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An Approximate Wavelet MLE of Short- and Long-Memory Parameters
Jensen, Mark - In: Studies in Nonlinear Dynamics & Econometrics 3 (2007) 4, pp. 239-253
when transformed by wavelets.In this paper we generalize the long-memory parameter estimator of McCoy and Walden (1996) to … approximate an ARFIMA model's likelihood function with the series' wavelet coefficientsand their variances. Maximization of this … likelihood estimates of the ARFIMA model.By simultaneously maximizing the likelihood function over both the short and long …
Persistent link: https://www.econbiz.de/10005007688
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An Approximate Wavelet MLE of Short- and Long-Memory Parameters
Jensen, Mark - In: Studies in Nonlinear Dynamics & Econometrics 3 (1999) 4, pp. 239-253
when transformed by wavelets.In this paper we generalize the long-memory parameter estimator of McCoy and Walden (1996) to … approximate an ARFIMA model's likelihood function with the series' wavelet coefficientsand their variances. Maximization of this … likelihood estimates of the ARFIMA model.By simultaneously maximizing the likelihood function over both the short and long …
Persistent link: https://www.econbiz.de/10005046475
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An Approximate Wavelet MLE of Short and Long Memory Parameters
Jensen, Mark J. - EconWPA - 1998
wavelets. In this paper we generalize the long-memory parameter estimator of McCoy and Walden (1996) to simultaneously estaimte … approximate an ARFIMA models likelihood function with the series wavelet coefficients and their variances. Maximization of this … likelihood estimator of the ARFIMA model. By simultaneously maximizing the likelihood function over both the short and long …
Persistent link: https://www.econbiz.de/10005119098
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An Alternative Maximum Likelihood Estimator of Long-Memeory Processes Using Compactly Supported Wavelets
Jensen, Mark J. - EconWPA - 1997
In this paper we apply compactly supported wavelets to the ARFIMA(p,d,q) long-memory process to develop an alternative … of compactly supported wavelets, series length, and contamination by generating ARFIMA(p,d,q) processes for different …. In our simulations we find the wavelet MLE to be superior to the approximate MLE when estimating contaminated ARFIMA(0,d …
Persistent link: https://www.econbiz.de/10005407968
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Predicting BRICS stock returns using ARFIMA models
Aye, Goodness C.; Balcilar, Mehmet; Gupta, Rangan; … - In: Applied financial economics 24 (2014) 16/18, pp. 1159-1166
Persistent link: https://www.econbiz.de/10010418936
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Overlaying Time Scales in Financial Volatility Data
Hillebrand, Eric - EconWPA - 2005
Apart from the well-known, high persistence of daily financial volatility data, there is also a short correlation structure that reverts to the mean in less than a month. We find this short correlation time scale in six different daily financial time series and use it to improve the short-term...
Persistent link: https://www.econbiz.de/10005062571
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Testing for Long Memory in REIT Returns
Anoruo, Emmanuel; Braha, Habtu - In: International Real Estate Review 13 (2010) 3, pp. 261-281
This study examines the long memory properties of composite, equity, mortgage, and hybrid real estate investment trust (REIT) returns by using semi-parametric and wavelet estimators. In particular, this paper applies the GPH semi-parametric estimator, the Haar and the Daubechies wavelet...
Persistent link: https://www.econbiz.de/10008836556
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The estimation gain in the usage of fractional integration to forecast a vacancy houses level series
Amorin, Anderson Luis Walker; Souza, Adriano Mendonça de; … - In: The empirical economics letters : a monthly … 14 (2015) 1, pp. 25-32
Persistent link: https://www.econbiz.de/10011311313
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Modelling persistence in the conditional mean of inflation using the ARFIMA process with GARCH and GJR-GARCH innovations : the case of Ghana and South Africa
Boateng, Alexander; Lesaoana, Maseka; Siweya, Hlengani; … - In: African review of economics & finance : AREF : (a … 9 (2017) 2, pp. 96-130
Persistent link: https://www.econbiz.de/10011780438
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The economic importance of rare earth elements volatility forecasts
Proelss, Juliane; Schweizer, Denis; Seiler, Volker - In: International review of financial analysis 71 (2020), pp. 1-17
Persistent link: https://www.econbiz.de/10012435747