Feb

23

Active Trader Magazine, March 2004
The Volatility-Market Connection
By Victor Niederhoffer and Laurel Kenner

Is everything you know about volatility wrong? Find out what history says about the volatility-market relationship — and what the VIX is saying about the stock market’s 2004 prospects.

Volatility is a crucial variable every market participant needs to consider. For speculators, volatility determines how much money to place on each trade relative to initial stake and stop point. For investors, it determines how much to allocate between stocks and bonds, and how much to invest for a secure retirement. For academics, volatility is one blade of the scissors in the fundamental theorem of finance — namely, that expected return is linearly related to volatility.

The article contains a sidebar that begins, "Dr. Hui Guo is one of the most respected and prolific authors in volatility research. Reading some of his articles sparked our quest." Dr. Guo was a senior research economist at the Federal Reserve Bank of St. Louis is currently at U Cincinnati.

Here is one of Dr. Guo's recent research papers:

Taylor Rule Monetary Policy and Equity Market Risk Premia
Hui Guo, University of Cincinnati - Department of Finance - Real Estate
Saidat Sanni, University of Cincinnati
Yan Yu, University of Cincinnati - Lindner College of Business
Posted: 12 Nov 2024, Last revised: 6 Nov 2024

The Fed mainly uses the federal funds rate (FFR) to achieve its dual mandate of price stability and maximum employment. Recent asset pricing models argue that changes in FFR affect equity market risk premia. Consistent with this financial condition channel of monetary transmission, the Fed's macroeconomic needs estimated using the Taylor (1993) rule negatively predict stock market returns. They are also identified as a crucial equity premium determinant along with the scaled market price and conditional market variance via variable selection analyses. The linear multifactor equity premium model has remarkably stable predictive power, outperforming machine learning and other prediction techniques.


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