The Divergence of High- and Low-Frequency Estimation: Implications for Performance Measurement

Posted: 21 May 2019

See all articles by William B. Kinlaw

William B. Kinlaw

State Street Global Markets

Mark Kritzman

Windham Capital Management

David Turkington

State Street Associates

Date Written: July 24, 2014


The Sharpe ratio is the most widely used metric for comparing performance across investment managers and strategies, and the information ratio is as commonly used to evaluate performance relative to a benchmark. Although it is widely recognized that non-linearities arising from the inclusion of options or the deployment of dynamic trading rules may distort these performance metrics, most analysts are unaware of another, perhaps more serious, source of distortion. Most analysts, either consciously or unthinkingly, assume that standard deviations scale with the square root of time and correlations are invariant to estimation intervals. These assumptions are not supported by evidence. Instead, non-zero lagged auto- and cross-correlations render these performance metrics highly sensitive to the return intervals used to estimate them. As a consequence, an investment manager who appears in the top quartile based on performance metrics estimated from monthly returns may appear in the bottom quartile within the same measurement period and universe based on the same performance metrics estimated from longer-horizon returns. Of particular note, the popular investment strategy known as risk parity, contrary to prior evidence, is shown to have significantly underperformed a 60/40 stock and bond portfolio when accounting for lagged auto- and cross-correlations. Finally, evidence suggests that high-frequency variability arises from changes in discount rates, whereas low-frequency variability is related to differences in cash flows.

Keywords: Auto-correlation, Cross-correlation, Excess dispersion, High-frequency estimation, Information ratio, Low-frequency estimation, Risk parity, Security market line, Sharpe ratio, Square root of time, Tracking error

JEL Classification: C10, C11, C13, C32, C50, C53, C61, G11, G12

Suggested Citation

Kinlaw, William B. and Kritzman, Mark and Turkington, David, The Divergence of High- and Low-Frequency Estimation: Implications for Performance Measurement (July 24, 2014). MIT Sloan Research Paper No. 5110-14,, Available at SSRN: or

William B. Kinlaw

State Street Global Markets ( email )

One Lincoln Street
Boston, MA 02111-2900
United States

Mark Kritzman (Contact Author)

Windham Capital Management ( email )

800 Boylston Street
30th Floor
Boston, MA 02199
United States
6174193900 (Phone)
6172365034 (Fax)

David Turkington

State Street Associates ( email )

United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics