monomvn (version 1.9-7)

returns: Financial Returns data from NYSE and AMEX

Description

Monthly returns of common domestic stocks traded on the NYSE and the AMEX from April 1968 until 1998; also contains the return to the market

Usage

data(returns)
data(returns.test)
data(market)
data(market.test)

Format

The returns provided are collected in a data.frame with 1168 columns, and 360 rows in the case of returns and 12 rows for returns.test. The columns are uniquely coded to identify the stock traded on NYSE or AMEX. The market return is in two vectors market and market.test of length 360 and 12, respectively

Details

The columns contain monthly returns of common domestic stocks traded on the NYSE and the AMEX from April 1968 until 1998. returns contains returns up until 1997, whereas returns.test has the returns for 1997. Both data sets have been cleaned in the following way. All stocks have a share price greater than \$5 and a market capitalization greater than 20% based on the size distribution of NYSE firms. Stocks without completely observed return series in 1997 were also discarded.

The market returns provided are essentially the monthly return on the S\&P500 during the same period, which is highly correlated with the raw monthly returns weighted by their market capitalization

References

Louis K. Chan, Jason Karceski, and Josef Lakonishok (1999). On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model. The Review of Financial Studies. 12(5), 937-974

Ravi Jagannathan and Tongshu Ma (2003). Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps. Journal of Finance, American Finance Association. 58(4), 1641-1684

Robert B. Gramacy, Joo Hee Lee, and Ricardo Silva (2008). On estimating covariances between many assets with histories of highly variable length. Preprint available on arXiv:0710.5837: http://arxiv.org/abs/0710.5837

http://bobby.gramacy.com/r_packages/monomvn

See Also

monomvn, bmonomvn

Examples

# NOT RUN {
data(returns)

## investigate the monotone missingness pattern
returns.na <- is.na(returns)
image(1:ncol(returns), 1:nrow(returns), t(returns.na))

## for a portfolio balancing exercise, see
## the example in the bmonomvn help file
# }