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Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Volatility; Generalized Autoregressive Conditional Heteroskedasticity; Arch effect.

Статті в журналах з теми "Volatility; Generalized Autoregressive Conditional Heteroskedasticity; Arch effect"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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Анотація:

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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9

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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10

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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11

Omokehinde, Joshua Odutola, Matthew Adeolu Abata, Russell Olukayode Christopher Somoye, and Stephen Oseko Migiro. "Asymmetric Information and Volatility of Stock Returns in Nigeria." Journal of Economics and Behavioral Studies 9, no. 3 (2017): 220. http://dx.doi.org/10.22610/jebs.v9i3.1761.

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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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12

XIAO, JINGLIANG, ROBERT D. BROOKS, and WING-KEUNG WONG. "GARCH AND VOLUME EFFECTS IN THE AUSTRALIAN STOCK MARKETS." Annals of Financial Economics 05, no. 01 (2009): 0950005. http://dx.doi.org/10.1142/s2010495209500055.

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This paper explores the relationship between volume and volatility in the Australian Stock Market in the context of a generalized autoregressive conditional heteroskedasticity (GARCH) model. In contrast to other studies who only examine the interaction of GARCH and volume effects on a small number of stocks, we examine these effects on the entire available data for the Australian All Ordinaries Index. We also emphasize on the impact of firm size and trading volume. Our results indicate that GARCH model testing and estimation is impacted by firm size and trading volume. Specifically, our analys

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13

Ghufran, Bushra, Hayat M. Awan, Aftab Khan Khakwani, and Muhammad Azeem Qureshi. "What Causes Stock Market Volatility in Pakistan? Evidence from the Field." Economics Research International 2016 (August 28, 2016): 1–9. http://dx.doi.org/10.1155/2016/3698297.

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We examined the presence of volatility at the Karachi Stock Exchange (recently changed the name to Pakistan Stock Exchange) (KSE) by fitting Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model to 25 years’ index data. We found that the ARCH effects are present in the data indicating the stock market cluster volatility during the period under study. We found persistent high volatility in the stock market and presence of negative leverage effect. Moreover, we tried to identify the factors causing stock market volatility by collecting and analyzing the primary dat

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14

Nasrudin, Muhammad, Endah Setyowati, and Shindi Shella May Wara. "Application of VAR-GARCH for Modeling the Causal Relationship of Stock Prices in the Mining Sub-sector." Jurnal Varian 8, no. 1 (2024): 89–96. https://doi.org/10.30812/varian.v8i1.4239.

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Accurate modeling is expected to minimize risk and maximize profit in investment portfolios, one ofwhich is in stock price modeling. This research aims to model the causal relationship between stockprices using the Vector Autoregressive - Generalized Autoregressive Conditional Heteroskedasticity(VAR-GARCH) model. The VAR-GARCH model is used to overcome heteroscedasticity and modeldynamic volatility. The data used for the modeling consists of daily stock prices from July 2023 toMay 2024 for mining sub-sector companies listed on the Jakarta Islamic Index (JII), including ADMR,ADRO, and ANTM. The

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15

Hema, Saini. "Volatility Spillover Among Sectoral Indices of the Indian and US Stock Markets." International Journal of Management and Humanities (IJMH) 11, no. 9 (2025): 11–17. https://doi.org/10.35940/ijmh.G1801.11090525.

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<strong>Abstract:</strong> The main aim of this study is to empirically analyze the volatility spillover among the sectoral equity returns for Indian and US markets. Utilizing the Dynamic Conditional Correlation model, the paper extracts the time‐varying conditional correlations between the sector indices. The analysis of the DCC-GARCH model indicates a conditional correlation between the Indian and US stock markets. Furthermore, despite market volatility and a significant disruption caused by the COVID-19 crisis in 2019, the consistent presence of a positive correlation highlights the strong

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16

Hema, Saini. "Volatility Spillover Among Sectoral Indices of the Indian and US Stock Markets." International Journal of Management and Humanities (IJMH) 11, no. 9 (2025): 11–17. https://doi.org/10.35940/ijmh.G1801.11090525/.

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<strong>Abstract: </strong>The main aim of this study is to empirically analyze the volatility spillover among the sectoral equity returns for Indian and US markets. The paper extracts the time‐varying conditional correlations between the sector indices using the Dynamic Conditional Correlation model. The analysis of the DCC-GARCH model indicates a conditional correlation between the Indian and US stock markets. Furthermore, despite market volatility and a significant disruption caused by the COVID-19 crisis in 2019, the consistent presence of a positive correlation highlights the strong and l

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17

Marisetty, Nagendra. "Applications of GARCH Models in Forecasting Financial Market Volatility: Insights from Leading Global Stock Indexes." Asian Journal of Economics, Business and Accounting 24, no. 9 (2024): 63–84. http://dx.doi.org/10.9734/ajeba/2024/v24i91477.

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This study investigates the volatility dynamics of major global stock indexes, including the FTSE 100, Hang Seng Index, NIKKEI 225, and S&P 500, using a range of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The analysis spans a comprehensive 20-year period from January 1, 2004, to December 31, 2023, encompassing diverse market conditions such as bull and bear markets, the 2008 financial crisis, and the COVID-19 pandemic. The methodology includes preprocessing steps such as calculating daily log returns, performing descriptive statistics, and conducting stationa

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18

Akhtar, Shahan, and Naimat U. Khan. "Modeling volatility on the Karachi Stock Exchange, Pakistan." Journal of Asia Business Studies 10, no. 3 (2016): 253–75. http://dx.doi.org/10.1108/jabs-05-2015-0060.

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Purpose The current paper aims to fill a gap in the literature by analyzing the nature of volatility on the Karachi Stock Exchange (KSE) 100 index of the KSE, and develop an understanding as to which model is most suitable for measuring volatility among those used. The study contributes significantly to the literature as, compared with the limited previous studies of Pakistan undertaken in the past, it covers three types of data (i.e. daily, weekly and monthly) for the whole period from the introduction of the KSE 100 index on November 2, 1991 to December 31, 2013. In addition, to analyze the

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19

Rashid, Abdul, and Mohammad Basit. "Empirical determinants of exchange-rate volatility: evidence from selected Asian economies." Journal of Chinese Economic and Foreign Trade Studies 15, no. 1 (2021): 63–86. http://dx.doi.org/10.1108/jcefts-04-2021-0017.

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Purpose This paper aims to explore the empirical determinants of exchange-rate volatility (ERV) in selected Asian economies, namely, Bangladesh, China, India, Indonesia, Malaysia and Pakistan. Specifically, it examines how the volatility of foreign reserves, government spending, industrial production, gold prices and terms of trade affect monthly ERV during the examined period. Design/methodology/approach The authors carry out the empirical analysis by using monthly data for the period January 1997–March 2019. First, the volatility of the underlying variables is measured based on the condition

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20

Othman, Anwar Hasan Abdullah, Syed Musa Alhabshi, and Razali Haron. "The effect of symmetric and asymmetric information on volatility structure of crypto-currency markets." Journal of Financial Economic Policy 11, no. 3 (2019): 432–50. http://dx.doi.org/10.1108/jfep-10-2018-0147.

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Purpose This paper aims to examine whether the crypto-currencies’ market returns are symmetric or asymmetric informative, through analysing the daily logarithmic returns of bitcoin currency over the period of 2011-2017. Design/methodology/approach In doing so, the symmetric informative analysis is estimated by applying the generalised auto-regressive conditional heteroscedasticity (GARCH) (1,1) model, whereas asymmetric informative or leverage effects analysis is estimated by exponential GARCH (1,1), asymmetric power ARCH (1,1) and threshold GARCH (1,1) models. In addition, the generalized aut

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21

Baghebo, Michael, and Kingdom Mienebimo. "BALANCE OF TRADE, EXCHANGE RATE AND ECONOMIC GROWTH IN NIGERIA." International Journal of Advanced Studies in Business Strategies and Management 11, no. 1 (2024): 217–41. http://dx.doi.org/10.48028/iiprds/ijasbsm.v11.i1.15.

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This study delved into the relationship between the balance of trade, exchange rates, and economic growth in Nigeria. Utilizing annual time series data spanning from 1981 to 2021 sourced from the Central Bank of Nigeria (CBN) statistical bulletin and the National Bureau of Statistics (NBS), the investigation employed a Heteroskedasticity Model. Within this framework, GARCH, an expanded version of ARCH, and the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGACH) were applied. The findings revealed that both trade balance and exchange rates exhibited heightened sensitiv

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22

Sadon, Aida Nabilah, Shuhaida Ismail, Azme Khamis, and Muhammad Usman Tariq. "Heteroscedasticity effects as component to future stock market predictions using RNN-based models." PLOS ONE 19, no. 5 (2024): e0297641. http://dx.doi.org/10.1371/journal.pone.0297641.

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Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long S

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23

Fateye, Oluwatosin Babatola, Damilola Damilola, and Professor Ajayi. "Modelling of Daily Price Volatility of South Africa Property Stock Market Using GARCH Analysis." Journal of African Real Estate Research 7, no. 2 (2022): 24–42. http://dx.doi.org/10.15641/jarer.v7i2.1144.

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The study examined the volatility of the daily market price of listed property stocks on the Johannesburg Stock Exchange (JSE) for a 10year period (2008-2017). The study used daily prices from January 2, 2008 to December 29, 2017 of twelve (12) quoted property companies out of the twenty-seven (27) listed on Johannesburg Stock Exchange (SA REIT Association, 2020). The study computed the average daily price of the selected (12) property stocks and was used as a proxy for the daily market price for the property stock market in the analysis. The study modelled SA-REIT market price volatility usin

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24

Demiralay, Sercan, Nikolaos Hourvouliades, and Athanasios Fassas. "Dynamic co-movements and directional spillovers among energy futures." Studies in Economics and Finance 37, no. 4 (2020): 673–96. http://dx.doi.org/10.1108/sef-09-2019-0374.

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Purpose This paper aims to examine dynamic equicorrelations (DECO) and directional volatility spillover effects among four energy futures markets, namely, West Texas Intermediate crude oil, heating oil, natural gas and reformulated blendstock for oxygenate blending gasoline, by using a multivariate fractionally integrated asymmetric power ARCH–DECO–generalized autoregressive conditional heteroskedasticity (GARCH) model and the spillover index technique. Design/methodology/approach The empirical analysis uses the dynamic equicorrelation model of Engle and Kelly (2012) to examine time-varying co

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25

Kuziboev, Bekhzod, Petra Vysušilová, Raufhon Salahodjaev, Alibek Rajabov, and Tukhtabek Rakhimov. "The Volatility Assessment of CO2 Emissions in Uzbekistan: ARCH/GARCH Models." International Journal of Energy Economics and Policy 13, no. 5 (2023): 1–7. http://dx.doi.org/10.32479/ijeep.14487.

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The study is pioneer to investigate the volatility of CO2 emissions in Uzbekistan. To this end, ARCH (Autoregressive Conditional Heteroskedasticity) and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models are used spanning the period 1925-2021 for the annual data of CO2 emissions. The results indicate that ARCH model is more adequate that GARCH model in the volatility assessment. Furthermore, it is found that the volatility of CO2 emissions in Uzbekistan is very high. The policymakers have to consider the high volatility of CO2 emissions in the environmental policy measure

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26

Pícha, Kamil, Lucie Tichá, Sanat Chuponov, Jasur Ataev, Dilshod Hudayberganov, and Bekhzod Kuziboev. "The Volatility Spillover of Global Oil Price Uncertainty." International Journal of Energy Economics and Policy 14, no. 3 (2024): 619–24. http://dx.doi.org/10.32479/ijeep.15803.

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This manuscript, for the first time, analyses the volatility spillover of oil price uncertainty in the world using data from oil price uncertainty recently developed by Abdul and Qureshi (2023), spanning the time 1996-2019 on a monthly frequency. ARCH/GARCH (Autoregressive Conditional Heteroskedasticity and Generalized Autoregressive Conditional Heteroskedasticity) models are employed as an econometric tool. The findings suggest that ARCH model is more consistent than GARCH model in assessing the volatility of oil price uncertainty in the world. The results show that the volatility of oil pric

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27

Ogutu, Carolyn, Betuel Canhanga, and Pitos Biganda. "Modeling Exchange Rate Volatility using APARCH Models." Journal of the Institute of Engineering 14, no. 1 (2018): 96–106. http://dx.doi.org/10.3126/jie.v14i1.20072.

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Анотація:

ARCH (Autoregressive Conditional Heteroskedacity) and GARCH (Generalized Autoregressive Conditional Heteroskedacity) models have been used in forecasting fluctuations in exchange rates, commodities and securities and are appropriate for modeling time series in which there is non-constant variance, and in which the variance at one time period is dependent on the variance at a previous time period. In our paper we deal with APARCH models (Arithmetic Power Autoregressive Conditional Heteroskedasticity) in order to fit into a data series with asymmetric characteristics. We use Kenyan, Tanzanian an

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28

Aldeki, R. G. "Predicting Financial Market Volatility with Modern Model and Traditional Model." Finance: Theory and Practice 29, no. 2 (2025): 154–65. https://doi.org/10.26794/2587-5671-2025-29-2-154-165.

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The major topic investigates how classical methods (ARCH and GARCH) and well-known machine learning algorithms, support vector regression, and hybrid methods. This paper aims to predict and forecast volatility to develop a two-stage forecasting approach the volatility of the Amman Stock Exchange Index (ASE) effectively. Additionally, the effectiveness of the machine learning techniques’ selection and utilization of information in stock data is evaluated. Methods the semiparametric estimating technique known as support vector regression (SVR) has been widely used for the prediction of volatilit

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Halim, Siana, Shirley Adelia, and Jani Rahardjo. "MODEL MATEMATIK UNTUK MENENTUKAN NILAI TUKAR MATA UANG RUPIAH TERHADAP DOLLAR AMERIKA." Jurnal Teknik Industri 1, no. 1 (2004): 30–40. http://dx.doi.org/10.9744/jti.1.1.30-40.

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The main objective of this paper is to estimate parameters in the heteroskedasticity models, particularly in Auto Regressive Conditional Heteroskedasticity - ARCH(1) and Generalized Autoregressive Conditional Heteroskedasticity- GARCH(1,1). These models will be used to fit, to forecast and to update the volatility of Rupiah Vs US.Dollar rate.  In order to get the estimation of fitting and updating parameters of ARCH(1) and GARCH(1,1), here will be used iterative method which is derived from the standard maximum likelihood estimation and the initial values are taken from the result of Yul

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30

Tiara Kania Ladzuardini. "Volatilitas Imbal Hasil Saham dan Kaitannya dengan Harga Minyak Dunia (Pendekatan Model ARCH/GARCH dan VAR)." JURNAL RISET MANAJEMEN DAN EKONOMI (JRIME) 1, no. 4 (2023): 97–116. http://dx.doi.org/10.54066/jrime-itb.v1i4.723.

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Анотація:

Volatility generally refers to the amount of uncertainty or risk associated with changes in a security's value. The value of a security can potentially spread over a wider range of values if it has higher volatility. This study aims to analyze the volatility of stock returns and the growth of world oil prices. The stock that the author analyzes in this scientific article is PT Pakuwon Jati Tbk. by using daily time series data from January 2 2019 to December 14 2020. The model used in this study is Autoregressive Conditional Heteroskedasticity (ARCH)/Generalized Autoregressive Conditional Heter

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31

Yang, Xiaorong, Chun He, and Jie Chen. "Several Extended CAViaR Models and Their Applications to the VaR Forecasting of the Security Markets." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 4 (2016): 590–96. http://dx.doi.org/10.20965/jaciii.2016.p0590.

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Анотація:

The conditional autoregressive Value-at-Risk (CAViaR) model, as a conditional autoregressive specification for calculating the Value-at-Risk (VaR) of the security market, has been receiving more and more attentions in recent years. As asymmetry may have a significant influence on the markets and the returns may have an autoregressive mean, this study proposes some extended CAViaR models, including asymmetric indirect threshold autoregressive conditional heteroskedasticity (TARCH) model and indirect generalized autoregressive conditional heteroskedasticity (GARCH) model with an autoregressive m

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32

Budiandru, Budiandru. "ARCH and GARCH Models on the Indonesian Sharia Stock Index." JURNAL AKUNTANSI DAN KEUANGAN ISLAM 9, no. 1 (2021): 27–38. http://dx.doi.org/10.35836/jakis.v9i1.214.

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Анотація:

Investments in Islamic stocks are in demand because of the profit-sharing system so that the company is more stable in facing uncertain global economic conditions. This study aims to analyze the volatility of the Indonesian Sharia Stock Index and the Indonesian Sharia Stock Index's potential in the future. We use daily data from 2012 to 2020 and the Autoregressive Conditionally Heteroscedasticity-Generalized Autoregressive Conditional Heteroskedasticity (ARCH-GARCH) method. The results show that the Indonesian Sharia Stock Index's volatility is influenced by the risk of the two previous period

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33

Budiandru, Budiandru. "Dynamic Volatility Modeling of Indonesian Insurance Company Stocks." Jurnal Ekonomi dan Studi Pembangunan 14, no. 1 (2022): 1. http://dx.doi.org/10.17977/um002v14i12022p001.

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Анотація:

The Indonesian capital market is one of the investment destination countries for investors in developed countries. The development of economic conditions in Indonesia itself is considered suitable for investors to invest. Insurance sector stocks are one of the sectors that are the target of investors. This study predicts the share price of insurance companies. Data in daily form from 2010 to 2020 uses the Autoregressive Conditional Heteroskedasticity - Generalized Autoregressive Conditional Heteroscedasticity (ARCH - GARCH) method. The results showed that forecasting that was carried out until

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34

Dalimunthe, Desy Yuliana, Elyas Kustiawan, Khadijah -, Niken Halim, and Helen Suhendra. "VOLATILITY ANALYSIS AND INFLATION PREDICTION IN PANGKALPINANG USING ARCH GARCH MODEL." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 1 (2025): 237–44. https://doi.org/10.30598/barekengvol19iss1pp237-244.

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Анотація:

One of the concerns of both developed and developing countries, as well as in a region, is the amount of inflation that occurs. Inflation is a serious problem. Inflation is a macroeconomic variable that affects people's welfare and is defined as a complex phenomenon resulting from general and continuous price increases. This research aims to analyze the volatility and projected value of the inflation rate, especially in Pangkalpinang City, using the Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. This research u

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35

Kalaitzi, Athanasia Stylianou, and Evgenia Stylianou Kalaitzi. "Forecasting Gasoline Market Volatility using Non-Linear Time Series Models." International Journal of Energy Economics and Policy 15, no. 4 (2025): 139–51. https://doi.org/10.32479/ijeep.18825.

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This study forecasts the dynamics of gasoline price returns using daily data from January 2, 1992, to June 6, 2022, and crude oil price returns as a regressor. The non-linear dependence in the volatility of the gasoline return is confirmed and the Markov Switching (MS), the autoregressive conditional heteroskedasticity (ARCH) and the generalized autoregressive conditional heteroskedasticity (GARCH) models are estimated. To account for the linear dependence found in the initial estimates, a GARCH (1,1) model with lagged gasoline returns is used, while a GARCH (1,1) is fitted on the Markov switc

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36

Ukemenam, Angela Ifeanyi, Babatunde Opadeji, Tuwe Soro Garbobiya, and Augustine Ujunwa. "Macroeconomic Effects of Exogenous Oil Price Shock in Nigeria: Persistent or Transitory." International Journal of Economics and Finance 10, no. 11 (2018): 28. http://dx.doi.org/10.5539/ijef.v10n11p28.

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Анотація:

This paper examines the macroeconomic effects of exogenous oil price shock in Nigeria. The paper additionally investigates the symmetric effects of oil price shock and the persistence and/or transitory nature of the shock. To achieve these objectives, the Generalised autoregressive conditional heteroskedasticity (GARCH), Component generalised autoregressive conditional heteroskedasticity (CGARCH) and Exponential generalized autoregressive conditional heteroskedasticity (EGARCH) were employed to estimate the various equations. The results showed that oil price volatility has significant positiv

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37

Bangar Raju, Totakura, Ayush Bavise, Pradeep Chauhan, and Bhavana Venkata Ramalingeswar Rao. "Analysing volatility spillovers between grain and freight markets." Pomorstvo 34, no. 2 (2020): 428–37. http://dx.doi.org/10.31217/p.34.2.23.

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Анотація:

The International Grain Council (IGC) circulates two price indices which are the Grain and Oilseeds Index (GOI) and the Grain and Oilseeds Freight Market Index (GOFI). These two indices indicate the respective market prices. The GOI markets are affected by various factors like supply and demand, weather, freight markets, etc. This research article attempts to explore and analyse volatility in GOI and GOFI markets using various GARCH family models, that is Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) analysis. The multivariate Dynamic Conditional Correlation Ge

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38

Ahmar, Ansari Saleh, Salim Al Idrus, and Asmar. "Analyzing Rupiah-USD Exchange Rate Dynamics: A Study with ARCH and GARCH Models." JOIV : International Journal on Informatics Visualization 8, no. 3-2 (2024): 1802. https://doi.org/10.62527/joiv.8.3-2.3251.

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Анотація:

The study aims to analyze the volatility of the Rupiah-USD exchange rate and predict future fluctuations using the Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The exchange rate data, spanning from January 2010 to December 2023, is sourced from Bank Indonesia (BI) and adheres to the Jakarta Interbank Spot Dollar Rate (JISDOR) regulations, focusing solely on business days. ARCH and GARCH models are widely applied in financial time series analysis because they capture and forecast time-varying volatility. This

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39

Wang, W., P. H. A. J. M. Van Gelder, J. K. Vrijling, and J. Ma. "Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes." Nonlinear Processes in Geophysics 12, no. 1 (2005): 55–66. http://dx.doi.org/10.5194/npg-12-55-2005.

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Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity) effect), a nonlinear phenomenon of the variance behaviour, in the residual ser

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40

Sung, Sang-Ha, Jong-Min Kim, Byung-Kwon Park, and Sangjin Kim. "A Study on Cryptocurrency Log-Return Price Prediction Using Multivariate Time-Series Model." Axioms 11, no. 9 (2022): 448. http://dx.doi.org/10.3390/axioms11090448.

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Анотація:

Cryptocurrencies are highly volatile investment assets and are difficult to predict. In this study, various cryptocurrency data are used as features to predict the log-return price of major cryptocurrencies. The original contribution of this study is the selection of the most influential major features for each cryptocurrency using the volatility features of cryptocurrency, derived from the autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) models, along with the closing price of the cryptocurrency. In addition, we sought

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41

Roni, Bhowmik, Chao Wu, Roy Kumar Jewel, and Shouyang Wang. "A Study on the Volatility of the Bangladesh Stock Market — Based on GARCH Type Models." Journal of Systems Science and Information 5, no. 3 (2017): 193–215. http://dx.doi.org/10.21078/jssi-2017-193-23.

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Анотація:

Abstract The generalized autoregressive conditional heteroskedasticity (GARCH) type models are used to investigate the volatility of Bangladesh stock market. The findings of the study demonstrate that the index volatility characteristics changes over time. The article shows that the data are divided into three sub-periods: pre crisis, crisis, and post crisis. Accordingly, the results of the findings indicate changes in the GARCH-type models parameter, risk premium and persistence of volatility in different periods. A significant “low-yield associated with high-risk” phenomenon is detected in t

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42

Dzingirai, Canicio, and Nixon S. Chekenya. "Longevity swaps for longevity risk management in life insurance products." Journal of Risk Finance 21, no. 3 (2020): 253–69. http://dx.doi.org/10.1108/jrf-05-2019-0085.

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Анотація:

Purpose The life insurance industry has been exposed to high levels of longevity risk born from the mismatch between realized mortality trends and anticipated forecast. Annuity providers are exposed to extended periods of annuity payments. There are no immediate instruments in the market to counter the risk directly. This paper aims to develop appropriate instruments for hedging longevity risk and providing an insight on how existing products can be tailor-made to effectively immunize portfolios consisting of life insurance using a cointegration vector error correction model with regime-switch

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43

Mukhaiyar, Utriweni, and Syahri Ramadhani. "The Generalized STAR Modeling with Heteroscedastic Effects." CAUCHY 7, no. 2 (2022): 158–72. http://dx.doi.org/10.18860/ca.v7i2.13097.

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Анотація:

In general, the Generalized Space Time Autoregressive (GSTAR) model of space-time assumes constant error variance. In this study, a GSTAR model was built with an error variance that was not constant or had a heteroscedasticity effect, namely the combination of GSTAR–Autoregressive Conditional Heteroskedasticity (ARCH). The parameters of the GSTAR–ARCH model were estimated using the Generalized Least Square (GLS) method to obtain an efficient parameter estimation. As a case study, the GSTAR–ARCH model was applied to the daily mean wind speed data of New Orleans, Florida and Mississippi to predi

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44

Kumar, Surender, Moon MoonHaque, and Prashant Sharma. "Volatility Spillovers across Major Emerging Stock Markets." Asia-Pacific Journal of Management Research and Innovation 13, no. 1-2 (2017): 13–33. http://dx.doi.org/10.1177/2319510x17740043.

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Анотація:

Emerging stock markets of Asia have become a matter of interest for international financial researchers and policy-makers during the last couple of decades. Series of reforms, increasing financial transparency and decreasing restrictions on transactions have made these markets better diversification opportunities for international investors. This paper examines independently as well the linkages of stock markets across the selected Asian countries. The volatility spillover is modelled through an asymmetric multivariate generalized autoregressive conditional heteroscedastic model. In large numb

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45

Sulistiowati, Dwi, Maya Sari Syahrul, and Ilham Dangu Rianjaya. "Risk Analysis of Gold Sale Price and Investment of Antam Shares Using Expected Shortfall in Pandemic Covid-19." Jurnal Matematika, Statistika dan Komputasi 17, no. 3 (2021): 428–37. http://dx.doi.org/10.20956/j.v17i3.12779.

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Анотація:

The Covid-19 pandemic caused the price of gold produced by PT Aneka Tambang (Antam) to experience a high increase following the world gold price, while stock investment decreased. Measuring risk is significant in financial analysis; this is related to investment funds, which are quite large and narrow about public funds. This study analyzes the risk data on Antam gold price and Antam stock closing price with an estimated Shortfall (ES). The method used to measure the risk of investing in stocks is ES. ES is the expectation of a conditional loss that exceeds Value at Risk (VaR). To compute ES d

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46

Rohilla, Dr Amit. "Exploring Volatility: Evolution, Advancements, Trends, and Applications." Indian Journal of Economics and Finance 3, no. 2 (2023): 73–79. http://dx.doi.org/10.54105/ijef.a2570.03021123.

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Анотація:

Volatility is a fundamental notion in financial markets, influencing investment decisions, risk management techniques, and market dynamics. This paper provides a thorough overview of the historical evolution and practical implications of volatility, focusing on important works and key advancements in the field. The overview begins with early conceptions of volatility and the necessity for measurement prompted by market collapses, then progresses to advanced quantitative models and computer tools. The study includes key innovations such as the Black-Scholes model, which revolutionized options p

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47

Matei, Rovira, and Agell. "Bivariate Volatility Modeling with High-Frequency Data." Econometrics 7, no. 3 (2019): 41. http://dx.doi.org/10.3390/econometrics7030041.

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Анотація:

We propose a methodology to include night volatility estimates in the day volatility modeling problem with high-frequency data in a realized generalized autoregressive conditional heteroskedasticity (GARCH) framework, which takes advantage of the natural relationship between the realized measure and the conditional variance. This improves volatility modeling by adding, in a two-factor structure, information on latent processes that occur while markets are closed but captures the leverage effect and maintains a mathematical structure that facilitates volatility estimation. A class of bivariate

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48

Mamilla, Rajesh, Chinnadurai Kathiravan, Aidin Salamzadeh, Léo-Paul Dana, and Mohamed Elheddad. "COVID-19 Pandemic and Indices Volatility: Evidence from GARCH Models." Journal of Risk and Financial Management 16, no. 10 (2023): 447. http://dx.doi.org/10.3390/jrfm16100447.

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Анотація:

This study examines the impact of volatility on the returns of nine National Stock Exchange (NSE) indices before, during, and after the COVID-19 pandemic. The study employed generalized autoregressive conditional heteroskedasticity (GARCH) modelling to analyse investor risk and the impact of volatility on returns. The study makes several contributions to the existing literature. First, it uses advanced volatility forecasting models, such as ARCH and GARCH, to improve volatility estimates and anticipate future volatility. Second, it enhances the analysis of index return volatility. The study fo

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49

Monir, Mostafa, Mohammad Asrarul Hasanat, Jahedul Islam, and S. M. Sayem. "Exchange Rate Volatility in Bangladesh: An Exploration of the Leverage Effect of Positive and Negative Economic News." South Asian Journal of Social Sciences and Humanities 6, no. 3 (2025): 119–40. https://doi.org/10.48165/sajssh.2024.6307.

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Анотація:

Purpose of the Study: This research investigates the impact of leverage on exchange rate fluctuations in Bangladesh, with a specific focus on assessing whether negative news about the exchange rate generates a greater effect on volatility compared to positive news. Methodology: The study employs the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) (1,1) model to analyze monthly BDT/USD exchange rate data from January 1982 to May 2022. This approach captures the autoregressive conditional heteroskedasticity in the data, allowing for the assessment of volatility pat

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50

Dharma, Yuki Dwi, and Asri Utami. "Volatility Forecasting Using GARCH Versus EGARCH Models for Cryptocurrencies, Indonesian Stocks, and U.S. Stocks." IJBE (Integrated Journal of Business and Economics) 9, no. 2 (2025): 332. https://doi.org/10.33019/ijbe.v9i2.1125.

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Анотація:

This study examines and compares the effectiveness of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and EGARCH (Exponential GARCH) models in forecasting volatility across three distinct financial markets: cryptocurrencies, Indonesian stocks, and U.S. stocks. The research analyzes daily closing price data from April 2018 to September 2024, focusing on five major cryptocurrencies (Bitcoin, Ethereum, Tether, Binance Coin, and Ripple), five Indonesian blue-chip stocks (BBCA, BBRI, BYAN, BMRI, and TPIA), and five major U.S. stocks (Apple, Nvidia, Microsoft, Google, and Amazon).

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