Journal articles on the topic 'Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH)'

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1

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 (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, an
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Akhtar, Sohail, Maham Ramzan, Sajid Shah, et al. "Forecasting Exchange Rate of Pakistan Using Time Series Analysis." Mathematical Problems in Engineering 2022 (August 24, 2022): 1–11. http://dx.doi.org/10.1155/2022/9108580.

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Exchange rates are crucial in regulating the foreign exchange market's dynamics. Because of the unpredictability and volatility of currency rates, the exchange rate prediction has become one of the most challenging applications of financial time series forecasting. This study aims to build and compare the accuracy of various methods. The time series model Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) are utilized to forecast the daily US dollar to Pakistan rupee currency exchange rates (USD/PKR). Lagged observations of
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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 (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 condition
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Komal Batool, Mirza Faizan Ahmed, and Muhammad Ali Ismail. "A Hybrid Model of Machine Learning Model and Econometrics’ Model to Predict Volatility of KSE-100 Index." Reviews of Management Sciences 4, no. 1 (2022): 225–39. http://dx.doi.org/10.53909/rms.04.01.0125.

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Purpose: The purpose of this paper is to predict the volatility of the KSE-100 index using econometric and machine learning models. It also designs hybrid models for volatility forecasting by combining these two models in three different ways. Methodology: Estimations and forecasting are based on an econometric model GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) and a machine learning model NNAR (Neural Network Auto-Regressive model). The hybrid models designed with GARCH and NNAR include GARCH-based NNAR, NNAR-based GARCH, and the linear combination of GARCH and NNAR. Fin
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Jiang, Haoqing. "The Application of the ARIMA-GARCH Hybrid Model for Forecasting the Apple Stock Price." Journal of Intelligence and Knowledge Engineering 1, no. 1 (2023): 12–15. http://dx.doi.org/10.62517/jike.202304102.

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Modeling and forecasting stock prices is a meaningful task and one of the methods for forecasting is the classic ARIMA models. However, when the data exhibits clustering effects and heteroscedasticity, the generalized auto regressive conditional heteroskedatic (GARCH) model must be used for modeling and forecasting. In this paper, as the object of data analysis, the combination of ARIMA model and GARCH model shows a very good ability to predict the stock price with a very good description of the clustering effect of volatility.
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Zainal, Putri, Yenni Angraini, and Akbar Rizki. "Penerapan Metode Generalized Auto-Regressive Conditional Heteroscedasticity untuk Peramalan Harga Minyak Mentah Dunia." Xplore: Journal of Statistics 12, no. 1 (2023): 12–21. http://dx.doi.org/10.29244/xplore.v12i1.1096.

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Crude oil is one of the commodities that are needed in various fields. World crude oil prices that continue to fluctuate, of course, have a big influence on the country's economy. Crude oil price data collected is time series or the collection process is carried out from time to time with monthly periods. Therefore, we need a system that can forecast future world crude oil prices which are expected to be taken into consideration by the government for decision making. One method that can be used to predict world crude oil prices is ARIMA (Auto-Regressive Integrated Moving Average) and GARCH (Ge
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Atahau, Apriani, Robiyanto Robiyanto, and Andrian Huruta. "Predicting Co-Movement of Banking Stocks Using Orthogonal GARCH." Risks 10, no. 8 (2022): 158. http://dx.doi.org/10.3390/risks10080158.

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This study investigates the application of orthogonal generalized auto-regressive conditional heteroscedasticity (OGARCH) in predicting the co-movement of banking sector stocks in Indonesia. All state-owned banking sector stocks in Indonesia were studied using daily data from January 2013 to December 2019. The findings indicate that the OGARCH method can simplify the covariance matrix. Most state-owned banking stocks in the banking sector have a similar principal component influencing their conditional variance. Nonetheless, one stock has different principal components. The findings imply that
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Cheng, Cong, Ling Yu, and Liu Jie Chen. "Structural Nonlinear Damage Detection Based on ARMA-GARCH Model." Applied Mechanics and Materials 204-208 (October 2012): 2891–96. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.2891.

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Two economic models, i.e. auto-regressive and moving average model (ARMA) and generalized auto-regressive conditional heteroscedasticity model (GARCH), are adopted to assess the conditions of structures and to detect structural nonlinear damage based on time series analysis in this study. To improve the reliability of the method for nonlinear damage detection, a new damage sensitive feature (DSF) for the ARMA-GARCH model is defined as a ratio of the standard deviation of the variance time series of ARMA-GARCH model residual errors in test condition to ones in reference condition. Compared to t
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9

Tolulope, Jerumeh. "Nature, Trends and Drivers of Food Price Volatility in Nigeria." European Journal of Agriculture and Food Sciences 4, no. 6 (2022): 109–17. http://dx.doi.org/10.24018/ejfood.2022.4.6.619.

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The volatility of food prices is an important risk factor which constitutes serious threat to the welfare of millions of people around the world, particularly in developing countries like Nigeria. The study therefore investigated the pattern and drivers of food price volatility in Nigeria using annual and monthly time series data from January,2000 to December, 2020. Data analysis was done using descriptive statistics, Coefficient of Variation, Auto-Regressive Conditional Heteroscedasticity (ARCH) model, Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) model, and Exponential G
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ARIF HUSSAIN, AHMAD BILAL HUSSAIN, and SHAHID ALI. "The Impact of Interest Rate Volatility on Stock Returns Volatility: Empirical Evidence from Pakistan Stock Exchange." Journal of Business & Tourism 3, no. 2 (2021): 53–58. http://dx.doi.org/10.34260/jbt.v3i2.71.

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Apprehension pertaining to Stock return volatility always has been producing the appreciable significance in the various current research works and it has been lucrative to many researchers for forecasting stock market volatility. This study is about the forecasting of stock returns volatility on the basis of interest rate volatility in the well established Pakistan Stock Exchange (PSX). The stock returns are calculated on the basis of KSE 100 index and interest rate volatility is calculated on the basis of monthly treasury bills rate during a period of 1994 to 2016. Various volatility models
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Sukono, Sukono, Emah Suryamah, and Fujika Novinta S. "Application of ARIMA-GARCH Model for Prediction of Indonesian Crude Oil Prices." Operations Research: International Conference Series 1, no. 1 (2020): 25–33. http://dx.doi.org/10.47194/orics.v1i1.21.

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Crude oil is one of the most important energy commodities for various sectors. Changes in crude oil prices will have an impact on oil-related sectors, and even on the stock price index. Therefore, the prediction of crude oil prices needs to be done to avoid the future prices of these non-renewable natural resources to increase dramatically. In this paper, the prediction of crude oil prices is carried out using the Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) models. The data used for forecasting are Indonesian Crude Pr
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Sun, Kaiying. "Equity Return Modeling and Prediction Using Hybrid ARIMA-GARCH Model." International Journal of Financial Research 8, no. 3 (2017): 154. http://dx.doi.org/10.5430/ijfr.v8n3p154.

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In this paper, a hybrid ARIMA-GARCH model is proposed to model and predict the equity returns for three US benchmark indices: Dow Transportation, S&P 500 and VIX. Equity returns are univariate time series data sets, one of the methods to predict them is using the Auto-Regressive Integrated Moving Average (ARIMA) models. Despite the fact that the ARIMA models are powerful and flexible, they are not be able to handle the volatility and nonlinearity that are present in the time series data. However, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models are designed to c
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Winanti, Gandhes Linggar, Dwi Ispriyanti, and Sugito Sugito. "PEMODELAN INDEKS HARGA PERDAGANGAN BESAR (IHPB) SEKTOR EKSPOR MENGGUNAKAN ARFIMA-GARCH." Jurnal Gaussian 12, no. 1 (2023): 52–60. http://dx.doi.org/10.14710/j.gauss.12.1.52-60.

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Indonesia's price index serves as a barometer for the nation's economic condition. One of the Indonesia’s price index is Wholesale Price Index (WPI). WPI is a price index that tracks the average change in wholesale prices over time. Time series analysis can be used for forecasting because WPI is one of the time series data. WPI is long memory, which is a condition in which data from different time periods have a high link despite being separated by a large amount of time. The Autoregressive Fractional Integrated Moving Average (ARFIMA) model can be used to overcome this feature when modeling t
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Mirza, Hammad Hassan, and Naveed Mushtaq . "Stock Market Returns and Weather Anomaly: Evidence from an Emerging Economy." Journal of Economics and Behavioral Studies 4, no. 5 (2012): 239–44. http://dx.doi.org/10.22610/jebs.v4i5.323.

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Financial economists believe that the arbitrage forces in the market are the main reason of market efficiency and these forces are the fundamental concept of efficient market hypothesis (EMH). During last few years, various theoretical and empirical evidences have been presented to support the work of financial modeling for the markets with less than rational investors whose trading strategies are based on psychological factors like mood and emotions. Weather condition is among the substantial factors affecting investors’ mood and emotions. Present study investigates the impact of temperatur
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Kaya Soylu, Pınar, Mustafa Okur, Özgür Çatıkkaş, and Z. Ayca Altintig. "Long Memory in the Volatility of Selected Cryptocurrencies: Bitcoin, Ethereum and Ripple." Journal of Risk and Financial Management 13, no. 6 (2020): 107. http://dx.doi.org/10.3390/jrfm13060107.

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This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi Parametric (GSP) and the Geweke and Porter-Hudak (GPH) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting the use of fractional Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) extensions as suitable mo
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Abdullah, Ezatul Akma, Siti Meriam Zahari, S. Sarifah Radiah Shariff, and Muhammad Asmu’i Abdul Rahim. "Modelling volatility of Kuala Lumpur composite index (KLCI) using SV and garch models." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 1087. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp1087-1094.

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It is well-known that financial time series exhibits changing variance and this can have important consequences in formulating economic or financial decisions. In much recent evidence shows that volatility of financial assets is not constant, but rather that relatively volatile periods alternate with more tranquil ones. Thus, there are many opportunities to obtain forecasts of this time-varying risk. The paper presents the modelling volatility of the Kuala Lumpur Composite Index (KLCI) using SV and GARCH models. Thus, the aim of this study is to model the KLCI stock market using two models; St
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Ezatul, Akma Abdullah, Meriam Zahari Siti, Sarifah Radiah Shariff S., and Asmu'i Abdul Rahim Muhammad. "Modelling volatility of Kuala Lumpur composite index (KLCI) using SV and garch models." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 1087–94. https://doi.org/10.11591/ijeecs.v13.i3.pp1087-1094.

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It is well-known that financial time series exhibits changing variance and this can have important consequences in formulating economic or financial decisions. In much recent evidence shows that volatility of financial assets is not constant, but rather that relatively volatile periods alternate with more tranquil ones. Thus, there are many opportunities to obtain forecasts of this time-varying risk. The paper presents the modelling volatility of the Kuala Lumpur Composite Index (KLCI) using SV and GARCH models. Thus, the aim of this study is to model the KLCI stock market using two models; St
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18

Anand, C. "Comparison of Stock Price Prediction Models using Pre-trained Neural Networks." March 2021 3, no. 2 (2021): 122–34. http://dx.doi.org/10.36548/jucct.2021.2.005.

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Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto-Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. The deep learning architectures
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19

Białek-Jaworska, Anna, and Tomasz Krawczyk. "Corporate bonds or bank loans? The choice of funding sources and information disclosure of Polish listed companies." Central European Economic Journal 6, no. 53 (2020): 262–85. http://dx.doi.org/10.2478/ceej-2019-0017.

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AbstractThe paper aims to find what determines the choice of companies listed on the Warsaw Stock Exchange (WSE) between public debt (corporate bonds) and private debt (bank loans). For this purpose, we estimate logistic regression models and panel models of corporate borrowing determinants to compare the impact of enterprise characteristics on financing with the use of corporate bonds or bank loans. In this study, we are interested in explanatory variables that explain the role of transparency measured by the level of information disclosure; and a risk proxy of the variability of operational
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20

Chigozirim, Onwusiribe Ndubuisi, Nto Philips Okore, Oteh Ogbonnaya Ukeh, and Agwu Nnanna Mba. "Dynamics of Food Price Volatility and Households’ Welfare in Nigeria." Agris on-line Papers in Economics and Informatics 13, no. 4 (2021): 49–60. http://dx.doi.org/10.7160/aol.2021.130405.

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One of the most important economic factors in food choice is the price. Food dynamics' value is a subject of controversies and opinions, especially price issues, and sensitivity is often peculiar to seasons and market forces. Price dynamics have the potential to introduce and change consumptions, thus affecting household welfare. This study examined the dynamics of food price volatility and households' welfare in Nigeria from 1990: Q1 to 2019: Q4. We sourced the study data from the Food and Agriculture Organization (FAO) and the World Bank (WB). We estimated the quadratic trend equation, Gener
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Othman, Anwar Hasan Abdullah, Syed Musa Alhabshi, and Razali Haron. "The effect of symmetric and asymmetric information on volatility structure of crypto-currency markets." Journal of Financial Economic Policy 11, no. 3 (2019): 432–50. http://dx.doi.org/10.1108/jfep-10-2018-0147.

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Purpose This paper aims to examine whether the crypto-currencies’ market returns are symmetric or asymmetric informative, through analysing the daily logarithmic returns of bitcoin currency over the period of 2011-2017. Design/methodology/approach In doing so, the symmetric informative analysis is estimated by applying the generalised auto-regressive conditional heteroscedasticity (GARCH) (1,1) model, whereas asymmetric informative or leverage effects analysis is estimated by exponential GARCH (1,1), asymmetric power ARCH (1,1) and threshold GARCH (1,1) models. In addition, the generalized aut
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Musa, N. "Analysis of Crude Oil Market Volatility and Macroeconomic Conditions: Empirical Evidence from Nigeria." Review of Business and Economics Studies 11, no. 4 (2024): 61–71. http://dx.doi.org/10.26794/2308-944x-2023-11-4-61-71.

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This study aims to investigate the relationship between the volatility of the crude oil market and the macroeconomic conditions in Nigeria. The author used the methods of the auto-regressive distributed lag (ARDL) model in conjunction with the generalized autoregressive conditional heteroscedasticity (GARCH) to determine the extent of volatility using a monthly dataset from January 2012 to December 2022. The author regressed the crude oil price volatility index on Organization of the Petroleum Exporting Countries (OPEC) production quotas, conflicts, GDP growth rate, exchange rate and inflation
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Pradaswara, Hazelino Rafi, Dwi Susanti, and Sukono Sukono. "Company Stock Performance Analysis on IDX ESG Leaders Index Using the ARIMA-GARCH Model." International Journal of Quantitative Research and Modeling 3, no. 3 (2022): 133–37. http://dx.doi.org/10.46336/ijqrm.v3i3.347.

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Stocks are one of the most popular forms of investment. In investing stocks, it is necessary to know the movement of stock prices and the investment risks that may occur. The purpose of this study is to predict the level of risk, see the characteristics of stock returns, and whether the ESG Risk Rating makes the company's stock performance better. The models used to predict stock returns are Auto Regressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticty (GARCH), and Value at Risk (VaR) is used to predict risk. Based on the research, the potential
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Bulama, YaAshe M., Yakubu Bila, and Catherine O. Ojo. "TEST OF PRICE VOLATILITY: A CASE OF THE NIGERIAN CATTLE MARKET." American Journal of Economics 6, no. 1 (2022): 1–12. http://dx.doi.org/10.47672/aje.890.

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Purpose: The research investigated variation of cattle prices in Nigeria. Specifically, the research: determined the presence of volatility in cattle prices, determined the degree of volatility of the cattle prices and estimated the level of persistence of the volatility of the cattle prices.
 Methodology: Multi-stage and simple random (balotting) sampling techniques were used to select two states each from five out of the six geo-political zones in Nigeria, except South-East zone which was not represented due to unavailability of data. A total of ten states were selected. Data were analy
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Bulama, YaAshe M., Yakubu Bila, and Catherine O. Ojo. "TEST OF PRICE VOLATILITY: A CASE OF THE NIGERIAN CATTLE MARKET." American Journal of Economics 6, no. 1 (2022): 1–12. http://dx.doi.org/10.47672/aje.890.

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Purpose: The research investigated variation of cattle prices in Nigeria. Specifically, the research: determined the presence of volatility in cattle prices, determined the degree of volatility of the cattle prices and estimated the level of persistence of the volatility of the cattle prices.
 Methodology: Multi-stage and simple random (balotting) sampling techniques were used to select two states each from five out of the six geo-political zones in Nigeria, except South-East zone which was not represented due to unavailability of data. A total of ten states were selected. Data were analy
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Ndlovu, Thabani, and Delson Chikobvu. "The GARCH-EVT-Copula Approach to Investigating Dependence and Quantifying Risk in a Portfolio of Bitcoin and the South African Rand." Journal of Risk and Financial Management 17, no. 11 (2024): 504. http://dx.doi.org/10.3390/jrfm17110504.

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This study uses a hybrid model of the exponential generalised auto-regressive conditional heteroscedasticity (eGARCH)-extreme value theory (EVT)-Gumbel copula model to investigate the dependence structure between Bitcoin and the South African Rand, and quantify the portfolio risk of an equally weighted portfolio. The Gumbel copula, an extreme value copula, is preferred due to its versatile ability to capture various tail dependence structures. To model marginals, firstly, the eGARCH(1, 1) model is fitted to the growth rate data. Secondly, a mixture model featuring the generalised Pareto distri
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Atahau, Apriani Dorkas Rambu, Robiyanto Robiyanto, and Andrian Dolfriandra Huruta. "Co-Movement of Indonesian State-Owned Enterprise Stocks." Economies 11, no. 2 (2023): 46. http://dx.doi.org/10.3390/economies11020046.

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According to portfolio theory, diversifying investment to several stocks with negative correlations may reduce portfolio risk. In contrast, combining stocks with similar movement (co-movement) has no impact on portfolio risk reduction. This study aims to examine state-owned enterprise stock co-movement in Indonesia using orthogonal generalized auto-regressive conditional heteroscedasticity (O-GARCH) to help investors selectively choose stocks in a portfolio to reduce portfolio risks. Saturation sampling was used since all state-owned enterprise stocks listed on the Indonesian Stock Exchange we
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Ndlovu, Thabani, and Delson Chikobvu. "A Wavelet-Decomposed WD-ARMA-GARCH-EVT Model Approach to Comparing the Riskiness of the BitCoin and South African Rand Exchange Rates." Data 8, no. 7 (2023): 122. http://dx.doi.org/10.3390/data8070122.

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In this paper, a hybrid of a Wavelet Decomposition–Generalised Auto-Regressive Conditional Heteroscedasticity–Extreme Value Theory (WD-ARMA-GARCH-EVT) model is applied to estimate the Value at Risk (VaR) of BitCoin (BTC/USD) and the South African Rand (ZAR/USD). The aim is to measure and compare the riskiness of the two currencies. New and improved estimation techniques for VaR have been suggested in the last decade in the aftermath of the global financial crisis of 2008. This paper aims to provide an improved alternative to the already existing statistical tools in estimating a currency VaR e
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Ren, Zhiyuan. "What might happen to the global stock market after Brexit?" Studies in Economics and Finance 39, no. 2 (2022): 177–92. http://dx.doi.org/10.1108/sef-09-2020-0392.

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Purpose The stock market is vulnerable to various exogenous factors, and its fluctuations can reflect the effects of political, economic and market factors. The purpose of this paper is therefore to choose the stock market as a representative to analyze the potential impact of the Brexit event on global financial markets and how to prevent the spread of risks across global financial markets. Design/methodology/approach This study chooses the auto-regressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model to fit the financial series and uses it as the
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Indarwati, Septiana. "Benarkah Suku Bunga Memengaruhi Volatilitas Pasar Saham Syariah?" Journal of Islamic Economics and Finance Studies 2, no. 1 (2021): 56. http://dx.doi.org/10.47700/jiefes.v2i1.2780.

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AbstractThis research attempts to explore to what extent the sensitivity volatility of Islamic stock markets in Indonesia toward macroeconomics. The writer examines inflation, exchange rates, money supply (JUB), interest rates (BIRATE), and Industrial Production Index (IPI) as part of the macroeconomic variables. Meanwhile, the writer also uses Jakarta Islamic Index (JII) as the measurements for Islamic stock markets. This research uses the calculation of the stock return volatility based on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH (2, 1)) combined with Regressive D
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Roni, Bhowmik, Ghulam Abbas, and Shouyang Wang. "Return and Volatility Spillovers Effects: Study of Asian Emerging Stock Markets." Journal of Systems Science and Information 6, no. 2 (2018): 97–119. http://dx.doi.org/10.21078/jssi-2018-097-23.

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Abstract This paper examines the extent of contagion and interdependence across the six Asian emerging countries stock markets (e.g., Bangladesh, China, India, Malaysia, the Philippine, and South Korea) and then try to quantify the extent of the Asian emerging market fluctuations which are described by intra-regional contagion effect. These markets experienced both fast growth and key upheaval during the sample period, and thus, provide potentially rich information on the nature of border market interactions. Using the daily stock market index data from January 2002 to December 2016 (breaking
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Mustafa, Saima, Arfa Amjad Bajwa, and Shafqat Iqbal. "A New Fuzzy Grach Model to forecast Stock Market Technical Analysis." Operational Research in Engineering Sciences: Theory and Applications 5, no. 1 (2022): 185–204. http://dx.doi.org/10.31181/oresta040422196m.

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Decision making process in stock trading is a complex one. Stock market is a key factor of monetary markets and signs of economic growth. In some circumstances, traditional forecasting methods cannot contract with determining and sometimes data consist of uncertain and imprecise properties which are not handled by quantitative models. In order to achieve the main objective, accuracy and efficiency of time series forecasting, we move towards the fuzzy time series modeling. Fuzzy time series is different from other time series as it is represented in linguistics values rather than a numeric valu
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Haider, Syed Kamran Ali, Shujahat Haider Hashmi, and Ishtiaq Ahmed. "Systematic risk factors and stock return volatility." Applied Studies in Agribusiness and Commerce 11, no. 1-2 (2017): 61–70. http://dx.doi.org/10.19041/apstract/2017/1-2/8.

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This study analyzes the transmission of systematic risk exhaling from macroeconomic fundamentals to volatility of stock market by using auto regressive generalized auto regressive conditional heteroskedastic (AR-GARCH) and vector auto regressive (VAR) models. Systematic risk factors used in this study are industrial production, real interest rate, inflation, money supply and exchange rate from 2000-2014. Results indicate that there exists relationship among the volatility of macroeconomic factors and that of stock returns in Pakistan. The relationship among the volatility of macroeconomic vari
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Bakari, Yuliana. "PRICE VOLATILITY ANALYZE IN EARLY PANDEMIC COVID 19 OUTBREAKS: CASE STUDY IN GORONTALO PROVINCE SHALLOT MARKET." Agricultural Socio-Economics Journal 23, no. 1 (2023): 69–76. http://dx.doi.org/10.21776/ub.agrise.2023.023.1.9.

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The Covid-19 pandemic in early 2020 led to unpredictable price fluctuation in agricultural commodities such as Shallots. The purpose of this research was to analyze the price fluctuation behavior of shallots in Gorontalo Province during the Covid-19 Pandemic and the price volatility surge as well as the effect of the pandemic during 2020. This study used data from The National Food Strategy Information Center in the form of weekly price in time-series from January 2018 to December 2020. The data analysis on price volatility was conducted using the Auto-Regressive Conditional Heteroscedastic an
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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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Prawirosaputro, Bima, and Yudith Dyah Hapsari. "THE EFFECTS OF RUPIAH CURRENCY, WORLD OIL PRICES, AND WORLD GOLD PRICE ON COMPOSITED STOCK PRICE INDEX (IHSG) IN 2016." Jurnal Manajemen 14, no. 2 (2017): 144–51. http://dx.doi.org/10.25170/jm.v14i2.784.

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This study aims to determine and analyze the effects ofIndonesian Rupiah exchange rate, world oil prices, and world gold priceson IDX Composite in 2016. This research utilizedGeneralized Auto-Regressive Conditional Heteroscedasticity (GARCH) method on 234 daily observations throughout the whole year. This study results demonstrated that the Indonesian Rupiah exchange rate has a significant and negative impact, while the world gold and oil prices have a significant and positive impact on the IDX Composite in 2016. In addition to these results, the discovery and usage of GARCH method as the opti
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Wijoyo, Nugroho Agung. "Peramalan Nilai Tukar Rupiah Terhadap USD dengan Menggunakan Model GARCH." Kajian Ekonomi dan Keuangan 20, no. 2 (2016): 169–89. http://dx.doi.org/10.31685/kek.v20i2.187.

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Makalah ini menggunakan teknik ekonometrik Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) untuk meramalkan perubahan nilai tukar yang berfrekuensi tinggi di Indonesia. GARCH, suatu model non-linear, umumnya digunakan untuk data keuangan berfrekuensi tinggi, seperti nilai tukar harian Rupiah terhadap Dolar Amerika Serikat. Penelitian ini menilai perilaku dari nilai tukar Rupiah terhadap dolar Amerika Serikat dengan membuat model dari perubahan nilai tukar harian dalam bentuk logaritma untuk periode 3 Januari 2000 sampai 16 Desember 2015. Periode ini meliputi era volatilitas
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Queiroz, Rhenan G. S., and Sergio A. David. "Performance of the Realized-GARCH Model against Other GARCH Types in Predicting Cryptocurrency Volatility." Risks 11, no. 12 (2023): 211. http://dx.doi.org/10.3390/risks11120211.

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Cryptocurrencies have increasingly attracted the attention of several players interested in crypto assets. Their rapid growth and dynamic nature require robust methods for modeling their volatility. The Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model is a well-known mathematical tool for predicting volatility. Nonetheless, the Realized-GARCH model has been particularly under-explored in the literature involving cryptocurrency volatility. This study emphasizes an investigation on the performance of the Realized-GARCH against a range of GARCH-based models to predict the
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Rahardjo, Soemarso Slamet. "The Role of Speculative Factor in the Indonesian Stock Price Determination." Economics and Finance in Indonesia 61, no. 1 (2015): 69. http://dx.doi.org/10.7454/efi.v61i1.498.

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This study observes the speculative element in the price determination and its mean reverting pattern. The existence of speculative element in the Indonesian stock market price determination was proven. Exponential Generalized Auto Regressive Conditional Heteroscedasticity (EGARCH) method indicates the non-stationary process of the residuals. There are systematic as well as unsystematic component embedded in the speculative behavior. Vector Error Correction Model (VECM) concludes that prices contain volatilities in the short run, but, it will revert to the mean in the long run. Investors’ beha
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Luo, Jinyang. "Utilizing the GARCH Model for Analysis and Prediction of Stock Market Trends." Advances in Economics, Management and Political Sciences 96, no. 1 (2024): 1–11. http://dx.doi.org/10.54254/2754-1169/96/2024mur0102.

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This study utilizes the Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) model to analyze the Shanghai Stock Exchange Composite Index and the Shenzhen Composite Index. It computes the returns and logarithmic returns, fits the GARCH model with a student-t distribution, and calculates the standardized residuals of the stock. Tests for normality and heteroskedasticity of residuals are conducted, follows by the creation of auto-correlation and partial auto-correlation plots to aid in volatility forecasting. The findings indicate that both returns of stock distributions exhibit mu
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Shahateet, Mohammed, Najib Shrydeh, and Suleiman Mohammad. "Testing the linkages of Arab stock markets: a multivariate GARCH approach." Investment Management and Financial Innovations 16, no. 4 (2019): 192–204. http://dx.doi.org/10.21511/imfi.16(4).2019.17.

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The authors undertook to examine 720 monthly observations of activity in 15 Arab stock markets over four years (from 2014 to 2017) to identify the dynamic linkages among those markets. To achieve this, several forms of the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model were utilized. Both panel and individual stationarity, in addition to cointegration tests, were employed to highlight the interaction between these markets. The results suggest that Arab stock markets have weak linkages with the exception of those of the Gulf Cooperation Council (GCC). The authors also
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Naik, Nagaraj, and Biju R. Mohan. "Stock Price Volatility Estimation Using Regime Switching Technique-Empirical Study on the Indian Stock Market." Mathematics 9, no. 14 (2021): 1595. http://dx.doi.org/10.3390/math9141595.

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Volatility is the degree of variation in the stock price over time. The stock price is volatile due to many factors, such as demand, supply, economic policy, and company earnings. Investing in a volatile market is riskier for stock traders. Most of the existing work considered Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) models to capture volatility, but this model fails to capture when the volatility is very high. This paper aims to estimate the stock price volatility using the Markov regime-switching GARCH (MSGARCH) and SETAR model. The model selection was carried out u
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Nagesh, C., Koushik Reddy Chaganti, Sathvik Chaganti, S. K. Khaleelullah, P. Naresh, and M. I. Thariq Hussan. "Leveraging Machine Learning based Ensemble Time Series Prediction Model for Rainfall Using SVM, KNN and Advanced ARIMA+ E-GARCH." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7s (2023): 353–58. http://dx.doi.org/10.17762/ijritcc.v11i7s.7010.

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Today's precipitation is growing increasingly variable, making forecasting increasingly difficult. The Indian Meteorological Department (IMD) currently employs Composite and Stochastic approaches to forecast spring storm precipitation in Asia. As a corollary, planners are unlikely to predict the macroeconomic effects of disasters (due to excessive precipitation) or famine (less precipitation). The amount of precipitation that drops dependent on a variety of factors, including the temperature of the atmosphere, humidity, velocity, mobility, and weather conditions. This paper would then employ t
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Rostan, Pierre, Alexandra Rostan, and Mohammad Nurunnabi. "Options trading strategy based on ARIMA forecasting." PSU Research Review 4, no. 2 (2020): 111–27. http://dx.doi.org/10.1108/prr-07-2019-0023.

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Purpose The purpose of this paper is to illustrate a profitable and original index options trading strategy. Design/methodology/approach The methodology is based on auto regressive integrated moving average (ARIMA) forecasting of the S&P 500 index and the strategy is tested on a large database of S&P 500 Composite index options and benchmarked to the generalized auto regressive conditional heteroscedastic (GARCH) model. The forecasts validate a set of criteria as follows: the first criterion checks if the forecasted index is greater or lower than the option strike price and the second
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Batu, Barın,. "Investigating Performance of ESN’s in Forecasting Financial Metrics When Compared To Traditional RNN Types." International Journal of Social Science and Economic Research 09, no. 06 (2024): 1950–82. http://dx.doi.org/10.46609/ijsser.2024.v09i06.023.

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This research investigates the performance of Echo State Networks (ESN) in forecasting financial metrics and compares their effectiveness against traditional recurrent neural network (RNN) architectures like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU), as well as Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) models. By analyzing datasets sourced from Yahoo Finance for various financial indices, exchange-traded funds and stocks over five years, this study examines the accuracy, and structural simplicity of ESNs in predicting close prices, daily vo
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Panda, Ajaya Kumar, and Swagatika Nanda. "Time-varying synchronization and dynamic conditional correlation among the stock market returns of leading South American economies." International Journal of Managerial Finance 14, no. 2 (2018): 245–62. http://dx.doi.org/10.1108/ijmf-11-2016-0206.

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Purpose The purpose of this paper is to capture the pattern of return volatility and information spillover and the extent of conditional correlation among the stock markets of leading South American economies. It also examines the connectedness of market returns within the region. Design/methodology/approach The time series properties of weekly stock market returns of benchmark indices spanning from the second week of 1995 to the fourth week of December 2015 are analyzed. Using univariate auto-regressive conditional heteroscedastic, generalized auto-regressive conditional heteroscedastic, and
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CHANG, B., and H. TSAI. "Forecast approach using neural network adaptation to support vector regression grey model and generalized auto-regressive conditional heteroscedasticity." Expert Systems with Applications 34, no. 2 (2008): 925–34. http://dx.doi.org/10.1016/j.eswa.2006.10.034.

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KANDUKURI, KUMARASWAMY, and BHATRACHARYULU N. CH. "New method of precipitation forecast model and validation." MAUSAM 74, no. 4 (2023): 1065–72. http://dx.doi.org/10.54302/mausam.v74i4.4359.

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There is a lot of time series data in many realistic sectors with different forecast techniques over the years. However there is no unanimous conclusion on forecast techniques such as individual forecasts Autoregressive, Moving averages, Autoregressive Moving average, Autoregressive Integrated Moving average, Artificial Neural Network, Long Short Term Memory network and Auto-Regressive Conditional Heteroscedasticity / Generalized Autoregressive Conditional Heteroskedasticity and combination of forecast (simple Average of forecasts, Minimum variance method, and Regression method of the combine)
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Ma, Lin, and Jean-Paul Delahaye. "An Algorithmic Look at Financial Volatility." Algorithms 11, no. 11 (2018): 185. http://dx.doi.org/10.3390/a11110185.

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In this paper, we attempt to give an algorithmic explanation to volatility clustering, one of the most exploited stylized facts in finance. Our analysis with daily data from five exchanges shows that financial volatilities follow Levin’s universal distribution Kirchherr et al. (1997) once transformed into equally proportional binary strings. Frequency ranking of binary trading weeks coincides with that of their Kolmogorov complexity estimated byDelahaye et al. (2012). According to Levin’s universal distribution, large (resp. small) volatilities are more likely to be followed by large (resp. sm
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Nusantara, Aji Cahya, and Budhi Haryanto. "The Influence of Sex Appeal on Consumers Attitude toward the Ads Moderated by Product Factors." Jurnal Dinamika Manajemen 9, no. 2 (2018): 250–58. http://dx.doi.org/10.15294/jdm.v9i2.15938.

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This study wants to examine the relationship between sex appeal and attitude towards the ads, and more than, this study also wants to examine the role of product factors in moderating the relationship of this two variables. Experimental design is done to control the relation among the variables observed in this study. The participants consist of 100 males’ undergraduate students of Faculty of Economics and Business, Universitas Sebelas Maret, Surakarta-Indonesia, who are divided into 4 groups. Generalized Auto Regression Conditional Hetero-regressive (GARCH) is statistical method chosen to ana
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