GARCH Method Research Articles

Determinants JCI of the Indonesian Stock Exchange (Comparison of IHSG Projection of OLS Method and ARIMA and GARCH Methods for the Period 2015-2023)

This study aims to examine the effect of interest rate, exchange rate, inflation, gross domestic product and LQ-45 index variables on the movement of the Composite Stock Price Index using the ordinary least square method. Comparison in predicting the movement of the stock price index through three models, and then compared with the projection results with OLS, ARIMA and GARCH methods. This research was conducted using monthly data on JCI, interest rates, exchange rates, inflation, gross domestic product and the LQ-45 index for the period May 2015 - December 2023. From the results it can be concluded that interest rates have a significant negative effect on JCI, exchange rates have a significant negative effect on JCI, inflation has a significant negative effect on the value of JCI, gross domestic product has a significant negative effect on JCI and the LQ-45 index has a positive effect on JCI. The results of the comparison of JCI projections with 3 models showed that the OLS model offers more accurate results and has the lowest MAPE value in predicting stock price movements.

Modeling Long Memory Volatilities of Nigeria Selected Macro Economic Variables with Arfima and Arfima Figarch

The research delved into analysing the stochastic characteristics of Nigeria's Real GDP, the exchange rate of the Naira to US Dollar, and the inflation rate employing Autoregressive fractionally integrated moving average (ARFIMA) and the Autoregressive Fractionally Integrated Moving Average Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (FIGARCH) modelling approach. The ability of the hybrid formation of ARFIMA-FIGARCH model with Nigeria macroeconomic variables in modeling the periodicity of long memory volatilities was examined. ARIMA GARCH method of modeling was also employed in analyzing the volatilities of Nigeria selected macroeconomic variables to enrich the study. The efficiency of ARFIMA, ARFIMA FIGARCH and ARIMA GARCH models were evaluated with the forecast evaluation measurements. Results revealed that ARFIMA FIGARCH and ARIMA GARCH models are more adequate in modeling the Inflation rate and the exchange rate while ARFIMA present more adequacies in modeling the RGDP. This result revealed evidence of high volatilities in Nigeria Inflation and the exchange rate of Naira to US dollar

Beyond GARCH: Intraday Insights Into the Exchange Rate and Stock PriceVolatility Dynamics in Borsa Istanbul Sectors

This study investigated the impact of exchange rate volatility on sectoral stockvolatility by employing the intraday volatility measure directly calculated fromthe original data, using daily data from 27 Borsa Istanbul sectors between April 29, 2003, and April 25, 2023. In the literature, GARCH models are commonly usedtostudy the volatility spillovers between exchange rates and stock prices, typicallyusing aggregate data. However, the GARCH family models provide inefficient andbiased estimates if they are misspecified. Moreover, using aggregate-level data maylead to biased and misleading conclusions. The research used intraday volatilitymeasures to overcome the shortcomings of GARCH models. The ordinary least squares (OLS), GARCH (1,1) methods, and Garman and Klass (1980) volatilityestimator are used. The empirical results showed that the estimates fromeachmethod vary significantly, and these disparities in the results might be due tomisspecification in GARCH (1,1) models. The intraday volatility model estimationresults showed that although stock price volatilities in all sectors are positively andsignificantly affected by exchange rate volatility, their magnitudes varysignificantly. Taken together, this implies the presence of vast heterogeneities inthe responses of sectoral stock price volatilities to exchange rate volatility. The results encourage policymakers to pay special attention to these heterogeneities toprevent capital flights and underinvestment. Additionally, the findings assist investors in making more effective decisions by helping themadapt their investment strategies to factor in exchange rate fluctuations and mitigate the impact of unexpected events in the exchange rate market

Exchange Rate Volatility and Foreign Remittances in SAARC Countries

This study explores the relationship between exchange rate volatility and foreign remittances on the economies of SAARC countries over the period 2000 to 2023. With the ongoing debate among policymakers and researchers on this subject, the study employs the Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) model to analyze data from eight SAARC nations. The variables considered include remittances, foreign reserves, trade, foreign direct investment (FDI), political stability, and GDP growth, sourced from the World Development Indicators (WDI), while exchange rate volatility is calculated using the GARCH method. The analysis involves two models. In the first model, remittances serve as the dependent variable, with exchange rate volatility, GDP growth, trade, inflation, and political stability as independent variables. In the second model, exchange rate volatility is the dependent variable, with remittances, GDP growth, trade, inflation, foreign reserves, and FDI as the independent variables. The results indicate both long-run and short-run relationships between exchange rate volatility and foreign remittances in SAARC countries. The findings show that remittances are positively associated with GDP growth and inflation, while exchange rate volatility, trade, and political stability have negative relationships with remittances. Additionally, exchange rate volatility has a positive association with inflation, while GDP growth, remittances, trade, FDI, and foreign reserves exhibit negative associations with exchange rate volatility. Granger causality tests reveal complex bidirectional and unidirectional causal relationships among the variables. These insights offer valuable implications for policymakers aiming to manage exchange rate volatility and enhance remittance inflows in the SAARC region.

Analysis of Ramadhan Effect on Fast Moving Consumer Goods (FMCG) Sector Companies in Indonesia Sharia Stock Index 2020 - 2023

Indonesia is a country with a majority Muslim population and is listed as the first country with the largest Muslim population in the worl. With investment levels also increasing and a Muslim-majority population, this will greatly support researchers in conducting research related to sharia shares. This research investigates the existence of an anomaly effect. Where this effect is supported by religious situations and conditions, namely the month of Ramadan, so it is called the Ramadan effect which has also been widely documented by researchers, with the FMCG (Fast Moving Consumer Good) company sector indexed by Indonesian sharia shares from 2020 to 2023 as the object of this research. The GARCH method is the model used in this research. Introduced by Bollerslev in 1986. This research was conducted quantitatively using secondary data on daily closing stock prices. The results of this research show that there is no effect of Ramadan on returns or volatility in FMCG (Fast Moving Consumer Good) sector shares listed on the Indonesian Sharia Stock Index (ISSI). There are only 3 companies that have a Ramadan effect on stock returns, including PT Martina Berto Tbk., PT Prima Cakrawala Abadi Tbk., and PT Sekar Bumi Tbk., then on volatility there are also 3 companies that have a Ramadhan effect, including PT Pantai Indah Kapuk Dua Tbk. ., PT Sekar Bumi Tbk., and PT Kimia Farma Tbk.. However, it was found in this research that in the month of Ramadan all the returns of the companies studied produced positive values with the majority of volatility being low.

Volatilitas Nilai Tukar dan Harga Komoditas Global selama Krisis Laut Merah

Purpose: This study aims to determine the most suitable model for assessing the volatility of currency exchange rates for Indonesia, Malaysia, and Singapore, as well as global commodity prices, specifically gold and crude oil. Methodology: The research was conducted by performing various tests using the SPSS version 25 application, followed by a volatility analysis employing the GARCH method with the E-Views version 9 application. Results: The study reveals that the Red Sea crisis significantly impacts global investment volatility. The EGARCH(1,1) model best describes the volatility of USD/IDR, USD/SGD, and crude oil prices, while the GJR-GARCH(1,1) model is most suitable for USD/MYR, and the GARCH(1,1) model fits gold price volatility. Limitations: This study is limited by its focus on only three countries (Indonesia, Malaysia, and Singapore) and two commodities (gold and crude oil). Additionally, the analysis only covers the period following the first attack in the Red Sea crisis. Contribution: This research provides valuable insights for decision-makers and investors by highlighting the impact of global events on market volatility.

The connections that bind: Political connectivity in the face of geopolitical disruption

While a firm can minimize its own political risk, there are idiosyncratic and country-specific risks that are more difficult to control. In particular, home country governments pursue their own foreign policies independently of business, forging international linkages with other countries in pursuit of tangible benefits. But what happens when a government forges connectivity to a country which exhibits volatility or generates geopolitical shocks? This paper examines this question by studying the response of European stock markets to the ongoing (since 2014) Russian invasion of Ukraine. Using a variety of metrics to measure political connectivity, we distinguish between anti-Russian governments and governments which are more favorable to Russia during this period, combining this connectivity data with a new database of sanctions and war-related events. Applying asymmetric GARCH, panel estimations, and event study methods, we find that the uncertainty caused by Russian aggression in Ukraine has harmed financial markets in countries such as Serbia and Hungary, countries which have willingly forged connections with Russia during this time. Consistently, our empirical results show that, by tying a country to another one via political means, politicians also have tied the fortunes of their capital markets to the success or failure of this partner.

Forecasting Method for Optimal Diversification

Forecasting is a technique that uses historical data as inputs to make estimates that are predictive in determining the direction of future trends. The goal of investors is to make optimal choice that leads to minimization of risk and maximization of returns, but the method that leads to these objectives has been a challenge for investor. In this study, Black-Litterman model (BLM) is adopted and two forecasting methods; EGARCH and GARCH methods are used for two parameters of BLM; investor views and level of uncertainty. The aim of this paper is to investigate the best forecasting method to estimate BLM that would lead to minimum risk and maximum returns. The analysis of this paper shows that EGARCH method gives maximum expected returns and minimum risk.

A Hybrid GARCH and Deep Learning Method for Volatility Prediction

Volatility prediction plays a vital role in financial data. The time series movements of stock prices are commonly characterized as highly nonlinear and volatile. This study is aimed at enhancing the accuracy of return volatility forecasts for stock prices by investigating the prediction of their price volatility through the integration of diverse models. Thus, the study integrated four powerful methods: seasonal autoregressive (AR) integrated moving average (MA), generalized AR conditional heteroskedasticity (ARCH) family models, convolutional neural network (CNN), and bidirectional long short‐term memory (LSTM) network. The hybrid model was developed using the residuals generated by the seasonal AR integrated MA model as input for the generalized ARCH model. Following this, the estimated volatility obtained was utilized as an input feature for both the hybrid CNNs and bidirectional LSTM models. The model’s forecasting performance was assessed using key evaluation metrics, including mean absolute error (MAE) and root mean squared error (RMSE). Compared to other hybrid models, our new proposed hybrid model demonstrates an average reduction in MAE and RMSE of 60.35% and 60.61%, respectively. The experimental results show that the model proposed in this study has good performance and accuracy in predicting the volatility of stock prices. These findings offer valuable insights for financial data analysis and risk management strategies.

Analysis of Google Stock Prices from 2020 to 2023 using the GARCH Method

Analysis of Google Stock Prices from 2020 to 2023 using the GARCH Method

A LATIN AMERICAN MARKET ASSET VOLATILITY ANALYSIS: A COMPARISON OF GARCH MODEL, ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR REGRESSION

The objective of this research was to compare the effectiveness of the GARCH method with machine learning techniques in predicting asset volatility in the main Latin American markets. The daily squared return was utilized as a volatility indicator, and the accuracy of the predictions was assessed using root mean square error (RMSE) and mean absolute error (MAE) metrics. The findings consistently demonstrated that the linear SVR-GARCH models outperformed other approaches, exhibiting the lowest MAE and MSE values across various assets in the test sample. Specifically, the SVRGARCH RBF model achieved the most accurate results for the IPC asset. It was observed that GARCH models tended to produce higher volatility forecasts during periods of heightened volatility due to their responsiveness to significant past changes. Consequently, this led to larger squared prediction errors for GARCH models compared to SVR models. This suggests that incorporating machine learning techniques can provide improved volatility forecasting capabilities compared to the traditional GARCH models.

ARIMA-GARCH Model Price Forecasting in PT. Unilever Indonesia Tbk

The purpose of this research is to predict the closing stock price of PT. Unilever Indonesia Tbk using the ARIMA-GARCH method. The data used in this study covers the period from February 20, 2020, to February 17, 2023, consisting of 734 daily observations. The data processing is performed using E-Views software. The closing stock price data of PT. Unilever Indonesia Tbk is non-stationary, thus requiring natural logarithm transformation and differencing. This is followed by model identification, parameter estimation, and diagnostic checking. The best-selected ARIMA model is ARIMA ([3,9],1,0), which accounts for the presence of heteroscedasticity. Subsequently, the GARCH method is applied, including model identification, parameter estimation, and diagnostic checking. The best GARCH model is GARCH (1,1), with the mean equation = 0,000047 + 0,230821 + 0,681968σ , which is free from heteroscedasticity effect. The forecast using the ARIMA ([3,9],1,0) GARCH (1,1) model yields a Mean Absolute Percentage Error (MAPE) of 2.423%, indicating a close approximation to the actual data. From the results of this research, the best model for forecasting PT. Unilever Indonesia Tbk for the next period was obtained. Therefore, the findings can assist PT. Unilever Indonesia Tbk and prospective investors in making decisions regarding the sale and purchase of shares in PT. Unilever Indonesia Tbk.

Analyzing spillover effects of selected cryptocurrencies on gold and brent crude oil under COVID-19 pandemic: Evidence from GJR-GARCH and EVT copula methods

This study examines the dependence structure and estimates the Value at Risk (VaR) and risk spillover between cryptocurrencies, oil, and Gold market data. In this paper, we estimate VaR by applying a hybrid approach of extreme value theory (EVT), copula functions, and GARCH models. We implement this method in a dual pair of selected data: the cryptocurrencies (Bitcoin and Ripple) and the Brent crude oil and gold markets. To estimate the VaR structure of these combinations, we initially employ asymmetric GARCH and EVT methods to model the marginal distributions of each return series and subsequently apply Copula functions (Gaussian, Student's t, Clayton, and Frank) to connect the marginal distributions into a multivariate distribution. Based on the results, we infer that, generally, the GARCH-EVT-Copula approach works well and is better than traditional methods (e.g., Historical Simulation (HS) and variance-covariance (VC)). These results show that the GARCH-EVT-Copula model is the best method for examining the dependence structure. Finally, we implement the technique of Diebold-Yilmaz to compute the spillover index between four data before and during the COVID-19 pandemic. From this part, we conclude that cryptocurrencies have spillovers on each other, but their spillovers on Gold and oil are very small. This phenomenon implies that cryptocurrencies are tough to hedge against other financial assets but are appropriate diversifiers in an investor's portfolio. The current study's findings benefit all investors and financial market professionals who want to learn about the cryptocurrency market as a new asset class or incorporate it into their investment strategies or portfolios.

Fuzzy Gaussian GARCH and Fuzzy Gaussian EGARCH Models: Foreign Exchange Market Forecast

This article discusses a comparison of the GARCH and EGARCH conditional variance methods, with respect to the Fuzzy Gaussian GARCH and Fuzzy Gaussian EGARCH. The returns of four exchange rates were forecasted at daily periodicity from January 2015 to November 2022 and out-of-sample, January 2019, and December 2022. The results indicate that the Fuzzy GARCH and Fuzzy EGARCH models better estimate the volatility behaviour of the exchange market series compared to traditional techniques. Therefore, the recommendation is to estimate other high volatility variables to verify the efficiency of the fuzzy techniques, however, the main limitation is that it is not possible to apply traditional econometric tests for fuzzy techniques because the parameters are not estimated with the logarithm of maximum likelihood. The estimation of the parameters of GARCH and EGARCH models with fuzzy theory is the originality proposal. In conclusion, fuzzy methodologies have less error in forecasting the volatility of in-sample and out-of-sample exchange rates.

PERFORMANCE OF THE ACCURACY OF FORECASTING THE CONSUMER PRICE INDEX USING THE GARCH AND ANN METHODS

The Consumer Price Index (CPI) is the most widely used indicator of the inflation rate. Then, the value of CPI in the future must be known to be the basis of the government's making appropriate and accurate policies. The CPI data used in this study was taken from the Central Statistics Agency (BPS) from January 2006 - to December 2021. The CPI data used has a data pattern containing symptoms of heteroskedasticity. To overcome the symptoms of heteroskedasticity, the author uses the GARCH and ANN methods to determine the value of CPI in the future. The GARCH method can overcome the symptoms of heteroskedasticity in the time series forecasting process, while ANN is an effective method in time series forecasting because of its high level of accuracy. In this study, mape error calculation results were obtained with the ARIMA model (4,2,2)~GARCH(1.1) of 3.19% or with an accuracy of 96.81%, and ANN using two hidden layers of 1.24% or with an accuracy of 98.76%. Thus, the results of this study show that the ANN method is the best method of forecasting Consumer Price Index (CPI) data.

Comparison of Black-Scholes and GARCH Option Models on The Jakarta Islamic Index with Collar Strategy

Objective –The revival of the Islamic economy in Indonesia was marked by the establishment of the Jakarta Islamic Index (JII) on July 3, 2000. From 2000 to 2020, two economic crises hit Indonesia in 2008 and 2020. These crises also had an impact on the high price fluctuations of JII. Assets can be protected from significant losses by hedging. One of the hedging strategies was option contracts with a collar strategy. Methodology – The data analysed in this research were the JII closing price data from 2000 to 2020 using the Black Scholes model and the GARCH model with the Collar strategy. Findings – Option with the Collar strategy provided more benefits in crises than without Option. In crisis conditions, the Option with the GARCH method and the collar strategy provided a higher average profit of 3.07% on the one-month Option and 7.01% on the three-month Option than without using the Option with the collar strategy. In non-crisis conditions, the Option using the GARCH method, and the collar strategy provided a 0.16% higher average profit at one-month maturity than without the Option. Meanwhile, the average profit decreased by 1.45% at the three-month maturity. The collar strategy resulted in less volatility. Without a collar strategy, JII volatility was 103%. The collar strategy produced maximum volatility of 12.71%, 15.18%, and 17.14%. The findings also revealed that the GARCH model was better than the Black Scholes based on the AMSE value obtained in crisis conditions with a maturity of one month and three months and in non-crisis conditions with a maturity of one month. Meanwhile, the Black Scholes model showed better results in non-crisis conditions with a maturity of three months. Novelty – Based on the research result, the Option with a collar strategy is effective for hedging in crisis conditions. This research assesses the collar strategy with several different maturity values to get the most effective value. Type of Paper: Empirical JEL Classification: G11, G13. Keywords: Black Scholes; Collar Strategy; GARCH; Jakarta Islamic Index; Option Contracts. Reference to this paper should be made as follows: Hendrawan, R; Arifin, Z. (2023). Comparison of Black-Scholes and GARCH Option Models on The Jakarta Islamic Index with Collar Strategy, J. Fin. Bank. Review, 7(4), 16 – 27. https://doi.org/10.35609/jfbr.2023.7.4(2)

External energy security elements and the riskiness of clean energy stocks: a volatility analysis

PurposeClean energy stocks are exhibiting signs of increasing volatility reflecting the varied and conflicting strategies employed by nations to pursue energy security objectives. In this regard, this paper aims to examine the response of NASDAQ clean energy stock returns volatility to the influences of external energy security elements including oil price, natural gas price, coal price, carbon price and green information technology stock price.Design/methodology/approachThe paper uses symmetric and asymmetric generalised autoregressive conditional heteroskedasticity models (GARCH and TGARCH, respectively), which incorporate external energy security elements as exogenous variables, to estimate volatility models for clean energy stock returns.FindingsAlthough, prices of oil, coal and natural gas are negatively associated with NASDAQ clean energy returns volatility, only the effect of natural gas price is significant. While carbon price affects NASDAQ clean energy returns volatility positively, green information technology price affects the volatility negatively. These results are robust to exponential GARCH and lead-and-lag robust ordinary least-squares as alternative estimation methods.Research limitations/implicationsThe study lumps the effects of all other external and internal factors, including internal energy security elements, in the autoregressive conditional heteroscedasticity (ARCH) term to predict NASDAQ clean energy returns conditional variance. GARCH method does not disentangle individual roles of the factors captured in the ARCH term in predicting volatility.Practical implicationsResults documented imply that natural gas appears a closer substitute for renewable energy sources than crude oil and coal, such that its price rise is perceived as good news in the NASDAQ clean energy financial market, while a fall is considered bad news. Furthermore, both an increase in carbon price and a decrease in green information technology stock performance are perceived as negative shocks.Social implicationsIn assessing risks associated with clean energy stocks, investors and fund managers should carefully consider the effects of external energy security elements.Originality/valueTo the best of the author’s knowledge, the paper is the first to identify external energy security elements and examine their effects on clean energy stock volatility.

Crypto Price Prediction as an Investment Opportunity: An Empirical Study of Three Global Cryptocurrencies

Aims: To determine the investment feasibility of evaluating cryptocurrency opportunities as an investment product under the possibility of crypto price valuation selection. The study analyzes three indicators: asset price returns in unrelated time, selection of cryptocurrency investment price weights, and crypto price forward contract opportunities on ARCH-GARCH probability forecasts in the selection of price valuations by individual cryptocurrency prices.
 Study Design: Quantitative research.
 Place and Duration of Study: The period from 10 September 2021 to 4 September 2022 using sample data downloaded from the Yahoo Finance website database with metric data retrieval bound in amount, data quantity, or distance relative to writing opportunities to examine the distribution of the amount of research data.
 Methodology: This study employed Bitcoin (BTC), Ethereum (ETH), and Tether (USDT) cryptocurrencies as the research objects with used panel and multiple regression analysis methodologies and using forecasting the appropriate ARCH and GARCH methods
 Results: The results show that the prediction of future crypto price selection in BTC and ETH tokens has a probability of 78.6% and 59.6%, respectively. The study highlights the prediction of future BTC and ETH price selection with 79.21% and 78.64% forecast results as found in the ARCH-GARCH(1, 0, 1) technique. Meanwhile, USDT token has no possibility to be forecasted in the future, leaving a 7.3% possibility of crypto price selection under probability by investors in the form of high (or different) price fluctuation inequalities.
 Conclusion: Conclusions could state that the partial (combined) selection of crypto coin price assessments and individual crypto assets can reduce the expected return from the selection of the asset price so that this form of investment in crypto assets can reduce the level of observation of return on wealth from crypto assets for investors especially in expecting the chance on that investment.

Volatility Spillover Between Bitcoin And Financial Stress Index

Purpose: This paper aims to test the volatility models for Bitcoin (BTC) and the financial stress index (FSI) and examine the volatility spillover among them. This aim was reached by obtaining weekly data from the 7th of January 2011 and the 24th of December 2021. Methodology: First, volatility modelling for the series is provided, and GARCH (1,1) for the BTC series and IGARC (1,2) for the FSI series are determined as the most appropriate volatility models. Then, residual volatility series are created for each variable over the IGARCH (1,2) and GARCH (1,1) models for the volatility spread between the series. The volatility spread between the series is examined with the diagonal VECH GARCH method. It is concluded that there is a positive volatility spillover effect from the FSI variable to the BTC variable. Then, impulse-response analysis is performed on the volatility residual series created for each variable. The empirical findings from impulse response analysis support a risk transfer between BTC and FSI series. Results and Findings: Changes in the BTC return series and FSI series are caused mainly by themselves, and the series are most affected by their shocks. By comparing the variance decomposition of the volatility series with the analysis results, it can be said that the changes in the volatility series are caused mainly by each other.

ANALISIS VOLATILITAS HARGA DAGING SAPI MURNI DI PROVINSI JAWA TENGAH DENGAN PENDEKATAN ARCH GARCH

Indonesia has an important commodity for the community, namely beef including in Central Java Province. One of the foodstuffs that produce protein is beef where its usefulness is important to meet human nutritional needs. Besides being important for consumption needs, this commodity also contributes in economic terms because beef is produced by the community ranging from small to large scale. This research further leads to reviewing the volatility of beef prices in Central Java Province through the ARCH GARCH method and daily data (time series) of beef on January 1, 2020 to December 31, 2020. The results of the study showed the most appropriate model for calculating the volatility of beef prices is the model (1,2). The results of the model predictions show that the movement of beef price volatility tends to be stable when after eid al-Fitr, and it is expected that changes or spikes in beef prices in the future will be less minimal.