The Divergence of High- and Low-Frequency Estimation: Implications for Performance Measurement
研究发现，由于滞后自相关和交叉相关，基于不同频率收益率估计的夏普比率等业绩指标可能得出相反结论，例如月度数据中的顶级经理在低频数据中可能垫底，风险平价策略也因考虑这些相关性而表现不佳。
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 reported in the article suggest that high-frequency variability arises from changes in discount rates, whereas low-frequency variability is related to differences in cash flows. <b>TOPICS:</b>Portfolio theory, performance measurement