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The purpose of this paper is to present the Islamic stock and Sukuk market efficiency and focus on the presence of investor herding behaviour (HB) captured by Hurst exponent estimation.

The Hurst exponent was estimated with various methods. The authors studied the evolving efficiency of the “Dow Jones” indices from 1 January 2010 to 30 December 2016 using a rolling sample of the Hurst exponent. In addition, they used a time-varying parameter method based on the Hurst of delayed returns. After that, the robust Hurst method was considered. In the next step, the efficiency of the different activity types of Islamic bonds was studied using an efficiency index. Finally, the Hurst exponent estimates were applied to assess the presence of HB.

The results show that, firstly, there’s a strong correlation between the “DJIM” and “DJSI” prices and returns. Secondly, by using robust Hurst estimate, it is observed that the “DJIM” is the most efficient market. The Hurst exponent estimation results show that HB is more intensive in the Islamic stock market. These results indicate also the inexistence of this behaviour in the studied Sukuk market.

Sukuk as Islamic financial assets is recent. Their relative time series are not long enough to apply the long memory approach. Furthermore, this work can be extended to study other Islamic financial markets.

Herding affects risk-return characteristics of assets and has an impact on asset pricing models. Practitioners are interested in understanding herding and its timing as it might create profitable trading opportunities.

This work analyses the impact of Islamic principles on the financial markets and their ability to understand some behavioural biases.

This study contributes to the literature by identifying the efficiency and the presence of HB with Hurst exponent estimation in Islamic markets.

La presente investigación examina la existencia de memoria de largo plazo por medio del cálculo del coeficiente de Hurst y Hurst ajustado, y del análisis de características de estructuras caóticas en la serie del mercado bursátil de Chile, específicamente a través del Índice de Precios Selectivo de Acciones.

Se desarrolló un breve análisis del mercado, según la metodología de Box y Jenkings. La validez de los resultados se realizó por medio de la prueba propuesta por Brock, Dechert y Scheinkman. En segundo lugar, se procedió a analizar la dinámica y patrones del índice y de su rendimiento, para observar si existía evidencia de memoria de largo plazo.

Los resultados demuestran la presencia de esta memoria en el mercado bursátil chileno, determinado a través del índice accionario en dos escalas, diaria y trimestral, lo que además corrobora resultados obtenidos por otros autores, confirmando el uso de la metodología de Rango Re-escaldo para la identificación y determinación de memoria de largo plazo en una serie temporal.

Este estudio permitirá a futuros investigadores realizar análisis similares en otros mercados, aportando un nuevo enfoque al analizar la memoria de la largo plazo y los factores que inciden en ella.

Exponente de Hurst, Índice bursátil, Mercados eficientes, Mercados fractales

Artículo de investigación

This research examined the existence of long-term memory by calculating the coefficient of Hurst and Hurst set, and the analysis of characteristics of chaotic structures in the series of stock market of Chile, specifically through the Selective Price Index Shares.

A brief analysis of the market was developed, according to Box and Jenkins methodology. The validity of the results was performed by means of the test proposed by Brock, Dechert and Scheinkman. Secondly, we proceeded to analyze the dynamics and patterns of the index and its performance, to see if there was evidence of long-term memory.

The results demonstrate the presence of long-term memory in the Chilean stock market, determined by stock index in two scales, daily and quarterly, which also corroborates results obtained by other authors, confirming the use of the methodology Range Re-scalded for the identification and determination of long-term memory in a time series.

This study will allow future researchers to perform similar analyzes in other markets, providing a new approach when analyzing the long-term memory and the factors that affect it.

The purpose of this study is to provide a new way to optimize a portfolio and to show that combining the Hurst exponent and wavelet analysis may help to increase portfolio returns.

The authors use the Hurst exponent and wavelet analysis to study the long-term dependencies between sovereign bonds and sectoral indices of India. The authors further construct and evaluate the performance of three portfolios constructed on the basis of Hurst standard deviation (SD) – global minimum variance (GMV), most diversified portfolio (MDP) and equal risk contribution (ERC).

The authors find that an ERC portfolio generates positive superior return as compared other two. Since our sample includes periods of two crisis – post-2007 financial crisis and the ongoing pandemic, this study reveals that combining government bond with equities and gold provides a higher returns when the portfolios are constructed using the risk exposures of each asset in the overall portfolio risk.

The findings provide guidance to portfolio managers by helping them to select assets using the Hurst approach and wavelet analysis thereby increasing the portfolio returns.

In this study, the authors use a combination of Hurst exponent and wavelet analysis to understand the long-term dependencies among various assets and provide a new methodology to optimize a portfolio. As far as the authors’ knowledge, no study in the past has attempted to provide a joint framework for portfolio optimization and therefore this study is the first to apply this methodology.

The purpose of this paper is to determine whether foreign exchange markets in India have become more efficient over time. There were two major developments in India’s foreign exchange market since the 1980s: first, a shift in foreign exchange management regime from a basket peg to a free float; and second, a rapid phase of economic liberalization since the mid-1990s. The paper attempts to find out whether the market efficiency of foreign exchange markets is affected by these developments. The paper mainly uses the well-known Hurst exponent calculated through corrected empirical R over S analysis to determine whether the exchange rates possess long memory. The robustness of the method is tested by calculating the Hurst exponent through two other prevalent methods in the literature.

The authors apply the corrected empirical Hurst exponent which employs the Anis Lloyd correction with the modification suggested by Weron. The sensitivity of the results is then tested by replicating the calculations using the detrended fluctuation analysis and Robinson’s method.

All the methods show that: first, there is no significant change in the overall efficiency of the foreign exchange market vis a vis the US$ for the time period from 1980 to 2017. Second, neither regime shifts nor calculations over sub-time periods is able to identify significant change in the efficiency level of the market for the US$ exchange rate. Third, efficiency of different exchange rate markets are different over the time period 1999–2017. The US$ market has unequivocally more long run memory compared to the GBP, Yen and EURO markets. Fourth, the results are robust to the method used for calculations.

Does the efficiency of asset markets evolve over time? This paper attempts to answer this question. In the process, the paper studies the effect of regime shifts and progressive globalization on the ability of the market to internalize information.

The Hurst exponent has been very important in telling the difference between fractal signals and explaining their significance. For estimators of the Hurst exponent, accuracy and efficiency are two inevitable considerations. The main purpose of this study is to raise the execution efficiency of the existing estimators, especially the fast maximum likelihood estimator (MLE), which has optimal accuracy.

A two-stage procedure combining a quicker method and a more accurate one to estimate the Hurst exponent from a large to small range will be developed. For the best possible accuracy, the data-induction method is currently ideal for the first-stage estimator and the fast MLE is the best candidate for the second-stage estimator.

For signals modeled as discrete-time fractional Gaussian noise, the proposed two-stage estimator can save up to 41.18 per cent the computational time of the fast MLE while remaining almost as accurate as the fast MLE, and even for signals modeled as discrete-time fractional Brownian motion, it can also save about 35.29 per cent except for smaller data sizes.

The proposed two-stage estimation procedure is a novel idea. It can be expected that other fields of parameter estimation can apply the concept of the two-stage estimation procedure to raise computational performance while remaining almost as accurate as the more accurate of two estimators.

The purpose of this paper is to test the efficient market hypothesis for major Indian sectoral indices by means of long memory approach in both time domain and frequency domain. This paper also tests the accuracy of the detrended fluctuation analysis (DFA) approach and the local Whittle (LW) approach by means of Monte Carlo simulation experiments.

The author applies the DFA approach for the computation of the scaling exponent in the time domain. The robustness of the results is tested by the computation of the scaling exponent in the frequency domain by means of the LW estimator. The author applies moving sub-sample approach on DFA to study the evolution of market efficiency in Indian sectoral indices.

The Monte Carlo simulation experiments indicate that the DFA approach and the LW approach provides good estimates of the scaling exponent as the sample size increases. The author also finds that the efficiency characteristics of Indian sectoral indices and their stages of development are dynamic in nature.

This paper has both methodological and empirical originality. On the methodological side, the author tests the small sample properties of the DFA and the LW approaches by using simulated series of fractional Gaussian noise and find that both the approach possesses superior properties in terms of capturing the scaling behavior of asset prices. On the empirical side, the author studies the evolution of long-range dependence characteristics in Indian sectoral indices.

Recent studies indicating long term dependence in stock market indices have found a mean reversion process. However, studies using rescaled range (R/S) analysis have not found evidence of a mean reversion or ergodic process. Instead, evidence from these studies indicate either long term persistence in a nonperiodic cycle or short run Markovian dependence with no long term persistence. The purpose of this paper is to study the issue of long term dependence using rescaled range analysis. The empirical results obtained in this study support the persistent dependence/nonperiodic cycle results and suggest that the dependence arises from the general economic cycle.

To consider the recent decision by the Court of Appeal in the case of Hurst Stores v. M L Europe Property Ltd (2004) and the possible important implications for building surveyors and other construction professionals.

A review of literature and case law. Possible developments in this area of law are considered.

A person described as a “project manager” does not necessarily have authority to make legally binding agreements. Where a building surveyor is acting as a project manager, the extent of authority ought to be clarified, preferably in writing. Where a building surveyor is dealing or negotiating with a project manager, he/she cannot take it for granted that any agreement reached will be legally binding. Furthermore, as a consequence of the decision in the Court of Appeal, a document with the heading “Final Account” might not be a final account at all!

Increasingly, building surveyors are involved in adjudication, either as witnesses or as adjudicators. In the latter case, the building surveyor ought to be aware that as a result of this case, the probability of a legal challenge to an adjudicator's decision is greatly increased. Another consequence of cases such as Hurst means that building surveyors are more likely rather than less likely to be involved in adjudication if they are operating in the UK.

Applies recent case law to the work of building surveyors.

The purpose of this paper is to test the existence of stylized facts, such as the volatility clustering, heavy tails seen on financial series, long-term dependence and multifractality on the returns of four real estate indexes using different types of indexes: conventional and Islamic by comparing pre and during COVID-19 pandemic.

Firstly, the authors examined the characteristics of the indexes. Secondly, the authors estimated the parameters of the stable distribution. Then, the long memory is detected via the estimation of the Hurst exponents. Afterwards, the authors determine the graphs of the multifractal detrended fluctuation analysis (MF-DFA). Finally, the authors apply the WTMM method.

The results suggest that the real estate indexes are far from being efficient and that the lowest level of multifractality was observed for Islamic indexes.

The inefficiency behavior of real estate indexes gives us an idea about the prediction of the behavior of future returns in these markets on the basis of past informations. Similarly, market participants would do well to reassess their investment and risk management framework to mitigate new and somewhat higher levels of risk of their exposures during the turbulent period.

To the authors’ knowledge, this is the first real estate market study employing STL decomposition before applying the MF-DFA in the context of the COVID-19 crisis. Likewise, the study is the first investigation that focuses on these four indexes.

The purpose of this paper is to argue that a stationary-differenced autoregressive (AR) process with lag greater than 1, AR(q > 1), has certain properties that are consistent with a fractional Brownian motion (fBm). What the authors are interested in is the investigation of approaches to identifying the existence of persistent memory of one form or another for the purposes of simulating commodity (and other asset) prices. The authors show in theory, and with application to agricultural commodity prices the relationship between AR(q) and quasi-fBm.

In this paper the authors develop mathematical relationships in support of using AR(q > 1) processes for simulating quasi-fBm.

From theory the authors show that any AR(q) process is a stationary, self-similar process, with a lag structure that captures the essential elements of scaling and a fractional power law. The authors illustrate through various means the approach, and apply the quasi-fractional AR(q) process to agricultural commodity prices.

While the results can be applied to most time series of commodity prices, the authors limit the evaluation to the Gaussian case. Thus the approach does not apply to infinite-variance models.

The approach to using the structure of an AR(q > 1) model to simulate quasi-fBm is a simple approach that can be applied with ease using conventional Monte Carlo methods.

The authors believe that the approach to simulating quasi-fBm using standard AR(q > 1) models is original. The approach is intuitive and can be applied easily.