![The empirical risk-return relation: A factor analysis approach [An article from: Journal of Financial Economics]](https://www.ebooknetworking.net/books/noimage.jpg)
The empirical risk-return relation: A factor analysis approach [An article from: Journal of Financial Economics]
Price not available for India
You can still browse on Amazon. Try another country above.
Book Details
Author(s)
S.C. Ludvigson, S. Ng
Publisher
Elsevier
ISBN / ASIN
B000PC0QCU
ISBN-13
978B000PC0QC2
Marketplace
India 🇮🇳
Description
This digital document is a journal article from Journal of Financial Economics, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Existing empirical literature on the risk-return relation uses relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large data sets, to summarize a large amount of economic information by few estimated factors, and find that three new factors-termed ''volatility,'' ''risk premium,'' and ''real'' factors-contain important information about one-quarter-ahead excess returns and volatility not contained in commonly used predictor variables. Our specifications predict 16-20% of the one-quarter-ahead variation in excess stock market returns, and exhibit stable and statistically significant out-of-sample forecasting power. We also find a positive conditional risk-return correlation.
Description:
Existing empirical literature on the risk-return relation uses relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large data sets, to summarize a large amount of economic information by few estimated factors, and find that three new factors-termed ''volatility,'' ''risk premium,'' and ''real'' factors-contain important information about one-quarter-ahead excess returns and volatility not contained in commonly used predictor variables. Our specifications predict 16-20% of the one-quarter-ahead variation in excess stock market returns, and exhibit stable and statistically significant out-of-sample forecasting power. We also find a positive conditional risk-return correlation.
