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 Exchange

Mark Kritzman

Massachusetts Institute of Technology (MIT) - Sloan School of Management

David Turkington

State Street Associates

Date Written: July 24, 2014

Abstract

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: https://ssrn.com/abstract=2471396 or http://dx.doi.org/10.2139/ssrn.2471396

William B. Kinlaw

State Street Global Exchange ( email )

One Lincoln Street
Boston, MA 02111-2900
United States

Mark Kritzman (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-416
Cambridge, MA 02142
United States

David Turkington

State Street Associates ( email )

United States

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