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"Frontmatter". In: Analysis of Financial Time Series

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LONG-MEMORY MODELS 73=(k − d − 1)!k!(−d − 1)! .3. For −0.5 < d < 0.5, the ACF <strong>of</strong> x t isρ k =<strong>In</strong> particular, ρ 1 = d/(1 − d) andd(1 + d) ···(k − 1 + d), k = 1, 2,....(1 − d)(2 − d) ···(k − d)ρ k ≈(−d)!(d − 1)! k2d−1 , as k →∞.4. For −0.5 < d < 0.5, the PACF <strong>of</strong> x t is φ k,k = d/(k − d) for k = 1, 2,....5. For −0.5 < d < 0.5, the spectral density function f (ω) <strong>of</strong> x t , which is theFourier transform <strong>of</strong> the ACF <strong>of</strong> x t , satisfieswhere ω ∈[0, 2π] denotes the frequency.f (ω) ∼ ω −2d , as ω → 0, (2.45)Of particular interest here is the behavior <strong>of</strong> ACF <strong>of</strong> x t when d < 0.5. The propertysays that ρ k ∼ k 2d−1 , which decays at a polynomial, instead <strong>of</strong> exponential, rate. Forthis reason, such an x t process is called a long-memory time series. A special characteristic<strong>of</strong> the spectral density function in Eq. (2.45) is that the spectrum divergesto infinity as ω → 0. However, the spectral density function <strong>of</strong> a stationary ARMAprocess is bounded for all ω ∈[0, 2π].Earlier we used the binomial theorem for noninteger powers(1 − B) d =∞∑( )(−1) k dB k ,kk=0( d=k)d(d − 1) ···(d − k + 1).k!If the fractionally differenced series (1− B) d x t follows an ARMA(p, q) model, thenx t is called an ARFIMA(p, d, q) process, which is a generalized ARIMA model byallowing for noninteger d.<strong>In</strong> practice, if the sample ACF <strong>of</strong> a time series is not large in magnitude, but decaysslowly, then the series may have long memory. As an illustration, Figure 2.17 showsthe sample ACFs <strong>of</strong> the absolute series <strong>of</strong> daily simple returns for the CRSP valueandequal-weighted indexes from July 3, 1962 to December 31, 1997. The ACFs arerelatively small in magnitude, but decay very slowly; they appear to be significantat the 5% level even after 300 lags. For more information about the behavior <strong>of</strong>sample ACF <strong>of</strong> absolute return series, see Ding, Granger, and Engle (1993). For thepure fractionally differenced model in Eq. (2.44), one can estimate d using either amaximum likelihood method or a regression method with logged periodogram at the

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