Long Memory of NSE Indices

GIS Business ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. 21-28
Author(s):  
Vijaya Kumar
Keyword(s):  
Key Words ◽  
Long Memory ◽  
Hurst Exponent ◽  
Stock Exchange ◽  
Fractional Dimension ◽  
Range Analysis ◽  
Temporal Dependence ◽  
Rescaled Range ◽  
Rescaled Range Analysis ◽  
Periodic Cycles

Long range memory in share indices show temporal dependence between observations spaced by long intervals of time and has distinct non-periodic cycles. This paper examines the presence of long memory of various indices of National Stock Exchange (NSE). The data consists of closing values of indices over different periods of time. The tests applied to examine long memory are Hurst exponent, Manderbolt-Hurst exponent, Lo’s rescaled-range analysis and Geweke and Porter-Hudak (GPH) test. The results of the estimated Hurst exponent, Manderbolt-Hurst exponent and GPH test show that invariably all NSE indices series have long memory. However, the results of Lo’s rescaled-range analysis indicate the absence of long memory for all indices. Key words: Long memory, rescaled range analysis, fractional dimension, Hurst exponent, GPH test, NSE indices

International Real Estate Review

2012 ◽  
Vol 15 (3) ◽  
pp. 283-305
Author(s):  
Sanjay Rajagopal ◽  
◽  
Patrick Hays ◽  
Keyword(s):  
Real Estate ◽  
Long Memory ◽  
Stock Exchange ◽  
Emerging Market ◽  
Real Estate Market ◽  
Policy Implications ◽  
Population Pressure ◽  
Range Analysis ◽  
Rescaled Range ◽  
Rescaled Range Analysis

Over the last decade, numerous factors including robust economic growth, population pressure, and the mounting need for office space among growth sectors such as information technology have placed significant upward pressure on Indian realty prices. The easing of government restrictions on foreign investments and venture capital into Indian real estate have provided an additional fillip to the real estate market in the country, and the confluence of such factors appears to have contributed to a speculative bubble in Indian real estate equities in the latter part of the decade. By using this bubble period as a case study, we test for the existence of long memory among real estate equities. For the January 2006-December 2008 period, we employ three self-affine fractal analysis techniques (classical rescaled range, roughness-length, and the variogram/structure function methods) to estimate the Hurst exponent, and find significant evidence of long memory in the Bombay Stock Exchange (BSE) Realty Index. Return persistence is further confirmed by the more powerful Lo¡¦s modified rescaled range analysis (MRSA), which is robust to short-term dependence. In addition to potential regulatory policy implications for this emerging market, our results have ramifications for modeling and forecasting returns, as well as for technical trading rules.

scholarly journals Examining the Impact of Structural Breaks on Long Memory of Stock Returns: Evidence from Bombay Stock Exchange of India Long Memory

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Anju Bala
Keyword(s):  
Stock Returns ◽  
Long Memory ◽  
Hurst Exponent ◽  
Structural Breaks ◽  
Stock Exchange ◽  
Subprime Crisis ◽  
Range Analysis ◽  
Rescaled Range Analysis ◽  
Daily Stock Returns ◽  
The Impact

This study examines the presence of long memory of Stock Returns in India with reference to structural breaks. The study used the Hurst Exponent in Rescaled Range Analysis as proposed by Lo (1991) to measure the presence of long memory on daily stock returns of the Bombay Stock Exchange Indices from January 2000 to December 2017. The analysis indicates that all indices show long memory effects. It is also evident that all indices exhibit long memory effect in the pre and post subprime crisis period. These findings are consistent with Bhattacharya and Bhattacharya (2018), Jha et al.(2018), Goudarzi (2010) and Lillo and Farmer (2004). KEYWORDS: Long Memory, Hurst exponent, Market Efficiency. Structural Breaks

Empirical Evidence of Hurst Exponent Estimation Wavelet Based

2014 ◽  
Vol 687-691 ◽  
pp. 1668-1671
Author(s):  
Bin Luo ◽  
Tong Zhou Zhao ◽  
De Hua Li ◽  
Dun Bo Cai
Keyword(s):  
Long Range ◽  
Hurst Exponent ◽  
Wavelet Spectrum ◽  
Long Range Dependence ◽  
Range Analysis ◽  
Data Set ◽  
Massive Data Set ◽  
Rescaled Range ◽  
Rescaled Range Analysis ◽  
Original Time

In this paper, we study long-range dependence of hydrological records with high frequent and massive data set. For detecting breakpoints, we apply the Evolutionary Wavelet Spectrum (EWS) to provide a segmentation of the original time series. And rescaled range analysis (R/S) for estimating the Hurst exponent that describe the long-range dependence phenomenon are used. The results affirm that the hydrological records have long-range dependent (LRD) behaviors.

scholarly journals Investing in mutual funds: are you paying for performance or for the ties of the manager?

2020 ◽  
pp. 153-164
Author(s):  
Costas Siriopoulos ◽  
Maria Skaperda
Keyword(s):  
Mutual Funds ◽  
Hurst Exponent ◽  
Surrogate Data ◽  
Active Management ◽  
Range Analysis ◽  
Management Fees ◽  
Time Period ◽  
Rescaled Range ◽  
Rescaled Range Analysis ◽  
Morning Star

This study analyses the performance of US Mutual Funds, from the perspective of Long Memory (LM), exploring if the returns of MFs are systematic due to their active management or they are random. The sample was 200 US equity MFs, from four categories, Large Cap, Middle Cap, Small Cap and World Stock, both 1- and 5-stars rating funds according to Morning Star rating. The time period was starting between 1981 and 2006 and ending 2016. Rescaled Range Analysis (R/S) employed for the Hurst exponent estimation, so to detect LM. Using Surrogate Data Analysis (SDA), the study was extended to Hurst exponent estimation for surrogate time series. The findings suggest that the selection of a MF presents a lot of complexity for investors. The 5-star MFs, with high qualified, and so expensive managers, tend to achieve random returns, while the returns of 1-star MFs, are more systematic. These MFs have higher fees than the 5-star MFs, but the management fees paid are quite inferior. This leads to the conclusion, that it might be preferable to pay for gaining an almost the same, but systematic return than to pay for the ties of the manager.

SUNSPOTS DATA ANALYSIS USING TIME SERIES

Fractals ◽  
2008 ◽  
Vol 16 (03) ◽  
pp. 259-265 ◽  
Author(s):  
YUSUF H. SHAIKH ◽  
A. R. KHAN ◽  
M. I. IQBAL ◽  
S. H. BEHERE ◽  
S. P. BAGARE
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Sunspot Cycle ◽  
Fractal Dimensions ◽  
Data Sets ◽  
Range Analysis ◽  
Data Set ◽  
Rescaled Range ◽  
Rescaled Range Analysis ◽  
Sunspot Data

The record of the sunspot number visible on the sun is regularly collected over the centuries by various observatories for studying the different factors influencing the sunspot cycle and solar activity. Sunspots appear in cycles, and last several years. These cycles follow a certain pattern which is well known. We analyzed monthly and yearly averages of sunspot data observed from year 1818 to 2002 using rescaled range analysis. The Hurst exponent calculated for monthly data sets are 0.8899, 0.8800 and 0.8597 and for yearly data set is 0.7187. Fractal dimensions1 calculated are 1.1100, 1.1200, 1.1403 and 1.2813. From the study of Hurst exponent and fractal dimension, we conclude that time series of sunspots show persistent behavior. The fundamental tool of signal processing is the fast Fourier transform technique (FFT). The sunspot data is also analyzed using FFT. The power spectrum of monthly and yearly averages of sunspot shows distinct peaks at 11 years confirming the well known 11-year cycle. The monthly sunspot data is also analyzed using FFT to filter the noise in the data.

scholarly journals A Study of the Returns Behavior of Small Capitalization REITs

2012 ◽  
Vol 4 (8) ◽  
pp. 457-466
Author(s):  
Sanjay Rajagopal
Keyword(s):  
Hurst Exponent ◽  
Roughness Length ◽  
Weak Form ◽  
Range Analysis ◽  
Real Estate Investment ◽  
Analysis Techniques ◽  
Rescaled Range ◽  
Rescaled Range Analysis ◽  
Daily Returns ◽  
Weak Form Efficiency

We analyze the daily returns on 63 real estate investment trusts (REITs) that comprise five US Small Cap REIT indices, and test for weak-form efficiency by estimating the Hurst exponent and fractal dimension. Fourteen of the 63 firms (or roughly 22% of the firms studied) fail to exhibit weak-form efficiency, based on Classical Rescaled Range Analysis. Two additional self-affine fractal analysis techniques (Roughness-Length and Variogram analyses) provide some support for this finding. In particular, it is found that a majority of the series for which weak-form efficiency is rejected are anti-persistent, with estimated Hurst exponents below 0.50.These results are further confirmed by Lo’s (1991) modified rescaled range analysis, which reveals significant memory at long lags. Overall, the results suggest inefficient pricing for a significant subset of REITs, with important implications for trading and for the modeling of REIT returns. .Some aspects of their returns behavior warrant further study.

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