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Статті в журналах з теми "Generalized Autoregressive Conditional Heteroscedasticity"

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Автор: Grafiati

Опубліковано: 19 червня 2021

Оновлено: 8 лютого 2022

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1

Rossetti, Nara, Marcelo Seido Nagano, and Jorge Luis Faria Meirelles. "A behavioral analysis of the volatility of interbank interest rates in developed and emerging countries." Journal of Economics, Finance and Administrative Science 22, no. 42 (June 12, 2017): 99–128. http://dx.doi.org/10.1108/jefas-02-2017-0033.

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Purpose This paper aims to analyse the volatility of the fixed income market from 11 countries (Brazil, Russia, India, China, South Africa, Argentina, Chile, Mexico, USA, Germany and Japan) from January 2000 to December 2011 by examining the interbank interest rates from each market. Design/methodology/approach To the volatility of interest rates returns, the study used models of auto-regressive conditional heteroscedasticity, autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), exponential generalized autoregressive conditional heteroscedasticity (EGARCH), threshold generalized autoregressive conditional heteroscedasticity (TGARCH) and periodic generalized autoregressive conditional heteroscedasticity (PGARCH), and a combination of these with autoregressive integrated moving average (ARIMA) models, checking which of these processes were more efficient in capturing volatility of interest rates of each of the sample countries. Findings The results suggest that for most markets, studied volatility is best modelled by asymmetric GARCH processes – in this case the EGARCH – demonstrating that bad news leads to a higher increase in the volatility of these markets than good news. In addition, the causes of increased volatility seem to be more associated with events occurring internally in each country, as changes in macroeconomic policies, than the overall external events. Originality/value It is expected that this study has contributed to a better understanding of the volatility of interest rates and the main factors affecting this market.

2

Xiao, Zhijie, and Roger Koenker. "Conditional Quantile Estimation for Generalized Autoregressive Conditional Heteroscedasticity Models." Journal of the American Statistical Association 104, no. 488 (December 2009): 1696–712. http://dx.doi.org/10.1198/jasa.2009.tm09170.

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3

Zhang, Xibin, and Maxwell L. King. "Influence Diagnostics in Generalized Autoregressive Conditional Heteroscedasticity Processes." Journal of Business & Economic Statistics 23, no. 1 (January 2005): 118–29. http://dx.doi.org/10.1198/073500104000000217.

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4

Santi Singagerda, Faurani, Linda Septarina, and Anuar Sanusi. "The volatility model of the ASEAN Stock Indexes." Investment Management and Financial Innovations 16, no. 1 (March 18, 2019): 226–38. http://dx.doi.org/10.21511/imfi.16(1).2019.18.

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This research study examines the characteristics of the Association of Southeast Asian Nations (ASEAN) volatility of stock indexes. The following models are used in this research: Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH), Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity (FIGARCH), Glosten Jaganathan Runkle Generalized Autoregressive Conditional Heteroscedasticity (GJR-GARCH), and Multifractal Model of Asset Return (MMAR). The research also used the data from the ASEAN country members’ (the Philippines, Indonesia, Malaysia, Singapore, and Thailand) stock indexes for the period from January 2002 until 31 January 2016 to determine the suitable model.Meanwhile, the results of the MMAR parameter showed that the returns of the countries have a characteristic called long-term memory. The authors found that the scaling exponents are associated with the characteristics of the specific markets including the ASEAN member countries and can be used to differentiate markets in their stage of development. Finally, the simulated data are compared with the original data by scaling function where most of the stock markets of the selected ASEAN countries have long-term memory with the scaling behavior of information asymmetry. Some of the countries such as the Philippines and Indonesia have their own alternative models using GARCH and EGARCH due to the possibility of leverage. Generally, MMAR is the best model for use in ASEAN market, because this model considered Hurst exponent as a parameter of long-term memory that indicates persistent behavior.

5

Jiang, Wen, Zheng Yan, Dong-Han Feng, and Zhi Hu. "Wind speed forecasting using autoregressive moving average/generalized autoregressive conditional heteroscedasticity model." European Transactions on Electrical Power 22, no. 5 (June 24, 2011): 662–73. http://dx.doi.org/10.1002/etep.596.

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6

Otto, Philipp, Wolfgang Schmid, and Robert Garthoff. "Generalised spatial and spatiotemporal autoregressive conditional heteroscedasticity." Spatial Statistics 26 (August 2018): 125–45. http://dx.doi.org/10.1016/j.spasta.2018.07.005.

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7

Bahramgiri, Mohsen, Shahabeddin Gharaati, and Iman Dolatabadi. "Modeling jumps in organization of petroleum exporting countries basket price using generalized autoregressive heteroscedasticity and conditional jump." Investment Management and Financial Innovations 13, no. 4 (December 29, 2016): 196–202. http://dx.doi.org/10.21511/imfi.13(4-1).2016.05.

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This paper uses autoregressive jump intensity (ARJI) model to show that the oil price has both GARCH and conditional jump component. In fact, the distribution of oil prices is not normal, and oil price returns have conditional heteroskedasticity. Here the authors compare constant jump intensity with the dynamic jump intensity and evidences demonstrate that oil price returns have dynamic jump intensity. Therefore, there is strong evidence of time varying jump intensity Generalized Autoregressive Heteroscedasticity (GARCH) behavior in the oil price returns. The findings have several implications: first, it shows that oil price is highly sensitive to news, and it does settle around a trend in long-run. Second, the model separates variances of high volatilities from smooth volatilities. Third, the model rejects an optimal path for extracting oil and technology transmission. In fact, the lack of a long-term pattern can cause excessive oil extracting which can result in heavy climatic effects. Keywords: generalized autoregressive heteroscedasticity (GARCH), jumps, basket, oil price, Organization of Petroleum Exporting Countries (OPEC), Autoregre-ssive jump intensity (ARJI). JEL Classification: C32, C52, F31

8

Haris, M. Al. "PERAMALAN HARGA EMAS DENGAN MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (GARCH)." Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam 3, no. 1 (July 22, 2020): 19. http://dx.doi.org/10.32493/jsmu.v3i1.5263.

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9

Yip, Iris W. H., and Mike K. P. So. "Simplified specifications of a multivariate generalized autoregressive conditional heteroscedasticity model." Mathematics and Computers in Simulation 80, no. 2 (October 2009): 327–40. http://dx.doi.org/10.1016/j.matcom.2009.07.001.

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10

Aminul Isl, Mohd. "Applying Generalized Autoregressive Conditional Heteroscedasticity Models to Model Univariate Volatility." Journal of Applied Sciences 14, no. 7 (March 15, 2014): 641–50. http://dx.doi.org/10.3923/jas.2014.641.650.

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11

Tanjung, Hendri, and Taufik Akbar Martua Siregar. "Analisis Votalitas Saham di Jakarta Islamic Index (JII) periode Januari 2015-Januari 2018." Ihtifaz: Journal of Islamic Economics, Finance, and Banking 1, no. 1 (November 14, 2018): 147. http://dx.doi.org/10.12928/ijiefb.v1i1.270.

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Penelitian ini bertujuan untuk melihat volatilitas Jakarta Islamic Index (JII) pada Jakarta Stock Exchange. Adapun teknik analisis yang digunakan pada penelitian ini adalah Generalized Autoregressive Conditional Heteroscedasticity (GARCH) dan Autoregressive Conditional Heteroscedasticity (ARCH). Kenormalan distribusi tingkat return pada JII dianalisis untuk menjawab apakah returnnya tersebar secara normal atau tidak. Dengan menggunakan data JII dari januari 2015 sampai dengan januari 2018 (724 data harian), ditemukan bahwa distribusi dari return JII tidak menyebar normal. Penelitian ini menyimpulkan bahwa return dari Jakarta Islamic Indeks sangat berfluktuasi. Adapun implikasinya adalah akan diperoleh keuntungan yang sangat tinggi dan kerugian yang sangat besar pada satu hari.

12

Zhang, Guang Hui, Yang Gao, and Guo Yong Huang. "Research on Information Applied Technology with Analysis of Auction Data Fluctuations of Flowers Based on Generalized Autoregressive Conditional Heteroscedasticity." Advanced Materials Research 886 (January 2014): 541–45. http://dx.doi.org/10.4028/www.scientific.net/amr.886.541.

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In order to research on information applied technology with analysis the fluctuations of supply quantity, volume of trade, failed auction rate and price series in auction market, analysis the data from Kunming flowers auction market. The series have autoregressive conditional heteroscedasticity (ARCH) effect. Generalized autoregressive conditional heteroscedasticity (GARCH) model with normal distribution fits yield series, and EGARCH with General Error Distribution (GED) fits supply quantity and volume of trade change rate. EGARCH (1.1) with normal distribution fits change rate of failed auction rate. These results provide basis for forecasting change rate of supply quantity, volume of trade, failed auction rate and price in market.

13

Cicvarić, Branimir Cvitko. "Volatility of cryptocurrencies." Notitia 6, no. 1 (December 30, 2020): 13–23. http://dx.doi.org/10.32676/n.6.1.2.

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Many models have been developed to model, estimate and forecast financial time series volatility, amongst which are the most popular autoregressive conditional heteroscedasticity (ARCH) model introduced by Engle (1982) and generalized autoregressive conditional heteroscedasticity (GARCH) model introduced by Bollerslev (1986). The aim of this paper is to determine which type of ARCH/GARCH models can fit the best following cryptocurrencies: Ethereum, Neo, Ripple, Litecoin, Dash, Zcash and Dogecoin. It is found that the EGARCH model is the best fitted model for Ethereum, Zcash and Neo, PARCH model is the best fitted model for Ripple, while for Litecoin, Dash and Dogecoin it depends on the selected distribution and information criterion.

14

Hanifa, Rezky Dwi, Mustafid Mustafid, and Arief Rachman Hakim. "PEMODELAN AUTOREGRESSIVE FRACTIONALLY INTEGRATED MOVING AVERAGE DENGAN EFEK EXPONENTIAL GARCH (ARFIMA-EGARCH) UNTUK PREDIKSI HARGA BERAS DI KOTA SEMARANG." Jurnal Gaussian 10, no. 2 (May 31, 2021): 279–92. http://dx.doi.org/10.14710/j.gauss.v10i2.29933.

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Time series data is a type of data that is often used to estimate future values. Long memory phenomenon often occurs in time series data. Long memory is a condition that shows a strong correlation between observations even though they are quite far away. This phenomenon can be overcome by modeling time series data using the Autoregressive Fractional Integrated Moving Average (ARFIMA) model. This model is characterized by a fractional difference value. ARFIMA (Autoregressive Fractional Integrated Moving Average) model assumes that the residuals are normally distributed, mutually independent, and homogeneous. However, usually in financial data, the residual variants are not constant. This can be overcome by modeling variants. Standard equipment that can be used to model variants is the ARCH / GARCH (Auto Regressive Conditional Heteroscedasticity / Generalized Auto Regressive Conditional Heteroscedasticity) model. Another phenomenon that often occurs in GARCH models is the leverage effect on the residuals of the model. EGARCH (Exponential General Auto Regessive Conditional Heteroscedasticity) is a development of the GARCH model that is appropriate for data that has an leverage effect. The implementation of this model is by modeling financial data, so this study takes 136 monthly data on rice prices in Semarang City from January 2009 to April 2020. The purpose of this study is to create a long memory data forecasting model using the Exponential method. Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). The best model obtained is ARFIMA (1, d, 1) EGARCH (1,1) which is capable of forecasting with a MAPE value of 3.37%.Keyword : Rice price, forecasting , long memory, leverage effect, GARCH, EGARCH

15

Tse, Y. K., and Albert K. C. Tsui. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model With Time-Varying Correlations." Journal of Business & Economic Statistics 20, no. 3 (July 2002): 351–62. http://dx.doi.org/10.1198/073500102288618496.

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16

Augustine Kutu, Adebayo, and Harold Ngalawa. "Exchange rate volatility and global shocks in Russia: an application of GARCH and APARCH models." Investment Management and Financial Innovations 13, no. 4 (December 29, 2016): 203–11. http://dx.doi.org/10.21511/imfi.13(4-1).2016.06.

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This study examines global shocks and the volatility of the Russian rubble/United States dollar exchange rate using the symmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH), and Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) models. The GARCH and APARCH are employed under normal (Normal Gaussian) and non-normal (Student’s t and Generalized Error) distributions. Using monthly exchange rate data covering January 1994 – December 2013, the study finds that the symmetric (GARCH) model has the best fit under the non-normal distribution, which improves the overall estimation for measuring conditional variance. Conversely, the APARCH model does not show asymmetric response in exchange rate volatility and global shocks, resulting in no presence of leverage effect. The GARCH model under the Student’s t distribution produces better fit for estimating exchange rate volatility and global shocks in Russia, compared to the APARCH model. Keywords: exchange rate volatility, global Shocks, GARCH and APARCH models. JEL Classification: F30, F31, P33

17

Nabila, S. U., M. Usman, Warsono, N. Indryani, Widiarti, and D. Kurniasari. "Dynamic Modeling Data Time Series By Using Constant Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity." Journal of Physics: Conference Series 1751 (January 2021): 012015. http://dx.doi.org/10.1088/1742-6596/1751/1/012015.

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18

Azimi, Mohammad Naim, and Seyed Farhad Shahidzada. "A Correcting Note on Forecasting Conditional Variance Using ARIMA vs. GARCH Model." International Journal of Economics and Finance 11, no. 5 (April 30, 2019): 145. http://dx.doi.org/10.5539/ijef.v11n5p145.

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In this study, we demonstrate that a common approach in using the Autoregressive Integrated Moving Average model is not efficient to forecast all types of time series data and most specially, the out-of-sample forecasting of the time series that exhibits clustering volatility. This gap leads to introduce a competing model to catch up with the clustering volatility and conditional variance for which, we empirically document the efficient and lower error use of the Generalized Autoregressive Conditional Heteroscedasticity model instead.

19

Gupta, Kapil, and Mandeep Kaur. "Impact Of Financial Crisis On Hedging Effectiveness Of Futures Contracts: Evidence From The National Stock Exchange Of India." South East European Journal of Economics and Business 10, no. 2 (December 1, 2015): 69–88. http://dx.doi.org/10.1515/jeb-2015-0009.

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Abstract The present study examines the impact of the 2008 financial crisis on the hedging effectiveness of three index futures contracts traded on the National Stock Exchange of India for near, next and far month contracts over the sample period of January 2000 – June 2014. The hedge ratios were calculated using eight methods; Naive hedging, Ederington’s Model, Autoregressive Integrated Moving Average, Vector Autoregressive, Vector Error Correction Methodology, Generalized Autoregressive Conditional Heteroskedasticity, Exponential Generalized Autoregressive Conditional Heteroscedasticity and Threshold Generalized Autoregressive Conditional Heteroskedasticity. The study finds an improvement in hedging effectiveness during the post-crisis period, which implies that during the high-volatility period hedging effectiveness also improves. It was also found that near month futures contracts are a more effective tool for hedging as compared to next and far month contracts, which imply that liquidity is a more important determinant of hedging effectiveness than hedge horizons. The study also finds that a time-invariant hedge ratio is more efficient than time-variant hedging. Therefore, knowledge of sophisticated econometrical tools does not help to improve hedge effectiveness.

20

So, Mike K. P., Cathy W. S. Chen, and Feng-Chi Liu. "Best subset selection of autoregressive models with exogenous variables and generalized autoregressive conditional heteroscedasticity errors." Journal of the Royal Statistical Society: Series C (Applied Statistics) 55, no. 2 (April 2006): 201–24. http://dx.doi.org/10.1111/j.1467-9876.2006.00535.x.

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21

Sidik, Aninda Firdayati, and Jamaliatul Badriyah. "Metode Integrated Generalized Autoregressive Conditional Heteroscedasticity (IGARCH) Untuk Memodelkan Harga Gabah Dunia." JMPM: Jurnal Matematika dan Pendidikan Matematika 2, no. 2 (November 5, 2017): 110. http://dx.doi.org/10.26594/jmpm.v2i2.896.

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22

Hadizadeh, Reza, and Paria Soleimani. "Monitoring simple linear profiles in the presence of generalized autoregressive conditional heteroscedasticity." Quality and Reliability Engineering International 33, no. 8 (August 31, 2017): 2423–36. http://dx.doi.org/10.1002/qre.2199.

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23

Muharam, Harjum, Robiyanto Robiyanto, Irene Pangestuti, and Wisnu Mawardi. "Measuring Asian Stock Market Integration by Using Orthogonal Generalized Autoregressive Conditional Heteroscedasticity." Montenegrin Journal of Economics 16, no. 1 (March 2020): 121–37. http://dx.doi.org/10.14254/1800-5845/2020.16-1.8.

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24

Setiawan, Eri, Netti Herawati, and Khoirin Nisa. "Modeling Stock Return Data Using Asymmetric Volatility Models: A Performance Comparison Based On the Akaike Information Criterion and Schwarz Criterion." INSIST 3, no. 2 (October 20, 2018): 160. http://dx.doi.org/10.23960/ins.v3i2.160.

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The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used in time series forecasting especially with asymmetric volatility data. As the generalization of autoregressive conditional heteroscedasticity model, GARCH is known to be more flexible to lag structures. Some enhancements of GARCH models were introduced in literatures, among them are Exponential GARCH (EGARCH), Threshold GARCH (TGARCH) and Asymmetric Power GARCH (APGARCH) models. This paper aims to compare the performance of the three enhancements of the asymmetric volatility models by means of applying the three models to estimate real daily stock return volatility data. The presence of leverage effects in empirical series is investigated. Based on the value of Akaike information and Schwarz criterions, the result showed that the best forecasting model for our daily stock return data is the APARCH model.

25

Mubarok, Faizul, Abdul Hamid, and Mohammad Nur Rianto Al Arif. "Predicting Volatility of Non-Performing Financing: Lessons from Indonesian Islamic Banking Industry." Muqtasid: Jurnal Ekonomi dan Perbankan Syariah 11, no. 1 (June 18, 2020): 1–13. http://dx.doi.org/10.18326/muqtasid.v11i1.1-13.

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Islamic banking financing is carried out into three categories, namely financing based on economic sectors, financing based on contracts, and financing based on types of use. The funding is faced with risks where the customer is unable to pay the loan. This study discusses the forecasting of Islamic banking problem financing in Indonesia using monthly data from 2003 to 2019. Prediction is made using the Autoregressive Conditional Heteroscedasticity-Generalized Autoregressive Conditional Heteroscedasticity (ARCH-GARCH) method. Forecasting from the results of this study shows that non-performing financing tends to decrease in Indonesia. Islamic banking has been able to manage problem financing well. Islamic banking financing should be focused on sectors that have low risk. The results of this study are undoubtedly useful for stakeholders to make policies to improve the quality of the funding.

26

Ahmad Sonjaya. "PERAMALAN KINERJA PERBANKAN INDONESIA DENGAN ARCH-GARCH." Jurnal Indonesia Sosial Sains 2, no. 3 (March 21, 2021): 339–50. http://dx.doi.org/10.36418/jiss.v2i3.214.

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Perbankan sebagai salah satu bentuk organisasi memiliki tujuan tertentu yang ingin dicapai. Keberhasilan dalam mencapai tujuan perbankan merupakan prestasi manajemen. Penilaian prestasi atau kinerja suatu perbankan diukur karena dapat dipakai sebagai dasar pengambilan keputusan baik pihak eksternal maupun internal. Penelitian ini bertujuan untuk memodelkan serta meramalkan kinerja perbankan. Penelitian ini menggunakan data bulanan dari tahun 2012 sampai 2020 dengan metode Autoregressive Conditional Heteroscedasticity - Generalized Autoregressive Conditional Heteroscedasticity (ARCH-GARCH). Data yang digunakan dalam penelitian ini adalah Beban Operasional Pendapatan Operasional (BOPO), Loan to Deposits Ratio (LDR), Return On Assets Ratio (ROA), dan Net Interest Margin Ratio (NIM). Hasil pengujian dari pemodelan volatilitas ditemukan bahwa semua data memiliki sifat volatilitas dimana beberapa rasio dipengaruhi oleh error dan volatilitas return periode sebelumnya. Hasil peramalan data cenderung stabil walaupun pada periode tertentu terjadi lonjakan yang menandakan adanya volatilitas.

27

Layla, Nur Najmi, Eti Kurniati, and Didi Suhaedi. "Peramalan Indeks Harga Saham dengan Autoregressive Moving Average Generelized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH)." Jurnal Riset Matematika 1, no. 1 (July 6, 2021): 7–12. http://dx.doi.org/10.29313/jrm.v1i1.103.

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Abstract. The stock price index is the information the public needs to know the development of stock price movements. Stock price forecasting will provide a better basis for planning and decision making. The forecasting model that is often used to model financial and economic data is the Autoregressive Moving Average (ARMA). However, this model can only be used for data with the assumption of stationarity to variance (homoscedasticity), therefore an additional model is needed that can model data with heteroscedasticity conditions, namely the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. This study uses data partitioning in pre-pandemic conditions and during the pandemic, Insample data with pre-pandemic conditions and insample data during pandemic conditions. Based on the research results, the GARCH model (1,1) was obtained with the conditions before the pandemic and GARCH (1,2) during the pandemic condition. The forecasting model obtained has met the eligibility requirements of the GARCH model. If the forecasting model fulfills the eligibility requirements, then MAPE calculations are performed to see the accuracy of the forecasting model. And obtained MAPE in the conditions before the pandemic and during the pandemic in the very good category. Abstrak. Indeks harga saham merupakan informasi yang diperlukan masyarakat untuk mengetahui perkembangan pergerakan harga saham. Peramalan harga saham akan memberikan dasar yang lebih baik bagi perencanaan dan pengambilan keputusan. Model peramalan yang sering digunakan untuk memodelkan data keuangan dan ekonomi adalah Autoregrresive Moving Average (ARMA). Namun model tersebut hanya dapat digunakan untuk data dengan asumsi stasioneritas terhadap varian (homoskedastisitas), oleh karena itu diperlukan suatu model tambahan yang bisa memodelkan data dengan kondisi heteroskedastisitas, yaitu model Generalized Autoregressive Conditional Heteroscedastisity (GARCH). Penelitian ini menggunakan partisi data pada kondisi sebelum pandemi dan saat pandemi berlangsung data Insample dengan kondisi sebelum pandemi dan insample pada kondisi pandemi. Berdasarkan hasil penelitian, maka didapat model GARCH (1,1) dengan kondisi sebelum pandemi dan GARCH (1,2) saat kondisi pandemi. Model peramalan yang didapat sudah memenuhi syarat kelayakan model GARCH. Apabila model peramalan terpenuhi syarat kelayakannya maka dilakukan perhitungan MAPE untuk melihat keakuratan model peramalannya. Dan diperoleh MAPE pada kondisi sebelum pandemi dan saat pandemi dengan kategori sangat baik.

28

Mohamed Yusof, Noreha, Badrina Nur Yasmin Badrul Azhar, Syazana Zakaria, and Intan Nadia Azvilla Maulad Mohamad Rawi. "PERFORMANCE OF KUALA LUMPUR COMPOSITE INDEX STOCK MARKET." MALAYSIAN JOURNAL OF COMPUTING 5, no. 2 (September 8, 2020): 553. http://dx.doi.org/10.24191/mjoc.v5i2.9495.

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Financial Times Stock Exchange (FTSE) Bursa Malaysia Kuala Lumpur Composite Index (KLCI) is made up of over 30 large companies listed on the Bursa Malaysia Main Market. All FTSE Bursa Malaysia data are calculated and disseminated every 15 seconds in real-time. It is believed that the volatility of the stock market has a negative impact on real economic recovery. This paper aims to describe the underlying structure and the phenomenon of the sequence of observations in the series. The information obtained, can determine the performance of time series model to fit the data series from January 2002 until December 2018. Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have been shown to provide the correct trend of volatility. The objectives of this paper are to determine the overall trend of the KLCI stock return and to investigate the performance of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) based on KLCI stock return. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) have been chosen to be used in this paper to measure accuracy. The results show that the best ARIMA model is ARIMA(1,1), while for the GARCH model, it is GARCH(1,1).

29

Oko-Isu, Anthony, Agnes Ugboego Chukwu, Grace Nyereugwu Ofoegbu, Christiana Ogonna Igberi, Kennedy Okechukwu Ololo, Tobechi Faith Agbanike, Lasbrey Anochiwa, et al. "Coffee Output Reaction to Climate Change and Commodity Price Volatility: The Nigeria Experience." Sustainability 11, no. 13 (June 26, 2019): 3503. http://dx.doi.org/10.3390/su11133503.

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Empirical evidence is lacking on the nexus between coffee commodity output, climate change, and commodity price volatility of Africa’s most populous country, Nigeria, and other developing countries. To fill this gap, this study analyzed the reaction of coffee output to climate change and commodity price volatility. We used secondary data from 1961 to 2015 from reliable sources for Nigeria. The study adopted generalized autoregressive conditional heteroscedasticity (GARCH), autoregressive conditional heteroscedasticity (ARCH), and fully modified ordinary least square (FMOLS) in analysis of coffee output reaction to climate change and commodity price volatility. The findings show that coffee output in Nigeria is influenced by climate change and the international commodity price of coffee. The study demonstrates the potential benefits of improving coffee output and export through climate mitigation and adaptation measures and revival of agricultural commodity marketing in Nigeria and other developing countries.

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Rudolph, Andreas. "A central limit theorem for random coefficient autoregressive models and ARCH/GARCH models." Advances in Applied Probability 30, no. 01 (March 1998): 113–21. http://dx.doi.org/10.1017/s0001867800008107.

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In this paper we study the so-called random coeffiecient autoregressive models (RCA models) and (generalized) autoregressive models with conditional heteroscedasticity (ARCH/GARCH models). Both models can be represented as random systems with complete connections. Within this framework we are led (under certain conditions) to CL-regular Markov processes and we will give conditions under which (i) asymptotic stationarity, (ii) a law of large numbers and (iii) a central limit theorem can be shown for the corresponding models.

31

Rudolph, Andreas. "A central limit theorem for random coefficient autoregressive models and ARCH/GARCH models." Advances in Applied Probability 30, no. 1 (March 1998): 113–21. http://dx.doi.org/10.1239/aap/1035227994.

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In this paper we study the so-called random coeffiecient autoregressive models (RCA models) and (generalized) autoregressive models with conditional heteroscedasticity (ARCH/GARCH models). Both models can be represented as random systems with complete connections. Within this framework we are led (under certain conditions) to CL-regular Markov processes and we will give conditions under which (i) asymptotic stationarity, (ii) a law of large numbers and (iii) a central limit theorem can be shown for the corresponding models.

32

Baur, Dirk G., and Thomas Dimpfl. "A Quantile Regression Approach to Estimate the Variance of Financial Returns*." Journal of Financial Econometrics 17, no. 4 (November 13, 2018): 616–44. http://dx.doi.org/10.1093/jjfinec/nby026.

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We propose to estimate the conditional variance of a time series of financial returns through a quantile autoregressive (AR) model and demonstrate that it contains all information commonly captured in two separate equations for the mean and variance of a generalized AR conditional heteroscedasticity-type model. We show that the inter-quantile range spanned by conditional quantile estimates identifies the asymmetric response of volatility to lagged returns, resulting in wider conditional densities for negative returns than for positive returns. Finally, we estimate the conditional variance based on the estimated conditional density and illustrate its accuracy in a forecast evaluation.

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Kustiara, Sri, Indah Manfaati Nur, and Tiani Wahyu Utami. "ARCH GARCH METHOD OF FORECASTING CONSUMER PRICE INDEX (CPI) IN SEMARANG." Jurnal Litbang Edusaintech 1, no. 1 (December 23, 2020): 14–22. http://dx.doi.org/10.51402/jle.v1i1.3.

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Indeks Harga Konsumen (IHK) merupakan salah satu indikator ekonomi penting yang dapat memberikan informasi mengenai perkembangan harga barang/jasa yang dibayar oleh konsumen di suatu wilayah. Penghitungan IHK ditujukan untuk mengetahui perubahan harga dari sekelompok tetap barang atau jasa yang umumnya dikonsumsi oleh masyarakat setempat. Dalam metode yang digunakan dalam pemodelan data runtun waktu memiliki syarat khusus yaitu yang teridentifikasi efek heteroskedastisitas. Tujuan dari penelitian ini adalah untuk mengetahui model terbaik peramalan periode berikutnya serta hasil prediksi periode mendatang. Variabel yang digunakan adalah data Indeks Harga Konsumen dalam bulan. Sehingga untuk mengatasi permasalahan pada data penelitian ini digunakan metode Autoregressive Conditional Heteroscedasticity Generalized Autoregressive Conditional Heteroscedasticity (ARCH GARCH). Hasil dari penelitian ini didapatkan metode ARCH GARCH model terbaik yang digunakan adalah ARIMA (1,1,1)~GARCH (1,0). Dengan prediksi dari volatilitas dengan nilai standar deviasi 0.98283514 diperoleh prediksi volatilitas terendah sebesar 0.9632546 dan prediksi volatilitas tertinggi sebesar 0.9980155.

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Robinson Sihombing, Pardomuan, Oki Prasetia Hendarsin, Sarah Sholikhatun Risma, and Bekti Endar Susilowati. "The Application Of Autoregressive Integrated Moving Average Generalized Autoregressive Conditional Heteroscedastic (Arima - Garch)." Udayana Journal of Social Sciences and Humanities (UJoSSH) 4, no. 2 (September 29, 2020): 63. http://dx.doi.org/10.24843/ujossh.2020.v04.i02.p04.

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Rice farming for Indonesia is vital. Rice farming is inseparable from the fact that rice farming is the livelihood of most of the population, while rice is the staple food of almost all Indonesians. The nature of rice that is easy to process and, following the public consumption culture, causes a very high dependence on rice. On the other hand, the price of rice is quite volatile. If the price of rice is soaring high, it can cause changes in the pattern of rice consumption. Some people want a stable supply and rice price, available at all times and evenly distributed and at affordable prices. Because the cost of rice is quite fluctuating, it is necessary to have a model that can be used to predict future rice prices so that the right policies can be implemented. Autoregressive Integrated Moving Average Model Generalized Autoregressive Conditional Heteroscedastic (ARIMA-GARCH) is a useful model for evaluating and predicting price fluctuations. This model's application is implemented in the national average retail rice price data between January 2007 and December 2017. In this study, rice data in the study period was not stationary at the level so that differentiating was carried out in the data. The best model is ARIMA (1,1,2) and Garch model (2,0). In this model, the data has complied with the white noise assumption, and the resulting GARCH model is free from the heteroscedasticity assumption.

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Budiandru, Budiandru. "ARCH and GARCH Models on the Indonesian Sharia Stock Index." JURNAL AKUNTANSI DAN KEUANGAN ISLAM 9, no. 1 (April 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 periods and the return volatility in the previous period. Potential Indonesian Sharia Stock Index tends to fluctuate in return by an average of 3 percent.

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Zeng, Ning. "Monetary Stability and Stock Returns: A Bivariate Generalized Autoregressive Conditional Heteroscedasticity Modelling Study." Business and Economic Research 5, no. 2 (June 17, 2015): 1. http://dx.doi.org/10.5296/ber.v5i2.7623.

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<p class="ber"><span lang="EN-GB">This paper employs a constant conditional correlation bivariate EGARCH-in-mean model to investigate interactions among the rate of inflation, stock returns and their respective volatilities. This approach is capable of accommodating all the possible causalities among the four variables simultaneously, and therefore could deliver contemporary evidence of the nexus between monetary stability and stock market. The postwar dataset of the US inflation and stock returns is divided into pre- and post- Volcker period and the estimation results show some significant changes of inflation-stock return relation, as well as indirect links between two volatilities. The core findings in this study suggest that promoting monetary stability contributes to more mutual interactions among the four variables, in particular, common stock is a more effective hedge against inflation, and the level of inflation rate is central to explaining the relation between the two volatilities.</span></p>

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Agboluaje, Ayodele Abraham, Suzilah bt Ismail, and Chee Yin Yip. "Modeling the Error Term by Moving Average and Generalized Autoregressive Conditional Heteroscedasticity Processes." American Journal of Applied Sciences 12, no. 11 (November 1, 2015): 896–901. http://dx.doi.org/10.3844/ajassp.2015.896.901.

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38

Tyas, Mutik Dian Prabaning, Di Asih I. Maruddani, and Rita Rahmawati. "PERHITUNGAN VALUE AT RISK DENGAN PENDEKATAN THRESHOLD AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY-GENERALIZED EXTREME VALUE." MEDIA STATISTIKA 12, no. 1 (July 24, 2019): 73. http://dx.doi.org/10.14710/medstat.12.1.73-85.

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39

Ghosh, Asim K. "MARKET MODEL CORRECTED FOR GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY AND THE SMALL FIRM EFFECT." Journal of Financial Research 15, no. 3 (September 1992): 277–83. http://dx.doi.org/10.1111/j.1475-6803.1992.tb00805.x.

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40

Chang, Bao Rong, and Hsiu-Fen Tsai. "Quantum minimization for adapting ANFIS outputs to its nonlinear generalized autoregressive conditional heteroscedasticity." Applied Intelligence 31, no. 1 (December 22, 2007): 31–46. http://dx.doi.org/10.1007/s10489-007-0110-y.

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41

Liko, Rozana. "Modeling the Behavior of Inflation Rate in Albania Using Time Series." JOURNAL OF ADVANCES IN MATHEMATICS 13, no. 3 (July 30, 2017): 7257–63. http://dx.doi.org/10.24297/jam.v13i3.6196.

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In this paper, time series theory is used to modelling monthly inflation data in Albania during the period from January 2000 to December 2016. The autoregressive conditional heteroscedastic (ARCH) and their extensions, generalized autoregressive conditional heteroscedasticity (GARCH)) models are used to better fit the data. The study reveals that the inflation series is stationary, non-normality and has serial correlation. Based on minimum AIC and SIC values the best model turn to be GARCH (1, 1) model with mean equation ARMA (2, 1)x(2, 0)12. Based on the selected model one year of inflation is forecasted (from January 2016 to December 2016).

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Widodo, Dea Manuella, Sudarno Sudarno, and Abdul Hoyyi. "PEMODELAN RETURN HARGA SAHAM MENGGUNAKAN MODEL INTERVENSI–ARCH/GARCH (Studi Kasus : Return Harga Saham PT Bayan Resources Tbk)." Jurnal Gaussian 7, no. 2 (May 30, 2018): 110–18. http://dx.doi.org/10.14710/j.gauss.v7i2.26642.

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The intervention method is a time series model which could be used to model data with extreme fluctuation whether up or down. Stock price return tend to have extreme fluctuation which is caused by internal or external factors. There are two kinds of intervention function; a step function and a pulse function. A step function is used for a long-term intervention, while a pulse function is used for a short-term intervention. Modelling a time series data needs to satisfy the homoscedasticity assumptions (variance of residual is homogeneous). In reality, stock price return has a high volatility, in other words it has a non-constant variance of residuals (heteroscedasticity). ARCH (Autoregressive Conditional Heteroscedasticity) or GARCH (Generalized Autoregressive Conditional Heteroscedasticity) can be used to model data with heteroscedasticity. The data used is stock price return from August 2008 until September 2018. From the stock price return data plot is found an extreme fluctuation in September 2017 (T=110) that is suspected as a pulse function. The best model uses the intervention pulse function is ARMA([1,4],0) (b=0, s=1, r=1). The intervention model has a non-constant variance or there is an ARCH effect. The best variance model obtained is ARMA([1,4],0)(b=0, s=1, r=1)–GARCH(1,1) with the AIC value is -205,75088. Keywords: Stock Return, Intervention, Heteroscedasticity, ARCH/GARCH

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Robiyanto, Robiyanto. "Month of the Year Effect pada Pasar Obligasi di Indonesia." Jurnal Ekonomi dan Bisnis 20, no. 2 (November 2, 2017): 291. http://dx.doi.org/10.24914/jeb.v20i2.1093.

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<p><em>This study examines the month-of-the-year effect on the bond returns in Indonesia. I use the monthly closing price index (Indonesia Bond Indexes / INDOBeX) data for the periods of July 2003-July 2017 from Bloomberg. I then run the Generalize Autoregressive Conditional Heteroscedasticity (GARCH) analysis technique to analyze the data because the residuals exhibit a significant pattern of Autoregressive Conditional Heteroscedasticity (ARCH). The results show that only the month of July has a significantly positive effect on the bond returns; indicating that there is the month-of-the-year effect in the Indonesian bond market. Further, these also imply that the Indonesian bond market does not exhibit a random walk pattern and consequently they are inefficient in the weak form.</em></p><p><em><br /></em>Abstrak</p><p>Penelitian ini menguji pengaruh bulan-bulan perdagangan (month of the year) terhadap return obligasi di Indonesia. Data yang dipergunakan dalam penelitian ini adalah data indeks harga obligasi (Indonesia Bond Indexes / INDOBeX) penutupan bulanan selama periode Juli 2003 hingga Juli 2017 yang diperoleh dari Bloomberg. Analisis data dilakukan dengan menggunakan teknik analisis Generalize Autoregressive Conditional Heteroscedasticity (<em>GARCH</em>) karena pola residual yang dihasilkan menunjukkan adanya pola Autoregressive Conditional Heteroscedasticity (<em>ARCH</em>) yang signifikan. Hasil penelitian ini menunjukan bahwa bulan Juli memiliki pengaruh positif yang signifikan terhadap return obligasi di Indonesia. Sementara bulan-bulan lainnya tidak memiliki pengaruh terhadap return obligasi di Indonesia. Hasil ini menunjukkan bahwa terjadi month of the year effect di pasar obligasi di Indonesia. Temuan ini memiliki implikasi bahwa pasar obligasi di Indonesia tidak berjalan acak (random walk) sehingga tidak efisien dalam bentuk lemah.</p>

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Ali, Rafaqat, and Rana Ejaz Ali Khan. "Socioeconomic Stability and Variability in Stock Market Prices: A Case Study of Karachi Stock Exchange." Asian Journal of Economic Modelling 6, no. 4 (October 5, 2018): 428–40. http://dx.doi.org/10.18488/journal.8.2018.64.428.440.

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The study attempted to identify the factors that responsible for variability in stock market prices in Karachi Stock Exchange particularly focusing on socioeconomic stability in the country. The socioeconomic stability is measured by an index including social, economic and political dimensions of stability. Annual time series data for the years 1973-2012 is utilized, and Phillips & Perron (PP) test is employed for stationarity. Autoregressive Conditional Heteroscedasticity and Generalized Conditional Heteroscedasticity (ARCH/GARCH) technique are used for volatility in stock market prices. For the structural breaks, Chow test is applied. Finally, the study utilized the Autoregressive Distributed Lag (ARDL) approach to estimate the long-run and short-run dynamic relationship. The results indicate that inflation, exchange rate, and foreign direct investment positively influence the stock price volatility. Socioeconomic stability negatively affects the volatility in stock market prices in both short-run and long-run. The country should improve socioeconomic stability by attaining economic, social and political standards in the country.

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Dhliwayo, Lawrence, Florance Matarise, and Charles Chimedza. "Modeling Seasonal Fractionally Integrated Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroscedasticity Model with Seasonal Level Shift Intervention." Open Journal of Statistics 10, no. 05 (2020): 810–31. http://dx.doi.org/10.4236/ojs.2020.105047.

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46

Prasetya, Lingga Bayu, Dwi Ispriyanti, and Alan Prahutama. "ESTIMASI VALUE AT RISK PORTOFOLIO SAHAM MENGGUNAKAN METODE GARCH-COPULA (Studi Kasus : Harga Penutupan Saham Harian Unilever Indonesia dan Kimia Farma Periode 1 Januari 2013- 31 Desember 2016)." Jurnal Gaussian 7, no. 4 (November 30, 2018): 397–407. http://dx.doi.org/10.14710/j.gauss.v7i4.28867.

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Any investment in the stock market will earn returns accompanied by risks. Return and risk has a mutual correlation that equilibrium. The formation of a portfolio is intended to provide a lower risk or with the same risk but provide a higher return. Value at Risk (VaR) is a instrument to analyze risk management. Time series model used in stock return data that it has not normal distribution and heteroscedastisicity is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). GARCH-Copula is a combined method of GARCH and Copula. The Copula method is used in joint distribution modeling because it does not require the assumption of normality of the data and can capture tail dependence between each variable. This research uses return data from stock closing prices of Unilever Indonesia and Kimia Farma period January 1, 2013 until December 31, 2016. Copula model is selected based on the highest likelihood log value is Copula Clayton. Value at Risk estimates of Unilever Indonesia and Kimia Farma's stock portfolio on the same weight were performed using Monte Carlo simulation with backtesting of 30 days period data at 95% confidence level. Keywords : Stock, Risk, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Copula, Value at Risk

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Chandrasekaran, Buvanesh, and Rajesh H. Acharya. "A study on volatility and return spillover of exchange-traded funds and their benchmark indices in India." Managerial Finance 46, no. 1 (October 14, 2019): 19–39. http://dx.doi.org/10.1108/mf-01-2019-0025.

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Purpose The purpose of this paper is to empirically examine the volatility and return spillover between exchange-traded funds (ETFs) and their respective benchmark indices in India. The paper uses time series data which consist of equity ETF and respective index returns. Design/methodology/approach The study uses autoregressive moving average–generalized autoregressive conditional heteroscedasticity and autoregressive moving average–exponential generalized autoregressive conditional heteroscedasticity models. The study uses data from the inception date of each ETF to December 2016. Findings The findings of the paper confirm that there is unidirectional return spillover from the benchmark index to ETF returns in most of the ETFs. Furthermore, ETF and benchmark index return have volatility persistence and show the presence of asymmetric volatility wherein a negative news has more influence on volatility compared to a positive news. Finally, unlike unidirectional return spillover, there is a bidirectional volatility spillover between ETF and benchmark index return. Practical implications The study has several practical implications for investors and regulators. A positive daily mean return over a fairly long period of time indicates that the passive equity ETFs can be a viable long-term investment option for ordinary investors. A bidirectional volatility spillover between the ETFs and benchmark index returns calls for the attention of the market regulators to examine the reasons for the same. Originality/value ETFs have seen fast growth in the Indian market in recent years. The present study considers the longest period data possible.

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Zhao, Xin, Hong Lei Qin, and Li Cong. "A Novel Adaptive Integrated Navigation Filtering Method Based on ARMA/GARCH Model." Applied Mechanics and Materials 462-463 (November 2013): 259–66. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.259.

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This paper proposes a novel adaptive integrated navigation filtering method based on autoregressive moving average (ARMA) model and generalized autoregressive conditional heteroscedasticity (GARCH) model. The main idea in this study is to employ ARMA/GARCH model to estimate statistical characteristics of filtering residual series online, namely, the conditional mean and conditional standard deviation, and then the filter parameters are adaptively adjusted based on forecasted results of ARMA/GARCH model in order to improve the reliability of the system when there are abnormal disturbance and other uncertain factors in real condition. On this basis, experiment is used to verify the validity of the method. The simulation results demonstrate that the ARMA/GARCH model can well capture the unusual condition of GPS receiver output, and this adaptive filtering method can effectively improve the reliability of the system.

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Olanrewaju, Rasaki Olawale. "Integer-valued Time Series Model via Generalized Linear Models Technique of Estimation." International Annals of Science 4, no. 1 (April 29, 2018): 35–43. http://dx.doi.org/10.21467/ias.4.1.35-43.

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The paper authenticated the need for separate positive integer time series model(s). This was done from the standpoint of a proposal for both mixtures of continuous and discrete time series models. Positive integer time series data are time series data subjected to a number of events per constant interval of time that relatedly fits into the analogy of conditional mean and variance which depends on immediate past observations. This includes dependency among observations that can be best described by Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model with Poisson distributed error term due to its positive integer defined range of values. As a result, an integer GARCH model with Poisson distributed error term was formed in this paper and called Integer Generalized Autoregressive Conditional Heteroscedasticity (INGARCH). Iterative Reweighted Least Square (IRLS) parameter estimation technique type of the Generalized Linear Models (GLM) was adopted to estimate parameters of the two spilt models; Linear and Log-linear INGARCH models deduced from the identity link function and logarithmic link function, respectively. This resulted from the log-likelihood function generated from the GLM via the random component that follows a Poisson distribution. A study of monthly successful bids of auction from 2003 to 2015 was carried out. The Probabilistic Integral Transformation (PIT) and scoring rule pinpointed the uniformity of the linear INGARCH than that of the log-linear INGARCH in describing first order autocorrelation, serial dependence and positive conditional effects among covariates based on the immediate past. The linear INGARCH model outperformed the log-linear INGARCH model with (AIC = 10514.47, BIC = 10545.01, QIC = 34128.56) and (AIC = 37588.83, BIC = 37614.28, QIC = 37587.3), respectively.

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Moroke, Ntebogang Dinah. "An Optimal Generalized Autoregressive Conditional Heteroscedasticity Model for Forecasting the South African Inflation Volatility." Journal of Economics and Behavioral Studies 7, no. 4(J) (August 30, 2015): 134–49. http://dx.doi.org/10.22610/jebs.v7i4(j).600.

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Abstract: In most cases, financial variables are explained by leptokurtic distribution and often fail the assumption of normal distribution. This paper sought to explore the robustness of GARCHâ€“type models in forecasting inflation volatility using quarterly time series data spanning 2002 to 2014. The data was sourced from the South African Reserve Bank database. SAS version 9.3 was used to generate the results. The initial analyses of data confirmed non-linearity, hereroscedasticity and non-stationarity in the series. Differencing was imposed in a log transformed series to induce stationarity. Further findings confirmed that ð´ð‘… (1)_ð¼ðºð´ð‘…ð¶ð» (1, 1)model suggested a high degree persistent in the conditional volatility of the series. However, theð´ð‘… (1)_ð¸ðºð´ð‘…ð¶ð» (2, 1)model was found to be more robust in forecasting volatility effects than the ð´ð‘… (1)_ð¼ðºð´ð‘…ð¶ð» (1, 1) and ð´ð‘… (1)_ðºð½ð‘… âˆ’ ðºð´ð‘…ð¶ð» (2, 1)models. This model confirmed that inflation rates in South Africa exhibits the stylised characteristics such as volatility clustering, leptokurtosis and asymmetry effects. These findings may be very useful to the industry and scholars who wish to apply models that capture heteroscedastic and non-linear errors. The findings may also benefit policy makers and may be referred to when embarking on strategies in-line with inflation rate.