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Quantitative Finance · शब्दावली

HAC estimator क्या है?

Definition 11.12 Quantitative Methods · अध्याय 11 — Estimation

A heteroskedasticity- and autocorrelation-consistent (HAC estimator) estimates a long-run variance by weighting sample autocovariances; the Newey–West estimator $\hat\gamma(0) + 2\sum_{k=1}^L(1 - \frac k{L+1})\hat\gamma(k)$ uses Bartlett weights, which keep it nonnegative, with a bandwidth $L$ growing slowly with $n$ (a common rule is $L = \lfloor4(n/100)^{2/9}\rfloor$ ).

The researcher’s five years of daily P&L. Left: sample autocorrelations and the theoretical 1 - k/5 of five overlapping positions. Right: the Newey–West standard error of the mean P&L against the number of lags: 1.19 bp with none (the iid formula), 2.32 at the rule’s seven lags, against a true 2.65. Data: the chapter’s tutorial, seeded. — Figure 11.2. The researcher’s five years of daily P&L. Left: sample autocorrelations and the theoretical $1 - k/5$ of five overlapping positions. Right: the Newey–West standard error of the mean P&L against the number of lags: 1.19 bp with none (the iid formula), 2.32 at the rule’s seven lags, against a true 2.65. Data: the chapter’s tutorial, seeded.

Coverage of nominal 95% confidence intervals for the mean daily P&L of the overlapping strategy, over 2 000 simulated five-year histories: the iid standard error covers 62.5% of the time, Newey–West 92–94%. Data: the chapter’s tutorial, seeded. — Figure 11.3. Coverage of nominal 95% confidence intervals for the mean daily P&L of the overlapping strategy, over 2 000 simulated five-year histories: the iid standard error covers 62.5% of the time, Newey–West 92–94%. Data: the chapter’s tutorial, seeded.

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