Scaling Analysis of National Stock Exchange Index (original) (raw)
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Scaling Analysis of Prime Stock Exchange Indices of the Emerging Seven (E7) Countries
2012
The emerging seven (E7) countries represent the seven major emerging economies that are predicted to have much larger economies than the G7 countries by 2020. The term was coined by the PricewaterhouseCoopers in the Stern Review report, which was published on 30 October 2006. These seven countries are People's Republic of China, India, Brazil, Mexico, Russia, Indonesia and Turkey. In the present work we have studied daily data of prime stock exchanges of these E7 countries in order to study their scaling behaviours. Here we have used Finite Variance Scaling Method which is a form of scaling in order to find the corresponding Hurst Exponents. Hurst exponent is a measurement of the trend of the time series. It determines whether a time series is persistent (long memory process) or anti-persistent (short memory process) or random. Interestingly it is seen that some stock exchange data are persistent and some are anti-persistent. We have also studied the autocorrelation coefficients with different lags for all the present indices and study reveals that some of the indices are governed by autoregressive method, some of them by moving average method and some have mixed influence of both these two methods.
2012
The purpose of the present work was to study the variability in scaling behavior as well as in trend pattern of prime Indian stock exchanges, namely Bombay Stock Exchange (BSE) and National Stock Exchange (NSE) at different phases of time during last decade. For the present analysis we considered these two financial time series in the backward direction within the period from 31st December, 2010 to 1st January, 2001. Hurst Exponents were used to measure degree of persistency and autocorrelation graphs were used to predict the trend pattern of the time series. Interestingly we found that at most of the phases the data was persistent and in a few number of phases anti-persistent. Moreover the measure of persistency or anti-persistency also changed with time significantly. On the other hand, it was seen that at most of the phases autocorrelation graphs showed autoregressive behaviour and at a few instances moving average behaviour. Interestingly, the autocorrelation graphs for BSE and NSE data showed almost same trend patterns at similar phases.
Using the Scaling Analysis to Characterize Financial Markets
Arxiv preprint cond-mat/0302434, 2003
We empirically analyze the scaling properties of daily Foreign Exchange rates, Stock Market indices and Bond futures across different financial markets. We study the scaling behaviour of the time series by using a generalized Hurst exponent approach. We verify the robustness of this approach and we compare the results with the scaling properties in the frequency-domain. We find evidence of deviations from the pure Brownian motion behavior. We show that these deviations are associated with characteristics of the specific markets and they can be, therefore, used to distinguish the different degrees of development of the markets.
The European Physical Journal B, 2014
In this paper we have analyzed scaling properties of time series of stock market indices (SMIs) of developing economies of Western Balkans, and have compared the results we have obtained with the results from more developed economies. We have used three different techniques of data analysis to obtain and verify our findings: Detrended Fluctuation Analysis (DFA) method, Detrended Moving Average (DMA) method, and Wavelet Transformation (WT) analysis. We have found scaling behavior in all SMI data sets that we have analyzed. The scaling of our SMI series changes from long-range correlated to slightly anti-correlated behavior with the change in growth or maturity of the economy the stock market is embedded in. We also report the presence of effects of potential periodic-like influences on the SMI data that we have analyzed. One such influence is visible in all our SMI series, and appears at a period T p ≈ 90 days. We propose that the existence of various periodic-like influences on SMI data may partially explain the observed difference in types of correlated behavior of corresponding scaling functions.
Scaling and Correlation in Financial Time Series
We discuss the results of three recent phenomenological studies focussed on understanding the distinctive statistical properties of ÿnancial time series -(i) The probability distribution of stock price uctuations: Stock price uctuations occur in all magnitudes, in analogy to earthquakesfrom tiny uctuations to very drastic events, such as the crash of 19 October 1987, sometimes referred to as "Black Monday". The distribution of price uctuations decays with a power-law tail well outside the LÃ evy stable regime and describes uctuations that di er by as much as 8 orders of magnitude. In addition, this distribution preserves its functional form for uctuations on time scales that di er by 3 orders of magnitude, from 1 min up to approximately 10 days. (ii) Correlations in ÿnancial time series: While price uctuations themselves have rapidly decaying correlations, the magnitude of uctuations measured by either the absolute value or the square of the price uctuations has correlations that decay as a power-law, persisting for several months. (iii) Volatility and trading activity: We quantify the relation between trading activitymeasured by the number of transactions N t -and the price change G t for a given stock, over a time interval [t; t + t]. We ÿnd that N t displays long-range power-law correlations in time, which leads to the interpretation that the long-range correlations previously found for |G t | are connected to those of N t .
Origins of the scaling behaviour in the dynamics of financial data
Physica A: Statistical Mechanics and its Applications, 1999
The conditionally exponential decay (CED) model is used to explain the scaling laws observed in ÿnancial data. This approach enables us to identify the distributions of currency exchange rate or economic indices returns (changes) corresponding to the empirical scaling laws. This is illustrated for daily returns of the Dow Jones industrial average (DJIA) and the Standard & Poor's 500 (S&P500) indices as well as for high-frequency returns of the USD=DEM exchange rate.
Time and scale Hurst exponent analysis for financial markets
Physica A-statistical Mechanics and Its Applications, 2008
We use a new method of studying the Hurst exponent with time and scale dependency. This new approach allows us to recover the major events affecting worldwide markets (such as the September 11th terrorist attack) and analyze the way those effects propagate through the different scales. The time-scale dependence of the referred measures demonstrates the relevance of entropy measures in distinguishing the several characteristics of market indices: "effects" include early awareness, patterns of evolution as well as comparative behaviour distinctions in emergent/established markets.
Study of scaling behavior of NIFTY using detrended fluctuation analysis
phys.sinica.edu.tw
Detrended fluctuation analysis has been proved to be a useful method in the analysis of nonstationary time series data. Since the changes in the stock market indices are nonstationary, hence DFA method is more suitable than R/S method. In this paper we study National Stock Exchange (NSE) index for fractal behavior and calculated scaling exponents for different time intervals.
2019
Different investment strategies are adopted in short-term and long-term depending on the time scales, even though time scales are adhoc in nature. Empirical mode decomposition based Hurst exponent analysis and variance technique have been applied to identify the time scales for short-term and long-term investment from the decomposed intrinsic mode functions(IMF). Hurst exponent ($H$) is around 0.5 for the IMFs with time scales from few days to 3 months, and Hgeq0.75H\geq0.75Hgeq0.75 for the IMFs with the time scales geq5\geq5geq5 months. Short term time series [$X_{ST}(t)$] with time scales from few days to 3 months and H0˜.5H~0.5H0˜.5 and long term time series [$X_{LT}(t)$] with time scales geq5\geq5geq5 and Hgeq0.75H\geq0.75Hgeq0.75, which represent the dynamics of the market, are constructed from the IMFs. The XST(t)X_{ST}(t)XST(t) and XLT(t)X_{LT}(t)XLT(t) show that the market is random in short-term and correlated in long term. The study also show that the XLT(t)X_{LT}(t)XLT(t) is correlated with fundamentals of the company. The analysis will be useful f...
Journal of Banking & Finance, 2005
The scaling properties encompass in a simple analysis many of the volatility characteristics of financial markets. That is why we use them to probe the different degree of markets development. We empirically study the scaling properties of daily Foreign Exchange rates, Stock Market indices and fixed income instruments by using the generalized Hurst approach. We show that the scaling exponents are associated with characteristics of the specific markets and can be used to differentiate markets in their stage of development. The robustness of the results is tested by both Monte-Carlo studies and a computation of the scaling in the frequency-domain.