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Journal articles on the topic 'Autoregressive Conditional Heteroskedasticity (ARCH)'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Jati, Kumara. "ANALISIS EFEK MUSIM HUJAN DAN KEMARAU TERHADAP HARGA BERAS." Jurnal Manajemen Industri dan Logistik 2, no. 1 (2018): 40–51. http://dx.doi.org/10.30988/jmil.v2i1.24.

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This study analyzes the effects of the rainy and dry seasons on rice prices. Autoregressive and Moving Average (ARMA) and Autoregressive Conditional Heteroskedasticity / Generalized Autoregressive Conditional Heteroskedasticity (ARCH / GARCH) with a dummy variable. We used daily data of the stock and the price of rice from January 29, 2014 until January 29, 2018. ARMA (0,1)-ARCH (1) model with dummy variable that is dry season is more influence conditional variance of rice price compared with rainy season dummy variable. Stakeholders need to pay more attention to fluctuations in rice prices, e

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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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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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Diebold, Francis X., Steve C. Lim, and C. Jevons Lee. "A Note on Conditional Heteroskedasticity in the Market Model." Journal of Accounting, Auditing & Finance 8, no. 2 (1993): 141–50. http://dx.doi.org/10.1177/0148558x9300800203.

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We examine the usefulness and implications of modeling conditional heteroskedasticity in market model residual returns. Autoregressive conditional heteroskedasticity (ARCH) plays a key role in our approach. To highlight the salient issues, we first provide a case study of one firm, Winn-Dixie Stores. Formal testing procedures reveal strong ARCH effects. ARCH models are then estimated and used to infer the pattern of time-varying volatility; differences in parameter estimates caused by use of the fully efficient estimator are also noted. Next, we provide a systematic examination of the entire N

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Jati (Kementerian Perdagangan), Kumara. "ANALISIS EFEK MUSIM HUJAN DAN KEMARAU TERHADAP HARGA BERAS." JURNAL MANAJEMEN INDUSTRI DAN LOGISTIK 2, no. 1 (2018): 37. http://dx.doi.org/10.30988/jmil.v2i1.68.

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<p><em>Penelitian ini menganalisis efek dari musim hujan dan kemarau terhadap harga beras. Metode yang digunakan yaitu Autoregressive and Moving Average (ARMA) dan Autoregressive Conditional Heteroskedasticity/Generalized Autoregressive Conditional Heteroskedasticity (ARCH/GARCH) dengan variabel dummy. Data yang digunakan yaitu stok dan harga beras harian dari 29 Januari 2014 sampai dengan 29 Januari 2018. Penggunaan model ARMA-ARCH/GARCH dapat menunjukkan bahwa model ini bisa untuk membantu melihat pola pergerakan harga beras. Model ARMA (0,1)-ARCH (1) dengan variabel dummy yaitu

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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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Venkateswara Rao, K., D. Srilatha, D. Jagan Mohan Reddy, Venkata Subbaiah Desanamukula, and Mandefro Legesse Kejela. "Regression Based Price Prediction of Staple Food Materials Using Multivariate Models." Scientific Programming 2022 (June 13, 2022): 1–7. http://dx.doi.org/10.1155/2022/4572064.

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Profit margins for essential foodstuffs could be a demand rising problem. There are several variables influencing currency fluctuations. For example, the various variables of commodity food prices are climate, crude prices, and so on. Forecasting the fluctuating prices of basic foodstuffs is also relevant even for the government, producers, and customers. The article will use ARCH (autoregressive conditional heteroskedasticity) to forecast the essential food market considering external conditions. The findings agree well enough with the assessment price in the industry by employing two main ap

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Sulistiowati, Dwi, Maya Sari Syahrul, and Iswan Rina. "Pemodelan Harga Saham Menggunakan Arma-Garch." Jurnal Penelitian Dan Pengkajian Ilmiah Eksakta 1, no. 2 (2022): 89–93. http://dx.doi.org/10.47233/jppie.v1i2.532.

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Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were used for modeling with heteroscedasticity data. This study aims to determine the time series model on the stock price data of PT Triputra Agro Persada Tbk. (TAPG) with modeling ARMA, ARCH and GARCH. Based on the smallest Akaike Information Criterion (AIC) and Schwarz Criterion (SC), it shows that the ARMA(1,0)-GARCH(2,1) model is the best model for predicting the value of TAPG stock prices.

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Hokayem, Jihad El, Joseph Gemayel, and Dany Mezher. "Forecasting Oil Prices: A Comparative Study." International Journal of Economics and Finance 14, no. 7 (2022): 55. http://dx.doi.org/10.5539/ijef.v14n7p55.

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Oil prices have been a major concern for many policy makers, businesses and individuals throughout the years. The spillover of inflation, which is at its highest level since several decades, due to the supply chain problems and spike in energy prices, following the war between Russia and Ukraine pushed oil and gas under the spotlight recognizing its crucial role. In turn, this has imposed many challenges on numerous countries across regions building up feelings of fear and anxiety amid serious concerns about energy and food security. Forecasting oil prices is still a major challenge as it is s

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Dwi Murniati, Ni Luh Ketut, Indwiarti Indwiarti, and Aniq Atiqi Rohmawati. "Implemetasi Model Autoregressive (AR) Dan Autoregressive Conditional Heteroskedasticity (ARCH) Untuk Memprediksi Harga Emas." Indonesian Journal on Computing (Indo-JC) 3, no. 2 (2018): 29. http://dx.doi.org/10.21108/indojc.2018.3.2.225.

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Gold is a one of high selling value items in the market, and it can be used as an investment item. The price of gold in the market tends to be stable and not undergoing too significant changes which makes gold be a very valuable item. The aim of this research is to predict gold price using AR (1) and ARCH (1) model which are the part of time series methods. The data of gold price is obtained from ANTAM's daily historical website from 2007 - 2017. Here, the basic information about data is given by using descriptive statistic and the estimation of parameters in each model is condacted by using &

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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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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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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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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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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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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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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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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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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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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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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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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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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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T., Mohamed Nishad, and Thomachan K.T. "HOW VOLATILE IS INDIAN STOCK MARKET? A STUDY BASED ON SELECTED SECTORAL INDICES." International Journal of Research – Granthaalayah 3, no. 12 (2017): 142–49. https://doi.org/10.5281/zenodo.848963.

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This paper examines the nature of volatility of selected sectoral stock indices traded in the National Stock Exchange. Using the EGARCH model introduced by Nelson, it has been observed that the selected indices are subject to Autoregressive Conditional Heteroskedasticity (ARCH) effects. There are significant leverage effects in the case of five indices. Volatility seems to be highly persistent in the case of all the indices except one. Moreover, four indices are highly sensitive to market events.

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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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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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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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Broda, Simon, and Marc S. Paolella. "ARCHModels.jl: Estimating ARCH Models in Julia." Journal of Statistical Software 107, no. 5 (2023): 1–25. https://doi.org/10.5281/zenodo.10682941.

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This paper introduces ARCHModels.jl, a package for the Julia programming language that implements a number of univariate and multivariate autoregressive conditional heteroskedasticity models. This model class is the workhorse tool for modeling the conditional volatility of financial assets. The distinguishing feature of these models is that they model the latent volatility as a (deterministic) function of past returns and volatilities. This recursive structure results in loop-heavy code which, due to its just-in-time compiler, Julia is well-equipped to handle. As such, the entire package is wr

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ROMAN, H. E., and M. PORTO. "FRACTIONAL BROWNIAN MOTION WITH STOCHASTIC VARIANCE: MODELING ABSOLUTE RETURNS IN STOCK MARKETS." International Journal of Modern Physics C 19, no. 08 (2008): 1221–42. http://dx.doi.org/10.1142/s0129183108012820.

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We discuss a model for simulating a long-time memory in time series characterized in addition by a stochastic variance. The model is based on a combination of fractional Brownian motion (FBM) concepts, for dealing with the long-time memory, with an autoregressive scheme with conditional heteroskedasticity (ARCH), responsible for the stochastic variance of the series, and is denoted as FBMARCH. Unlike well-known fractionally integrated autoregressive models, FBMARCH admits finite second moments. The resulting probability distribution functions have power-law tails with exponents similar to ARCH

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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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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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Rehman, Asad Ur. "Examining the Dynamics of Unemployment, GDP, Inflation, Government Education Expenditure, and Foreign Direct Investment in Pakistan's Transitioning Economy: An Econometric Analysis." Research Letters 2, no. 1 (2025): 66–72. https://doi.org/10.5281/zenodo.14802979.

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This study aims to analyze the interrelationship between unemployment, GDP, inflation, government expenditure on education, and foreign direct investment (FDI) in Pakistan’s steadily transitioning economy. To achieve this objective, the study employs both descriptive and econometric analyses, utilizing Ordinary Least Squares (OLS), Autoregressive Conditional Heteroskedasticity (ARCH), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The analysis is based on annual time series data spanning from 1990 to 2023. The results indicate a negative relationship betwee

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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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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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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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Nihal Zaidi, Ataulla. "Wavelets in the Analysis of Autoregressive Conditional Heteroskedasticity (ARCH) Models Using Neural Network." American Journal of Applied Mathematics 4, no. 2 (2016): 92. http://dx.doi.org/10.11648/j.ajam.20160402.14.

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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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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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Mohamed, Nishad, and K. T. Thomachan. "HOW VOLATILE IS INDIAN STOCK MARKET? A STUDY BASED ON SELECTED SECTORAL INDICES." International Journal of Research -GRANTHAALAYAH 3, no. 12 (2015): 142–49. http://dx.doi.org/10.29121/granthaalayah.v3.i12.2015.2899.

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This paper examines the nature of volatility of selected sectoral stock indices traded in the National Stock Exchange. Using the EGARCH model introduced by Nelson, it has been observed that the selected indices are subject to Autoregressive Conditional Heteroskedasticity (ARCH) effects. There are significant leverage effects in the case of five indices. Volatility seems to be highly persistent in the case of all the indices except one. Moreover, four indices are highly sensitive to market events.

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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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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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Hörmann, Siegfried, Lajos Horváth, and Ron Reeder. "A FUNCTIONAL VERSION OF THE ARCH MODEL." Econometric Theory 29, no. 2 (2012): 267–88. http://dx.doi.org/10.1017/s0266466612000345.

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Improvements in data acquisition and processing techniques have led to an almost continuous flow of information for financial data. High-resolution tick data are available and can be quite conveniently described by a continuous-time process. It is therefore natural to ask for possible extensions of financial time series models to a functional setup. In this paper we propose a functional version of the popular autoregressive conditional heteroskedasticity model. We will establish conditions for the existence of a strictly stationary solution, derive weak dependence and moment conditions, show c

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Ardiyanti, Septika Tri. "THE IMPACT OF REAL EXCHANGE RATE VOLATILITY ON INDONESIA-US TRADE PERFORMANCE." Buletin Ilmiah Litbang Perdagangan 9, no. 1 (2015): 79–93. http://dx.doi.org/10.30908/bilp.v9i1.17.

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Studi ini mengkaji dampak volatilitas nilai tukar riil terhadap kinerja perdagangan bilateral Indonesia-Amerika Serikat (AS), dengan menggunakan data periode Q1:1990 sampai dengan Q3:2012. Studi ini menggunakan dua pendekatan untuk mengukur volatilitas nilai tukar riil, yaitu model Autoregressive Conditional Heteroskedasticity (ARCH-1) dan metode Moving Average Standards Deviation (MASD). Untuk menguji hubungan jangka panjang antara variabel penelitian, digunakan prosedur Autoregressive Distributed Lag (ARDL) bounds testing. Hasil analisis menunjukkan bahwa volatilitas nilai tukar riil berpeng

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Singh, Amit Kumar, Rajat Agarwal, and Rohit Kumar Shrivastav. "Returns and Volatility Spillover Between BSE SENSEX and BSE SME Stock Exchange of India." SEDME (Small Enterprises Development, Management & Extension Journal): A worldwide window on MSME Studies 48, no. 3 (2021): 257–71. http://dx.doi.org/10.1177/09708464211070054.

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Investigating the impact of volatility spillover among various markets has been the subject matter of numerous research. This study investigates the dynamic relationship between the Bombay Stock Exchange index (SENSEX) and the small and medium enterprises (SME) stock index (BSE SME) in India. The study uses univariate autoregressive conditional heteroskedasticity (ARCH)/generalised autoregressive conditional heteroskedasticity (GARCH) models to model the time-varying volatility of the BSE SME market and multivariate BEKK-GARCH analysis to model the volatility of the SENSEX and BSE SME Index co

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Hong, Yongmiao, and Jin Lee. "ONE-SIDED TESTING FOR ARCH EFFECTS USING WAVELETS." Econometric Theory 17, no. 6 (2001): 1051–81. http://dx.doi.org/10.1017/s0266466601176024.

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There has been increasing interest recently in hypothesis testing with inequality restrictions. An important example in time series econometrics is hypotheses on autoregressive conditional heteroskedasticity (ARCH). We propose a one-sided test for ARCH effects using a wavelet spectral density estimator at frequency zero of a squared regression residual series. The square of an ARCH process is positively correlated at all lags, resulting in a spectral mode at frequency zero. In particular, it has a spectral peak at frequency zero when ARCH effects are persistent or when ARCH effects are small a

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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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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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Krawiec, Monika, and Anna Górska. "Are soft commodities markets affected by the Halloween effect?" Agricultural Economics (Zemědělská ekonomika) 67, No. 12 (2021): 491–99. http://dx.doi.org/10.17221/216/2021-agricecon.

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Within the last three decades commodity markets, including soft commodities markets, have become more and more like financial markets. As a result, prices of commodities may exhibit similar patterns or anomalies as those observed in the behaviour of different financial assets. Their existence may cast doubts on the competitiveness and efficiency of commodity markets. It motivates us to conduct the research presented in this paper, aimed at examining the Halloween effect in the markets of basic soft commodities (cocoa, coffee, cotton, frozen concentrated orange juice, rubber and sugar) from 199

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Abdul Rahman, Nur Haizum, Goh Hui Jia, and Hani Syahida Zulkafli. "GARCH Models and Distributions Comparison for Nonlinear Time Series with Volatilities." Malaysian Journal of Fundamental and Applied Sciences 19, no. 6 (2023): 989–1001. http://dx.doi.org/10.11113/mjfas.v19n6.3101.

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The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is extensively used for handling volatilities. However, with numerous extensions to the standard GARCH model, selecting the most suitable model for forecasting price volatilities becomes challenging. This study aims to examine the performance of different GARCH models in forecasting crude oil price volatilities using West Texas Intermediate (WTI) data. The models considered are the standard GARCH, Integrated GARCH (IGARCH), Exponential GARCH (EGARCH), and Golsten, Jagannathan, and Runkle GARCH (GJR-GARCH), each with no

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Folorunso Sunday Ayadi and Olubunmi Elizabeth Oluwagbemi. "Oil Export Earnings, Exchange Rate Variability, and Economic Growth in Nigeria." International Journal of Sustainable Economies Management 3, no. 4 (2014): 11–23. http://dx.doi.org/10.4018/ijsem.2014100102.

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This paper investigates oil revenue and exchange rate volatility and as well as their impacts on Nigerian economic growth which is examined from 1980 – 2010. Exchange rate volatility was captured using standard deviationof monthly nominal effective exchange rate. During this period, Nigeria recorded high levels of volatility (in oil receipt and effective exchange rate) as can be seen from the Autoregressive Conditional Heteroskedasticity (ARCH) and the General Autoregressive Conditional Heteroskedasticity (GARCH) - ARCH/GARCH results. Also, the Augmented Dickey-Fuller test indicate that some o

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Yang, Mingxuan. "Stock Price prediction based on AR-ARCH model: A Case Study of ICBC." BCP Business & Management 38 (March 2, 2023): 766–74. http://dx.doi.org/10.54691/bcpbm.v38i.3772.

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Accompanying the progress of economy and financial market, the stock market has become one of the investment channels for many people. Therefore, it is of great practical significance to forecast the stock price with High accuracy. Based on the model of Autoregression (AR) and Autoregressive conditional heteroskedasticity (ARCH) models, this paper collects the daily closing share price of Industrial and Commercial Bank of China from 1st January 2018, to 1st June 2021. An AR-ARCH model is established to analyze the model based on the time series under the difference method, and then to forecast

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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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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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Aliyu Umar Shelleng, Yahaya Jamil Sule, Jibrin Yahaya Kajuru, and Adamu Kabiru. "Comparative Study of Lee Carter and Arch Model in Modelling Female Mortality in Nigeria." UMYU Scientifica 1, no. 2 (2022): 96–100. http://dx.doi.org/10.56919/usci.1222.012.

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Using Nigeria mortality data from 2009 to 2020, this study compares and contrasts how well the Lee-Carter and ARCH models performed. Singular value decomposition (SVD) method, Langrage multiplier test, and autoregressive conditional heteroskedasticity (ARCH) effects were examined. Five (5) different ARIMA and ARCH models were fitted together with their criteria, i.e., AIC and BIC in order to determine the best model for Nigeria mortality data. ARIMA (0,1,0) had the lowest AIC and BIC values, and was determined to be the best ARIMA model. The mortality index is then modelled using ARIMA (0,1,0)

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Angraini, Yenni, Adelia Putri Pangestika, and I. Made Sumertajaya. "Comparison of the Symmetric and Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models in Forecasting the 2018-2023 Jakarta Composite Index." ComTech: Computer, Mathematics and Engineering Applications 15, no. 1 (2024): 1–15. http://dx.doi.org/10.21512/comtech.v15i1.10610.

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The Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) method assumes a homogeneous residual variance, but data with high volatility can cause violations of this assumption. Hence, it is interesting to compare the forecasting accuracy of symmetric and asymmetric Autoregressive Conditional Heteroskedasticity (ARCH) models in various data conditions. The research aimed to compare the accuracy of the symmetric ARCH/ Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and asymmetric TGARCH models in forecasting weekly Jakarta Composite Index (JCI) data on Janu

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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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Dr. Madhur Jain, Shilpi Jain, and Ankit Gupta. "Decoding Stocks Patterns Using LSTM." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (2024): 306–10. http://dx.doi.org/10.32628/cseit2410328.

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Decoding stocks is extensively utilized in the financial sector by numerous organizations. It is volatile in nature, so it’s tough to predict the prices of stock. Numerous methodologies exist for tackling this task, including logistic regression, support vector machines (SVM), autoregressive conditional heteroskedasticity (ARCH) models, recurrent neural network (RNN), convolutional neural networks (CNN), backpropagation, Naïve Bayes, among others. Among these, Long Short-Term Memory (LSTM) stands out as particularly adept at handling time series data. The primary aim is to discern prevailing m

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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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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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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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