Time Evolution of Market Efficiency and Multifractality of the Japanese Stock Market (original) (raw)

A multifractal approach for stock market inefficiency

Physica A-statistical Mechanics and Its Applications, 2008

In this paper, the multifractality degree in a collection of developed and emerging stock market indices is evaluated. Empirical results suggest that the multifractality degree can be used as a quantifier to characterize the stage of market development of world stock indices. We develop a model to test the relationship between the stage of market development and the multifractality degree and find robust evidence that the relationship is negative, i.e., higher multifractality is associated with a less developed market. Thus, an inefficiency ranking can be derived from multifractal analysis. Finally, a link with previous volatility time series results is established.

Understanding the source of multifractality in financial markets

2012

In this paper, we use the generalized Hurst exponent approach to study the multi- scaling behavior of different financial time series. We show that this approach is robust and powerful in detecting different types of multiscaling. We observe a puzzling phenomenon where an apparent increase in multifractality is measured in time series generated from shuffled returns, where all time-correlations are destroyed, while the return distributions are conserved. This effect is robust and it is reproduced in several real financial data including stock market indices, exchange rates and interest rates. In order to understand the origin of this effect we investigate different simulated time series by means of the Markov switching multifractal (MSM) model, autoregressive fractionally integrated moving average (ARFIMA) processes with stable innovations, fractional Brownian motion and Levy flights. Overall we conclude that the multifractality observed in financial time series is mainly a consequence of the characteristic fat-tailed distribution of the returns and time-correlations have the effect to decrease the measured multifractality.

Multifractality of the Istanbul and Moscow Stock Market Returns

2003

There is a growing awareness among financial researchers that the traditional models of asset returns cannot capture essential time series properties of the current stock return data. We examine commonly used models, such as the autoregressive integrated moving average (ARIMA) and the autoregressive conditional heteroskedasticity (ARCH) family, and show that these models cannot account for the essential characteristics of the real Istanbul Stock Exchange and Moscow Stock Exchange returns. These models often fail, and when they succeed, they do at the cost of an increasing number of parameters and structural equations. The measures of risk obtained from these models do not reflect the true risk to traders, since they cannot capture all key features of the data. In this paper, we offer an alternative framework of analysis based on multifractal models. Compared to the traditional models, the multifractal models we use are very parsimonious and replicate all key features of the data with only three universal parameters. The multifractal models have superior risk evaluation performance. They also produce better forecasts at all scales. The paper also offers a justification of the multifractal models for financial modeling. is an assistant professor of econometrics at Çukurova University, Adana, Turkey (on leave) and Manas University, Bishkek, Kyrgyzstan. The author thanks the referee for invaluable comments that made improvements possible.

The Changing Market Efficiency of Tokyo Stock Exchange (Nikkei)

International Journal of Financial Management, 2023

A stock market is known as a secondary market which plays the role of buying and selling stocks and securities. The efficiency of the market depends upon how quickly the market assimilates new information. The market consists mainly of three types of market- weak form, semi-strong form, and strong form. The weak form of market efficiency indicates that all the previous market prices and information are fully displayed in stock prices in the semi-strong form of the market. The stock prices reflect all the publicly available information. In the case of strong form, the market is said to be efficient when the stock prices display all the information, where insider information is of no use. Any new information that helps to alter the prospect of the organisation’s potential profitability must instantly be displayed in the stock prices without delay. The Tokyo Stock Exchange (NIKKEI) began on 9th July 1950. The Tokyo Stock Exchange (NIKKEI) measures the performance of 225 large, publicly owned companies in Japan from a wide array of industry sectors. It is a price-weighted index operating in Japanese Yen (JP¥). The purpose of this paper is to test the market efficiency of the Tokyo Stock Exchange (NIKKEI) by using the daily time series data from the period 1st April 2010 to 31st March 2020. The study applied various statistical tools and techniques, including run tests, unit root tests, and VR tests. The study examines the market efficiency of the Tokyo Stock Exchange (NIKKEI) by considering the daily closing index prices and also observed that the null hypothesis of the daily returns of the indices is rejected and accepted.

Financial multifractality and its subtleties: an example of DAX

Physica A: Statistical Mechanics and its Applications, 2002

Detailed study of multifractal characteristics of the financial time series of asset values and of its returns is performed using a collection of the high frequency Deutsche Aktienindex data. The tail index (α), the Renyi exponents based on the box counting algorithm for the graph (d q) and the generalized Hurst exponents (H q) are computed in parallel for short and daily return times. The results indicate a more complicated nature of the stock market dynamics than just consistent multifractal.

Comparing multifractality among Czech, Hungarian and Russian stock exchanges

International Journal of Applied Decision Sciences, 2013

In this paper, we analyse multifractality among Czech, Hungarian and Russian stock exchanges. For this end we perform a method titled multifractal detrended fluctuation analysis (MF-DFA) to investigate the multifractal properties of PX, BUX and RTS indices. By applying the MF-DFA method we first calculate the generalised Hurst exponents, we then deduce the Rényi exponents as well as the singularity spectrum of these indices. Furthermore, we perform shuffling and surrogate techniques to detect the sources of multifractality. We also compute the contribution of two major sources of multifractality that are long-range temporal correlations and fat-tail distribution. This study shows that the Czech, Hungarian and Russian stock exchanges are neither efficient nor fractals, but they are multifractal markets. By comparing spectrum width of these indices, we also find which index has the richer multifractal feature.

Multifractal analysis of Asian markets during 2007–2008 financial crisis

Physica A: Statistical Mechanics and its Applications, 2015

h i g h l i g h t s • We study the US and Asian markets during 2007-2008 crisis triggered by the US subprime loans. • A study of markets during a crisis could reveal important information about their dynamics. • Markets of the US, Japan, Hong Kong, Korea and Indonesia show strong nonlinearities for positive q. • These nonlinearities are due to long range correlations of large fluctuations in returns. • The tail exponent of the cumulative log return distribution decreases during the crisis period. a b s t r a c t 2007-2008 US financial crisis adversely affected the stock markets all over the world.

Fractality and Multifractality in a Stock Market’s Nonstationary Financial Time Series

Journal of the Korean Physical Society, 2020

A financial time series, such as a stock market index, foreign exchange rate, or a commodity price, fluctuates heavily and shows scaling behaviors. Scaling and multi-scaling behaviors are measured for a nonstationary time series, such as stock market indices, high-frequency stock prices of individual stocks, or the volatility time series of a stock index. We review the fractality, multi-scaling, and multifractality of the financial time series of a stock market. We introduce a detrended fluctuation analysis of the financial time series to extract fluctuation patterns. Multifractality is measured using various methods, such as generalized Hurst exponents, the generalized partition function method, a detrended fluctuation analysis, the detrended moving average method, and a wavelet transformation.

Multifractality in the stock market: price increments versus waiting times

Physica A: Statistical Mechanics and its Applications, 2005

By applying the multifractal detrended fluctuation analysis to the high-frequency tick-by-tick data from Deutsche Börse both in the price and in the time domains, we investigate multifractal properties of the time series of logarithmic price increments and inter-trade intervals of time. We show that both quantities reveal multiscaling and that this result holds across different stocks. The origin of the multifractal character of the corresponding dynamics is, among others, the long-range correlations in price increments and in inter-trade time intervals as well as the non-Gaussian distributions of the fluctuations. Since the transaction-to-transaction price increments do not strongly depend on or are almost independent of the inter-trade waiting times, both can be sources of the observed multifractal behaviour of the fixed-delay returns and volatility. The results presented also allow one to evaluate the applicability of the Multifractal Model of Asset Returns in the case of tick-by-tick data.

Multifractal properties of the Indian financial market

Physica A: Statistical Mechanics and its Applications, 2009

We investigate the multifractal properties of the logarithmic returns of the Indian financial indices (BSE & NSE) by applying the multifractal detrended fluctuation analysis. The results are compared with that of the US S&P 500 index. Numerically we find that qth-order generalized Hurst exponents h(q) and τ (q) change with the moments q. The nonlinear dependence of these scaling exponents and the singularity spectrum f (α) show that the returns possess multifractality. By comparing the MF-DFA results of the original series to those for the shuffled series, we find that the multifractality is due to the contributions of long-range correlations as well as the broad probability density function. The financial markets studied here are compared with the Binomial Multifractal Model (BMFM) and have a smaller multifractal strength than the BMFM.