity, we consider only price changes in this example.

Current values in USD/ton for the respective metals are

[1.13] The portfolio’s holdings are:

• 1000 tons of aluminum,
• 2000 tons of copper,
• 250 tons of nickel,
• 1000 tons of tin, and
• 100 tons of zinc,

which we represent with a row vector:

[1.14] The portfolio’s current value is

[1.15] Its future value is random:

[1.16] We call this relationship a portfolio mapping. We represent it schematically as

[1.17] Let 1|0σ and 1|0Σ be the standard deviation of and the covariance matrix of , both conditional on information available at time 0. Let’s apply [1.11]. By [1.16], is a linear polynomial of , so:2

[1.18] We know ω. We need 1|0Σ to obtain 1|0σ. Exhibit 1.4 indicates historical metals price data. Exhibit 1.4: Thirty weekly historical prices for the indicated metals. All prices are in USD per ton. Source: London Metals Exchange (LME).

Applying time-series methods described in Chapter 4, we construct

[1.19] Substituting [1.14] and [1.19] into [1.18], we conclude that has conditional standard deviation 1|0σ of USD 0.217MM.

Let denote the cumulative distribution function (CDF) of portfolio loss 1L conditional on information available at time 0. Its inverse provides quantiles of 1L. Our value-at-risk metric is 1-week 90% USDvalue-at-risk, so we seek the .90-quantile, 1| , of portfolio loss 1L.

We have no expression for . All we have is a conditional standard deviation 1|0σ for . We need additional assumptions or information. A simple solution is to assume that is conditionally normal with conditional mean 1|0μ = = 13.011MM. Since a normal distribution is fully specified by a mean and standard deviation, we have now specified a conditional CDF, , for .

The .90-quantile of portfolio loss is

[1.20] A property of normal distributions is that, as described in Section 3.10, a .10-quantile occurs 1.282 standard deviations below the mean, so

[1.21] Substituting [1.21] into [1.20]:

[1.22] The portfolio’s 1-week 90% USDvalue-at-risk is USD 0.278MM. Note that dropped out of the calculations entirely, so we did not actually need to calculate its value in [1.15].

###### Exercises
1.12

This exercise is based upon an equity example in Harry Markowitz’s (1959) book Portfolio Selection. Suppose today is January 1, 1955. Measure time t in years and define:

[1.23] Each accumulated value represents the value at time 1 of an investment worth 1 USD at time 0 in the indicated stock. Accumulated values include price changes and dividends. Consider a portfolio with holdings

[1.24] Based upon data provided by Markowitz, we construct a conditional covariance matrix 1|0Σ for :

[1.25] Calculate the portfolio’s 1-year 90% USDvalue-at-risk according to the following steps:

1. Value the vector 0s. (Hint: Based upon how the problem has been presented, the answer is trivial.)
2. Using the formula , value .
3. Specify a portfolio mapping that defines as a linear polynomial of .
4. Draw a schematic for your portfolio mapping.
5. Determine the conditional standard deviation 1|0σ of using [1.11].
6. Assume is normally distributed with conditional mean 1|0μ = and conditional standard deviation obtained in part (e). Calculate the .10-quantile of with the formula

[1.26] 7. Calculate the portfolio’s 1-year 90% USDvalue-at-risk as

[1.27] 