Relevant bibliographies by topics / Volatility; Generalized Autoregressive Conditional Heteroskedasticity; Arch effect

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Academic literature on the topic 'Volatility; Generalized Autoregressive Conditional Heteroskedasticity; Arch effect'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Volatility; Generalized Autoregressive Conditional Heteroskedasticity; Arch effect"

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Bai G., Vidya, Daniel Frank, Ramona Birau, Virgil Popescu, and Maddodi B. S. "Market volatility in cryptocurrencies: A comparative study using GARCH and TGARCH models." Multidisciplinary Science Journal 7, no. 1 (2024): 2025029. http://dx.doi.org/10.31893/multirev.2025029.

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Price volatility has a negative connotation, as it is associated with market instability, uncertainty, and loss. When markets swing, investors and traders tend to place additional bets anticipating further swings, resulting in increased price volatility. There are no indices to assess crypto price volatility, but investigating historical price fluctuations provides insights into the rising peaks and depressive troughs that occur at a faster and more extreme rate in crypto prices compared to asset values in mainstream markets. This study employed generalized autoregressive conditional heteroske
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Juliana, Ahmad, and Apriliani Mutoharo. "STUDI SPILLOVER EFEK EXCHANGE-TRADED FUNDS (ETFs) DI ASEAN." Jurnal Riset Manajemen dan Bisnis (JRMB) Fakultas Ekonomi UNIAT 4, no. 2 (2019): 245–56. http://dx.doi.org/10.36226/jrmb.v4i2.262.

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The volatility of financial security make an investor difficult and inaccurate to predict the value of targeted investation. The failure for predicting the value of financial asset will mitigate for either succeed or not an investation. That condition will not happen if an investor has knowledge for predicting the volatility financial asset. There for, we need study for forecasting the spillover effect of financial asset using ARCH-GARCH model. The novelty of this study is, we compare the three of ASEAN ETFs that still rarely investigate, are: Indonesia, Malaysia and Singapore using 5 samples
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Morina, Fisnik, Valdrin Misiri, Saimir Dinaj, and Simon Grima. "THE IMPACT OF THE COVID-19 PANDEMIC AND THE RUSSIAN INVASION OF UKRAINE ON GOLD MARKETS." Business, Management and Economics Engineering 22, no. 01 (2024): 17–32. http://dx.doi.org/10.3846/bmee.2024.19799.

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Purpose – The study examines global Gold market performance and correlations between COVID-19, the Russian invasion, inflation, investors’ fear, asymmetric shocks, and the VIX (volatility index) impact on volatility. Research Methodology – This research uses an econometric approach to analyse the impact of COVID-19 and the Russian invasion on Gold market performance – specifically the ARCH (Autoregressive Conditional Heteroskedasticity) – GARCH (Generalized Autoregressive Conditional Heteroskedasticity) Model and the Threshold-Asymmetric ARCH Model. Findings – The study reveals that the COVID-
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Umoru, David, Solomon Edem Effiong, Malachy Ashywel Ugbaka, et al. "Modelling and estimating volatilities in exchange rate return and the response of exchange rates to oil shock." Journal of Governance and Regulation 12, no. 1 (2023): 185–96. http://dx.doi.org/10.22495/jgrv12i1art17.

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Developing countries have persistently witnessed volatile exchange. Such volatility triggered instability in their exchange rates which induced colossal fluctuations in currency rates leading to uncertainty for both the consumers and firms. All these have instigated changes in official exchange rates that are harmful to underlie trade patterns in these countries. This study estimated fluctuations in daily exchange rate returns of ten African countries using generalized autoregressive conditional heteroskedasticity (GARCH) models, having ascertained the significance of autoregressive conditiona
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Shobha, C. V. "A STUDY ON GOLD AS A SAFER INVESTMENT ALTERNATIVE AMONG SMALL AND MEDIUM INVESTORS WITH SPECIAL REFERENCE TO KOZHIKODE DISTRICT." International Journal of Research - Granthaalayah 5, no. 11 (2017): 27–45. https://doi.org/10.5281/zenodo.1065958.

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Among the various precious metals “Gold” is the most popular as an investment.  Why it is so?  The answer is it is a mainstream asset as it is not only an effective diversifier but also gives a competitive return when compared to major financial assets.  The present study analyses ‘Gold as a safer investment alternative’ by examining its risk and return in terms of other investment alternatives like stock and bond.  The risk and return analysis of an asset class is better studied with its volatility measurement.   The present study uses dai
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Babar, Misbah. "Volatility in Stock Market Returns and Macroeconomic Factors in Pakistan." Research Letters 2, no. 1 (2025): 81–88. https://doi.org/10.5281/zenodo.14803272.

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This study examines the intricate relationship between macroeconomic factors and stock market returns in Pakistan over the period 1999–2023. Utilizing advanced econometric techniques, including Autoregressive Conditional Heteroskedasticity (ARCH), Generalized Autoregressive Conditional Heteroskedasticity (GARCH), and Threshold GARCH (TGARCH), the research investigates the impact of GDP growth, inflation, and exchange rate fluctuations on stock market volatility. The empirical findings highlight the crucial role of macroeconomic stability in mitigating systemic risks and enhancing financi
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Hutapea, Tigor. "Analysis of Volatility of the Return of Composite Stock Price Index Using ARCH/GARCH Model, January 2015 - September 2024." JURNAL KEWIRAUSAHAAN, AKUNTANSI DAN MANAJEMEN TRI BISNIS 7, no. 1 (2025): 81–99. https://doi.org/10.59806/jkamtb.v7i1.498.

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The objectives of this paper is to identify and measure the volatility of the return of composite stock price index in the time period January, 2015 – September, 2024 using model ARCH/GARCH. It has been identified that the best model in explaining the volatility of the return in the time period was GARCH (1,1). The interesting findings, among others, firstly, the average return of the index is 0.4548 or 45.48 percent monthly in the time period. Secondly, the volatility of return of index at the certain month affected by squared residual of previous months of 27.63 percent. Thirdly, 53.58 perce
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Baryshych, Luka, and Dieudonne Dusengumukiza. "GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY MODELING OF ONEYEAR MATURITY GOVERNMENT BONDS OF GREECE DURING SOVEREIGN DEBT CRISIS OF EUROZONE IN 2010." Scientific Bulletin of Mukachevo State University. Series “Economics” 1(13) (2020): 184–91. http://dx.doi.org/10.31339/2313-8114-2020-1(13)-184-191.

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ination of international trade imbalances, the impact of the global crisis from 2007 to 2012, failure in bailout approaches of European governments that troubled banking industries and private bondholders, high-risk lending and borrowing policies enforced by unrestricted credit requirements during the period from 2002 to 2008 and fiscal policy choices related to government revenues and expenses. The objective is to model the boiling state of the Greek local financial market before the peak of the Sovereign Debt Crisis of Eurozone in 2009, modelling the insights of foreign investors and credit
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Bakar, Nashirah Abu, and Sofian Rosbi. "Modeling Volatility for High-Frequency Data of Cryptocurrency Bitcoin Price using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model." International Journal of Advanced Engineering Research and Science 9, no. 9 (2022): 573–79. http://dx.doi.org/10.22161/ijaers.99.62.

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The cryptocurrency namely Bitcoin is a decentralized cryptocurrency considered a type of digital asset that uses public-key cryptography to record, sign and send transactions over the Bitcoin blockchain. All transaction processes are performed without the oversight of a central authority. The time series data for Bitcoin price movement exhibit time-varying volatility and volatility clustering. This study aims to evaluate the time-varying volatility of Bitcoin price using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. This study uses daily share prices starting fro
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Omokehinde, Joshua Odutola, Matthew Adeolu Abata, Olukayode Russell, Stephen Oseko Migiro, and Christopher Somoye. "Asymmetric Information and Volatility of Stock Returns in Nigeria." Journal of Economics and Behavioral Studies 9, no. 3(J) (2017): 220–31. http://dx.doi.org/10.22610/jebs.v9i3(j).1761.

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This paper investigates the effect of asymmetric information on volatility of stock returns in Nigeria using the best-fit Asymmetric Power Autoregressive Conditional Heteroskedasticity, APARCH (1,1) model, under the Generalized Error Distribution (GED) at 1% significance level from 3 January 2000 to 29 November 2016. The descriptive statistical results showed that the returns were not normally and linearly distributed, with strong evidence of a heteroskedasticity effect. The results of the analysis also confirmed the effect of asymmetric information on the volatility of stock returns in the Ni
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Book chapters on the topic "Volatility; Generalized Autoregressive Conditional Heteroskedasticity; Arch effect"

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Attri, Shradha, Sanjeev Gupta, and Sachin Singh. "Risk Forecasting Using Artificial Intelligence and Machine Learning." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-1200-2.ch009.

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The financial market is where physical or virtual assets like foreign exchange, stock, cryptocurrency, and derivatives can be bought and sold. The study examined the role of artificial intelligence and machine learning techniques, mainly focusing on the stock and cryptocurrency markets, which represent physical and virtual assets. Due to the high volatility in the stock and cryptocurrency market, traditional statistical tools like Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) families, Autoregressive Integrated Movin
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Engle, Robert F., and Chowdhury Mustafa. "Implied ARCH Models from Options Prices." In Arch. Oxford University PressOxford, 1995. http://dx.doi.org/10.1093/oso/9780198774310.003.0017.

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Abstract This paper estimates the implied stochastic process of the volatility of an asset from the prices of options written on the asset. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to parameterize the process. Then the GARCH model implied by the option market is estimated by a generalized simulation minimization method from option price data. The persistence of volatility shocks implied by options on the Standard & Poor’s 500 is found to be similar to that estimated from historical data on the index itself. However, the implied persistence after t
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Mugaloglu, Yusuf I. "The Effect of Index Warrant Trading on the Underlying Volatility in the Post-Crisis Period." In Technology and Financial Crisis. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3006-2.ch017.

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The global financial crisis of 2007-2008 led to a sharp decrease in asset prices and increased volatility in financial markets. Before the crisis, warrant trading was often justified by assuming a more stabilised complete market and lower volatility. The Istanbul Stock Exchange introduced a warrant market and trading of ISE-30 index-based warrants in 2010. The chapter examines the impact of index-based warrant trading on the volatility of underlying ISE-30 index during post-crisis period of 2009-2011. The study employed a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) approa
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Uğurlu, Erginbay. "Research Data Analysis Using EViews." In Advances in Library and Information Science. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8437-7.ch014.

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The aim of this chapter is to provide a detailed empirical example of autoregressive conditional heteroskedasticity (ARCH) model and selected generalized ARCH models. Before the ARCH/GARCH models are estimated, several calculations and tests should be done. The mean model is determined using the autocorrelation function and partial autocorrelation function and also the unit root test. The existence of ARCH effect is tested using ARCH-LM test. After these steps are done, then ARCH/GARCH models can be estimated. All these theoretical aspects are applied to Sofia Stock Indexes (SOFIX) using EView
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Lehner, Edward, John R. Ziegler, and Louis Carter. "A Call for Second-Generation Cryptocurrency Valuation Metrics." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-9257-0.ch008.

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This chapter builds on the body of work that has depicted cryptocurrency as a model for science and higher education funding. To that end, this work examines the degree to which one or more cryptocurrencies would need to be adopted and achieve a network effect prior to implementation of such a funding model. Empirical data from three different cryptocurrencies were examined. The current work deploys generalized autoregressive conditional heteroskedasticity (GARCH) to analyze stochastic volatility. This work contends that the examined coins are likely overdistributed and too volatile, thereby l
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Lehner, Edward, John R. Ziegler, and Louis Carter. "A Call for Second-Generation Cryptocurrency Valuation Metrics." In Research Anthology on Blockchain Technology in Business, Healthcare, Education, and Government. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5351-0.ch042.

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This chapter builds on the body of work that has depicted cryptocurrency as a model for science and higher education funding. To that end, this work examines the degree to which one or more cryptocurrencies would need to be adopted and achieve a network effect prior to implementation of such a funding model. Empirical data from three different cryptocurrencies were examined. The current work deploys generalized autoregressive conditional heteroskedasticity (GARCH) to analyze stochastic volatility. This work contends that the examined coins are likely overdistributed and too volatile, thereby l
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Conference papers on the topic "Volatility; Generalized Autoregressive Conditional Heteroskedasticity; Arch effect"

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Staugaitis, Algirdas Justinas. "Financial speculation impact on agricultural commodity price volatility: TGARCH approach." In 21st International Scientific Conference "Economic Science for Rural Development 2020". Latvia University of Life Sciences and Technologies. Faculty of Economics and Social Development, 2020. http://dx.doi.org/10.22616/esrd.2020.53.014.

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Motivated by agricultural commodity price fluctuations and spikes in the last decade, we investigate whether financial speculation destabilizes the price of agricultural commodities. The aim of this research is to assess the impact of financial speculation on agricultural commodity price volatility. In our study we use weekly returns on wheat, soybean and corn futures from Chicago Mercantile of Exchange. To measure this impact, we apply autoregressive conditional heteroskedasticity (ARCH) technique. We also propose a model with seasonal dummy variables to measure if financial speculation impac
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