Frequency Space Correlation Between REITs and Capital Market Indices

Several studies have examined real estate investment trust (REIT) co-movement with stocks or bonds using traditional time domain based methods, such as linear regression or correlation. Results of these studies have produced inconsistent statistical model parameters. The erratic behavior of the models may have resulted from the different time periods in the studies, the REITs included in a study or the market indices. Another factor contributing to the variation of the models comes from the compression of cyclical information over a study?s time period by time domain based techniques. Cross-spectral analysis provides a frequency space method of examining the coherency (i.e., frequency space correlation) between two time series across all frequencies. This article contains an examination of the coherency between REITs and stock market indices and REITs and U.S. Treasury debt indices for the period 1989-95. Results of the coherency spectra show significant co-movement between REITs and stock market indices, while debt instruments show very few frequencies with significant coherency. Furthermore, phase spectra provide evidence of contemporaneous movement between REITs and stock indices at all frequencies.

Window Functions and Their Applications in Signal Processing

Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture rada

Window Functions and Their Applications in Signal Processing

Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture rada

A 6-12 GHz Analogue Lag-Correlator for Radio Interferometry

Aims: We describe a 6-12 GHz analogue correlator that has been developed for use in radio interferometers. Methods: We use a lag-correlator technique to synthesis eight complex spectral channels. Two schemes were considered for sampling the cross-correlation function, using either real or complex correlations, and we developed prototypes for both of them. We opted for the ``add and square'' detection scheme using Schottky diodes over the more commonly used active multipliers because the stability of the device is less critical. Results: We encountered an unexpected problem, in that there were errors in the lag spacings of up to ten percent of the unit spacing. To overcome this, we developed a calibration method using astronomical sources which corrects the effects of the non-uniform sampling as well as gain error and dispersion in the correlator.Comment: 14 pages, 21 figures, accepted for publication in A&

Zolotarev polynomials utilization in spectral analysis

Tato práce je zaměřena na vybrané problémy Zolotarevových polynomů a jejich vyuľití ke spektrální analýze. Pokud jde o Zolotarevovy polynomy, jsou popsány základní vlastnosti symetrických Zolotarevových polynomů včetně ortogonality. Rovněľ se provádí prozkoumání numerických vlastností algoritmů generujících dokonce Zolotarevovy polynomy. Pokud jde o aplikaci Zolotarevových polynomů na spektrální analýzu, je implementována aproximovaná diskrétní Zolotarevova transformace, která umoľňuje výpočet spektrogramu (zologramu) v reálném čase. Aproximovaná diskrétní zolotarevská transformace je navíc upravena tak, aby lépe fungovala při analýze tlumených exponenciálních signálů. A nakonec je navrľena nová diskrétní Zolotarevova transformace implementovaná plně v časové oblasti. Tato transformace také ukazuje, ľe některé rysy pozorované u aproximované diskrétní Zolotarevovy transformace jsou důsledkem pouľití Zolotarevových polynomů.This thesis is focused on selected problems of symmetrical Zolotarev polynomials and their use in spectral analysis. Basic properties of symmetrical Zolotarev polynomials including orthogonality are described. Also, the exploration of numerical properties of algorithms generating even Zolotarev polynomials is performed. As regards to the application of Zolotarev polynomials to spectral analysis the Approximated Discrete Zolotarev Transform is implemented so that it enables computing of zologram in real–time. Moreover, the Approximated Discrete Zolotarev Transform is modified to perform better in the analysis of damped exponential signals. And finally, a novel Discrete Zolotarev Transform implemented fully in the time domain is suggested. This transform also shows that some features observed using the Approximated Discrete Zolotarev Transform are a consequence of using Zolotarev polynomials

A 6-12 GHz Analogue Lag-Correlator for Radio Interferometry

Aims: We describe a 6-12 GHz analogue correlator that has been developed for use in radio interferometers. Methods: We use a lag-correlator technique to synthesis eight complex spectral channels. Two schemes were considered for sampling the cross-correlation function, using either real or complex correlations, and we developed prototypes for both of them. We opted for the ``add and square'' detection scheme using Schottky diodes over the more commonly used active multipliers because the stability of the device is less critical. Results: We encountered an unexpected problem, in that there were errors in the lag spacings of up to ten percent of the unit spacing. To overcome this, we developed a calibration method using astronomical sources which corrects the effects of the non-uniform sampling as well as gain error and dispersion in the correlator.Comment: 14 pages, 21 figures, accepted for publication in A&

Estimation of the long memory parameter in non stationary models: A Simulation Study

In this paper we perform a Monte Carlo study based on three well-known semiparametric estimates for the long memory fractional parameter. We study the efficiency of Geweke and Porter-Hudak, Gaussian semiparametric and wavelet Ordinary Least-Square estimates in both stationary and non stationary models. We consider an adequate data tapers to compute non stationary estimates. The Monte Carlo simulation study is based on different sample size. We show that for d belonging to [1/4,1.25) the Haar estimate performs the others with respect to the mean squared error. The estimation methods are applied to energy data set for an empirical illustration.wavelets; long memory; tapering; non-stationarity; volatility.