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Book part
Publication date: 17 December 2003

Ching-Fan Chung, Mao-Wei Hung and Yu-Hong Liu

This study employs a new time series representation of persistence in conditional mean and variance to test for the existence of the long memory property in the currency…

Abstract

This study employs a new time series representation of persistence in conditional mean and variance to test for the existence of the long memory property in the currency futures market. Empirical results indicate that there exists a fractional exponent in the differencing process for foreign currency futures prices. The series of returns for these currencies displays long-term positive dependence. A hedging strategy for long memory in volatility is also discussed in this article to help the investors hedge for the exchange rate risk by using currency futures.

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Research in Finance
Type: Book
ISBN: 978-1-84950-251-1

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Article
Publication date: 2 October 2009

David G. McMillan and Pako Thupayagale

In order to assess the informational efficiency of African equity markets (AEMs), the purpose of this paper is to examine long memory in both equity returns and volatility…

Abstract

Purpose

In order to assess the informational efficiency of African equity markets (AEMs), the purpose of this paper is to examine long memory in both equity returns and volatility using auto‐regressive fractionally integrated moving average (ARFIMA)‐FIGARCH/hyperbolic GARCH (HYGARCH) models.

Design/methodology/approach

In order to test for long memory, the behaviour of the auto‐correlation function for 11 AEMs is examined. Following the graphical analysis, the authors proceed to estimate ARFIMA‐FIGARCH and ARFIMA‐HYGARCH models, specifically designed to capture long‐memory dynamics.

Findings

The results show that these markets (largely) display a predictable component in returns; while evidence of long memory in volatility is very mixed. In comparison, results from the control of the UK and USA show short memory in returns while evidence of long memory in volatility is mixed. These results show that the behaviour of equity market returns and risks are dissimilar across markets and this may have implications for portfolio diversification and risk management strategies.

Practical implications

The results of the analysis may have important implications for portfolio diversification and risk management strategies.

Originality/value

The importance of this paper lies in it being the first to systematically analyse long‐memory dynamics for a range of AEMs. African markets are becoming increasingly important as a source of international portfolio diversification and risk management. Hence, the results here have implication for the conduct of international portfolio building, asset pricing and hedging.

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Studies in Economics and Finance, vol. 26 no. 4
Type: Research Article
ISSN: 1086-7376

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Article
Publication date: 7 August 2007

Christos Floros, Shabbar Jaffry and Goncalo Valle Lima

This paper's aim is to test for the presence of fractional integration, or long memory, in the daily returns of the Portuguese stock market using autoregressive…

Abstract

Purpose

This paper's aim is to test for the presence of fractional integration, or long memory, in the daily returns of the Portuguese stock market using autoregressive fractionally integrated moving average (ARFIMA), generalised autoregressive conditional heteroskedasticity (GARCH) and ARFIMA‐FIGARCH models.

Design/methodology/approach

The data cover two periods: 4 January 1993‐13 January 2006 (full sample), and 1 February 2002‐13 January 2006 (that is, data are considered after the merger of the Portuguese Stock Exchange with Euronext).

Findings

The results from the full sample show strong evidence of long memory in stock returns. When data after the merger are considered, weaker evidence of long memory is found. It is concluded that the Portuguese stock market is more efficient after the merger with Euronext.

Originality/value

The findings of this paper are helpful to financial managers and investors dealing with Portuguese stock indices.

Details

Studies in Economics and Finance, vol. 24 no. 3
Type: Research Article
ISSN: 1086-7376

Keywords

Content available
Article
Publication date: 7 August 2019

Trang Nguyen, Taha Chaiechi, Lynne Eagle and David Low

Growth enterprise market (GEM) in Hong Kong is acknowledged as one of the world’s most successful examples of small and medium enterprise (SME) stock market. The purpose…

Abstract

Purpose

Growth enterprise market (GEM) in Hong Kong is acknowledged as one of the world’s most successful examples of small and medium enterprise (SME) stock market. The purpose of this paper is to examine the evolving efficiency and dual long memory in the GEM. This paper also explores the joint impacts of thin trading, structural breaks and inflation on the dual long memory.

Design/methodology/approach

State-space GARCH-M model, Kalman filter estimation, factor-adjustment techniques and fractionally integrated models: ARFIMA–FIGARCH, ARFIMA–FIAPARCH and ARFIMA–HYGARCH are adopted for the empirical analysis.

Findings

The results indicate that the GEM is still weak-form inefficient but shows a tendency towards efficiency over time except during the global financial crisis. There also exists a stationary long-memory property in the market return and volatility; however, these long-memory properties weaken in magnitude and/or statistical significance when the joint impacts of the three aforementioned factors were taken into account.

Research limitations/implications

A forecasts of the hedging model that capture dual long memory could provide investors further insights into risk management of investments in the GEM.

Practical implications

The findings of this study are relevant to market authorities in improving the GEM market efficiency and investors in modelling hedging strategies for the GEM.

Originality/value

This study is the first to investigate the evolving efficiency and dual long memory in an SME stock market, and the joint impacts of thin trading, structural breaks and inflation on the dual long memory.

Details

Journal of Asian Business and Economic Studies, vol. 27 no. 1
Type: Research Article
ISSN: 2515-964X

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Article
Publication date: 7 August 2017

Geeta Duppati, Anoop S. Kumar, Frank Scrimgeour and Leon Li

The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory.

Abstract

Purpose

The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory.

Design/methodology/approach

This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory.

Findings

Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts.

Practical implications

The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management.

Social implications

It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks.

Originality/value

This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.

Details

Pacific Accounting Review, vol. 29 no. 3
Type: Research Article
ISSN: 0114-0582

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Book part
Publication date: 2 December 2003

Jun Nagayasu

Using the ARFIMA-FIGARCH model, this paper studies the efficiency of the Japanese equity market by examining the statistical properties of the returns and volatility of…

Abstract

Using the ARFIMA-FIGARCH model, this paper studies the efficiency of the Japanese equity market by examining the statistical properties of the returns and volatility of the Nikkei 225. It shows that both follow a long-range dependence, which stands against the applicability of the efficient market hypothesis. The result is valid for all sample periods, suggesting that the Japanese market remains inefficient despite the recent equity market reform.

Details

The Japanese Finance: Corporate Finance and Capital Markets in ...
Type: Book
ISBN: 978-1-84950-246-7

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Article
Publication date: 17 December 2020

Zhengxun Tan, Yao Fu, Hong Cheng and Juan Liu

This study aims to examine the long memory as well as the effect of structural breaks in the US and the Chinese stock markets. More importantly, it further explores…

Abstract

Purpose

This study aims to examine the long memory as well as the effect of structural breaks in the US and the Chinese stock markets. More importantly, it further explores possible causes of the differences in long memory between these two stock markets.

Design/methodology/approach

The authors employ various methods to estimate the memory parameters, including the modified R/S, averaged periodogram, Lagrange multiplier, local Whittle and exact local Whittle estimations.

Findings

China's two stock markets exhibit long memory, whereas the two US markets do not. Furthermore, long memory is robust in Chinese markets even when we test break-adjusted data. The Chinese stock market does not meet the efficient market hypothesis (EMHs), including the efficiency of information disclosure, regulations and supervision, investors' behavior, and trading mechanisms. Therefore, its stock prices' sluggish response to information leads to momentum effects and long memory.

Originality/value

The authors elaborately illustrate how long memory develops by analyzing not only stock market indices but also typical individual stocks in both the emerging China and the developed US, which diversifies the EMH with wider international stylized facts and findings when compared with previous literature. A couple of tests conducted to analyze structural break effects and spurious long memory demonstrate the reliability of the results. The authors’ findings have significant implications for investors and policymakers worldwide.

Details

International Journal of Emerging Markets, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-8809

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Article
Publication date: 15 July 2020

Paria Soleimani, Bahareh Emami, Meysam Rafei and Hooman Shahrasbi

Today, because of the increasing need for the energy resources and the reduction of fossil fuels, renewable energy, especially wind energy, has attracted special…

Abstract

Purpose

Today, because of the increasing need for the energy resources and the reduction of fossil fuels, renewable energy, especially wind energy, has attracted special attention. The precise forecasting of such energy will be the main factor in designing and investing in this field. On the other hand, the wind energy forecast provides the possibility of optimal use of available resources. In addition, the produce maximum energy would be possible by identifying wind direction and putting wind turbines in the best position.

Design/methodology/approach

Time series forecasting methods with long-term memory in this research have been used.

Findings

Eventually, the autoregressive fractionally integrated moving average (3,0,0)-FIGARCH (1,0,1) long-term memory model has more acceptable performance. The obtained error is based on the RMSE (0.2889) and the TIC (0.2605) values.

Practical implications

In this paper, the forecast wind direction belongs to Ardebil province and Nayer city in Iran.

Originality/value

The speed and direction of wind are variables that constantly change; hence, it will be difficult to predict the exact wind energy. In recent years, some studies have been conducted on wind speed forecasting, whereas wind direction forecasting has been done in a fewer number of studies. Most studies are related to low-lying areas. As the height of the wind turbine is directly related to the energy generation, 78 m height has been considered in this study.

Details

International Journal of Energy Sector Management, vol. 15 no. 2
Type: Research Article
ISSN: 1750-6220

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Article
Publication date: 8 November 2011

Turkhan Ali Abdul Manap and Salina H. Kassim

The purpose of this paper is to examine the long memory property of equity returns and volatility of emerging equity market by focusing on the Malaysian equity market…

Abstract

Purpose

The purpose of this paper is to examine the long memory property of equity returns and volatility of emerging equity market by focusing on the Malaysian equity market, namely the Kuala Lumpur Stock Exchange (KLSE).

Design/methodology/approach

The study adopts the Fractionally Integrated GARCH (FIGARCH) model and Fractionally Integrated Asymmetric Power ARCH (FIAPARCH), focusing on the Malaysian data covering the period from April 15, 2004 to April 30, 2007.

Findings

The study finds evidence of long memory property as well as asymmetric effects in the volatility of the KLSE. The traditional ARCH/GARCH is shown to be insufficient in modeling the volatility persistence. The FIAPARCH specification outperforms the FIGARCH model by capturing both asymmetry effects and long memory in the conditional variance.

Research limitations/implications

The results of this study have practical implications for the investors intending to invest in the emerging markets such as Malaysia. Understanding volatility and developing the appropriate models are important since volatility can be a measure of risk which is highly relevant in forecasting the conditional volatility of returns for portfolio selection, asset pricing, and value at risk, option pricing and hedging strategies.

Originality/value

This study contributes in providing the empirical evidence on the long memory property of equity returns and volatility of an emerging equity market with reliable estimation models, which is currently lacking, particularly for emerging markets.

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Article
Publication date: 15 January 2018

Shaista Wasiuzzaman and Noura Abdullah Al-Musehel

The purpose of this paper is to focus on the influence of mood/emotions and religious experience on Islamic stock markets during the Ramadan month.

Abstract

Purpose

The purpose of this paper is to focus on the influence of mood/emotions and religious experience on Islamic stock markets during the Ramadan month.

Design/methodology/approach

This study uses stock returns data of two countries – Saudi Arabia and Iran – from January 2008 to September 2014 and the ARMA-GARCH models to study impact of the Ramadan month on the return and volatility of the stock market in these two countries.

Findings

The results of this study show some differences in the impact of the Ramadan month on the return and volatility of the stock market in these two countries. While the Ramadan month has a significant positive influence on the mean returns and the volatility of the Saudi market, its influence on the Iranian market is found to be insignificant. Further analysis on the last ten days of the Ramadan month provides a similar result for the Saudi market. However, for the Iranian market, volatility is significantly negatively affected during these last ten days.

Originality/value

Most prior studies have found significant changes in returns during the Ramadan month but a deeper understanding of this stock market anomaly is needed. The results point toward the influence of mood/emotions and religious experience in explaining the existence of the Ramadan anomaly.

Details

International Journal of Emerging Markets, vol. 13 no. 1
Type: Research Article
ISSN: 1746-8809

Keywords

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