schwartz97futures-data: Daily futures prices
In schwartz97: A package on the Schwartz two-factor commodity model

Description Usage Format Details Author(s) See Also Examples

Futures prices, time to maturity, open interest, volume, underlying tickers, and last trade date of ten different commoditites: corn, wheat, soybean, soybean meal, soybean oil, lumber, live cattle, coffee, heating oil, copper.

There are, depending on the liquidity of the commodity, between 4 and 10 ‘clean’ closest to maturity futures price series.

1	data(futures)

A list containing ten commodities as lists: “corn”, “wheat”, “soybean”, “soybean.meal”, “soybean.oil”, “lumber”, “live.cattle”, “coffee”, “heating.oil”, “copper”.

Each list contains six dimnamed matrices:

price: Daily futures prices.
ttm: The time to maturity of the futures contracts in units of days (see Details.)
oi: Open interest.
vol: Volume.
underl.tickers: Underlying tickers / contracts.
last.trade.dt: Last trade date as character in the ISO 8601 international standard format.

The i-th column of each matrix contains data for the i-th closest to maturity contract. The i-th column name is the ticker of the i-th generic futures.

Commodity	# Contracts	Exchange	Start date	End date
Corn	6	CBOT	1997-01-02	2010-04-07
Wheat	5	CBOT	1995-01-03	2010-04-07
Soybean	7	CBOT	1995-01-03	2010-04-07
Soybean meal	6	CBOT	2000-01-03	2010-04-07
Soybean oil	6	CBOT	1995-01-03	2010-04-07
Lumber	4	CME	1995-01-03	2010-04-07
Live cattle	6	CME	2004-07-01	2010-04-07
Coffee	5	ICE	1995-01-03	2010-04-07
Heating oil	10	NYMEX	1995-01-03	2010-03-31
Copper	8	COMEX	1996-01-02	2010-02-24

The elements of price and ttm have the following interpretation: price[i,j] denotes the futures price whose time to maturity was ttm[i,j] days when it was observed.

Philipp Erb, David Luethi, Juri Hinz

futuresplot, fit.schwartz2f.

data(futures)

## Plot forward curves of lumber
futuresplot(futures$lumber, type = "forward.curve")

## Plot time to maturity of heating oil data
futuresplot(futures$heating.oil, type = "ttm")

## Make 'futures' weekly, take Wednesday data
futures.w <- rapply(futures, function(x)x[format(as.Date(rownames(x)), "%w") == 3,],
                    classes = "matrix", how = "list")

## Make 'futures' monthly, take the 28th day of the month
futures.m <- rapply(futures, function(x)x[format(as.Date(rownames(x)), "%d") == 28,],
                    classes = "matrix", how = "list") 

## Plot weekly lumber and monthly soybean data
futuresplot(futures.w$lumber, type = "forward.curve", main = "Lumber") 
futuresplot(futures.m$soybean, type = "forward.curve", main = "Soybean")

## Not run: 
## Convert to zoo-objects:
require(zoo)
futures.zoo <- rapply(futures, function(x)zoo(x, as.Date(rownames(x))),
                      classes = "matrix", how = "list")

## ...and plot it nicely using plot.zoo:
plot(futures.zoo$heating.oil$ttm)
plot(futures.zoo$wheat$vol)
plot(futures.zoo$copper$oi)

## End(Not run)

## Estimate soybean meal parameters (stop after 100 iterations).
## ttm (time-to-maturity) is divided by 260 as it is in unit of days and
## deltat = 1/52 because weekly price observations are used.
soybean.meal.fit <- fit.schwartz2f(data = futures.w$soybean.meal$price,
                                   ttm = futures.w$soybean.meal$ttm / 260,
                                   deltat = 1 / 52, r = 0.04,
                                   control = list(maxit = 100))
soybean.meal.fit