Scaling behaviors in differently developed markets (original) (raw)
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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.
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.
Common Scaling Behavior in Finance and Macroeconomics
In order to test whether scaling exists in finance at the world level, we test whether the average growth rates and volatility of market capitalization (MC) depend on the level of MC. We analyze the MC for 54 worldwide stock indices and 48 worldwide bond indices. We find that (i) the average growth rate r of the MC and (ii) the standard deviation σ(r) of growth rates r decrease both with MC as power laws, with exponents αw = 0.28 ± 0.09 and βw = 0.12 ± 0.04. We define a stochastic process in order to model the scaling results we find for worldwide stock and bond indices. We establish a power-law relationship between the MC of a country's financial market and the gross domestic product (GDP) of the same country.
Scaling and Multi-scaling in Financial Markets
SSRN Electronic Journal, 2000
This paper reviews some of the phenomenological models which have been introduced to incorporate the scaling properties of financial data. It also illustrates a microscopic model, based on heterogeneous interacting agents, which provides a possible explanation for the complex dynamics of markets' returns. Scaling and multi-scaling analysis performed on the simulated data is in good quantitative agreement with the empirical results.
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.
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.
Modeling the Non-Markovian, Non-stationary Scaling Dynamics of Financial Markets
New Economic Windows, 2011
3 bovina@pd.infn.it 4 camana@pd.infn.it 5 stella@pd.infn.it † Summary. A central problem of Quantitative Finance is that of formulating a probabilistic model of the time evolution of asset prices allowing reliable predictions on their future volatility. As in several natural phenomena, the predictions of such a model must be compared with the data of a single process realization in our records. In order to give statistical significance to such a comparison, assumptions of stationarity for some quantities extracted from the single historical time series, like the distribution of the returns over a given time interval, cannot be avoided. Such assumptions entail the risk of masking or misrepresenting non-stationarities of the underlying process, and of giving an incorrect account of its correlations. Here we overcome this difficulty by showing that five years of daily Euro/US-Dollar trading records in the about three hours following the New York market opening, provide a rich enough ensemble of histories. The statistics of this ensemble allows to propose and test an adequate model of the stochastic process driving the exchange rate. This turns out to be a non-Markovian, self-similar process with non-stationary returns. The empirical ensemble correlators are in agreement with the predictions of this model, which is constructed on the basis of the time-inhomogeneous, anomalous scaling obeyed by the return distribution. †
Scaling Analysis of National Stock Exchange Index
The scaling behaviour of the daily national stock exchange index data during the time period from July, 1990 to July, 2008 is analyzed in the present paper. This data is here processed through Finite Variance Scaling Method which is a form of scaling in order to find the corresponding Hurst exponent. The study reveals that the present data of daily national stock exchange index exhibits a persistent trend (long memory process).
On the statistics of scaling exponents and the multiscaling value at risk
The European Journal of Finance, 2021
Scaling and multiscaling financial time series have been widely studied in the literature. The research on this topic is vast and still flourishing. One way to analyse the scaling properties of time series is through the estimation of scaling exponents. These exponents are recognized as being valuable measures to discriminate between random, persistent, and anti-persistent behaviours in time series. In the literature, several methods have been proposed to study the multiscaling property and in this paper we use the generalized Hurst exponent (GHE). On the base of this methodology, we propose a novel statistical procedure to robustly estimate and test the multiscaling property and we name it RNSGHE. This methodology, together with a combination of t-tests and F-tests to discriminated between real and spurious scaling. Moreover, we also introduce a new methodology to estimate the optimal aggregation time used in our methodology. We numerically validate our procedure on simulated time series using the Multifractal Random Walk (MRW) and then apply it to real financial data. We also present results for times series with and without anomalies and we compute the bias that such anomalies introduce in the measurement of the scaling exponents. Finally, we show how the use of proper scaling and multiscaling can ameliorate the estimation of risk measures such as Value at Risk (VaR). We also propose a methodology based on Monte Carlo