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In this paper the generalized autoregressive conditional heteroscedastic models are applied in modeling exchange rate volatility of the USD/KES exchange rate using daily observations over the period starting 3 rd January 2003 to 31 st December 2015. The paper applies both symmetric and asymmetric models that capture most of the stylized facts about...
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... By modeling ICBP's daily stock returns with a GARCH-M approach, it is possible to gain insights into the volatility-return relationship and assess the compensation investors require for bearing risk. some empirical studies showed that the GARCH-M and GJR GARCH models work well, e.g., in [11][12][13][14][15]. The objective of this study is to predict the volatility of daily stock returns of PT Indofood CBP Sukses Makmur Tbk. using the GARCH-M model. ...
The LQ45 Index was observed to be in the red zone, with a decline of 9.64% year-to-date (YTD), reaching the level of 877.02. The LQ45 Index became increasingly weakened following the announcement of Donald Trump's victory in the U.S. presidential election, which impacted the Indonesian capital market. It was recorded that the LQ45 Index fell by 5.3% during the final trading month of 2024. Nevertheless, there remains a potential for strengthening the stock prices of LQ45 constituent issuers in the remainder of this year, particularly in December 2024. One of the stocks recommended by IDX is PT Indofood CBP Sukses Makmur Tbk., which has also been one of the most liquid companies according to IDX throughout 2024. The return volatility of stocks in emerging markets is generally much higher than that of developed markets. High volatility reflects a higher level of risk faced by investors, as it indicates significant fluctuations in stock price movements. Therefore, equity investments in Indonesia carry a potentially high level of risk. A common characteristic of financial time series data, particularly return data, is that the probability distribution of returns exhibits fat tails and volatility clustering, often referred to as heteroscedasticity. Time series models that can be used to model these conditions include ARCH and GARCH models. One variation of the ARCH/GARCH models is the Generalized Autoregressive Conditional Heteroscedasticity in Mean (GARCH-M) model, which incorporates the effect of volatility into the mean equation. The purpose of this study is to predict volatility using the GARCH-M model in the analysis of daily closing price return data of PT Indofood CBP Sukses Makmur Tbk. The best model used for volatility forecasting is ARIMA(2,0,1) GARCH(1,1)-M.
... The literature shows that the exchange rate volatility has asymmetric shock on the export (Miron and Tudor, 2010;Narsoo, 2015;Omari et al., 2017). Obeng (2018) states that, over time, researchers realised the fluctuating effects of volatility were unequal and could either be negative or positive such that agents responded to these uneven effects differently. ...
Despite the extensive literature on the exchange rates volatility and international trade, there is no consensus in the literature. This study examines how South African exports demand is affected by exchange rate volatility. The sample period covers the period from the year 2000 first quarter to the beginning of 2021 first quarter. To estimate the volatility of the exchange rates, in this study, we have used the Generalised Autoregressive Conditional Heteroscedastic (GARCH) mode. While we use Autoregressive Distributed lags (ARDL) models to estimate the impact of exchange rates volatility on domestic exports. The findings suggested that there is a positive relationship between exchange rate volatility and exports. Hence, policies such as bilateral trade agreements are important to promote export growth.
... Contrarily, Thorlie, Song, Wang, and Amin [40] found negative asymmetry in the Sierra Leone/USA dollars exchange rate returns computed from the monthly data from January 2004 to December 2013 while using asymmetric GJR-GARCH models. In Kenya, Omari, Mwita, and Waititu [41] found asymmetry and the presence of a negative leverage effect in Kenya's daily exchange rates spanning 3 rd January 2003 to 31 st December 2015 while using the AR (2)-E-GARCH (1, 1) and AR (2)-GJR-GARCH (1, 1). The findings vary due to the different time frames used to estimate volatility. ...
Modelling and forecasting the volatility of a financial time series has become essential in many economic and financial applications like portfolio optimization and risk management. The symmetric-GARCH type models can capture volatility and leptokurtosis. However, the models fail to capture leverage effects, volatility clustering, and the thick tail property of high-frequency financial time series. The main objective of this study was to apply the asymmetric-GARCH type models to Kenyan exchange to overcome the shortcomings of symmetric-GARCH type models. The study compared the asymmetric Conditional Heteroskedasticity class of models: EGARCH, TGARCH, APARCH, GJR-GARCH, and IGARCH. Secondary data on the exchange rate from January 1993 to June 2021 were obtained from the Central Bank of Kenya website. The best fit model is determined based on parsimony of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Log-Likelihood criterion, and minimisation of prediction production errors (Mean error [ME] and Root Mean Absolute error [RMAE]). The optimal variance equation for the exchange rates data was APARCH (1,1) - ARMA (3,0) model with a skewed normal distribution (AIC = -4.6871, BIC = -4.5860). Volatility clustering was present in exchange rate data with evidence of the leverage effect. Estimated Kenya’s exchange rate volatility narrows over time, indicating sustained exchange rate stability.
... Additionally (Epaphra, 2017) States has a positive impact on the USD/EUR, USD/Yuan, and USD/LivreSterling. While In another paper by (Omari et al., 2017), both symmetric and asymmetric GARCH models are used. The paper finds strong evidence that the aforementioned models can characterize daily returns. ...
The purpose of the study is to forecast the volatility for returns of the exchange rate of Pakistan concerning US dollars along with the impact of covid-19 so that we can find out the feasibility of holding this asset along with the risk and returns associated with it. For this purpose daily data has been taken from the State bank of Pakistan on a period from February 01, 2001, to June 30, 2021, where covid-19 is used as a dummy variable. Furthermore, in methodology, we applied GARCH models after finding the presence of the ARCH effect which is at ARCH (6) in the series. It is found in all GARCH models that the past volatility of the exchange rate returns has a statistically significant influence on the current volatility of the exchange rate means there is time-varying and time-correlated volatility associated with exchange rate returns. According to GARCH-M, GARCH-M (variance) (1,1) and GARCH-M (SD) (1,1) results it is concluded that average returns of exchange rate are small but significant and there is no risk factor associated with exchange rate returns but the past square residual terms have a significant impact on risk volatility. Furthermore, Both T-GARCH and E-GARCH depicts that the impact of covid-19, which is bad news, although has a significant impact but its magnitude has a lesser influence on exchange rate volatility than the good news.
... Many scholars demonstrated application of symmetric and asymmetric GARCH models that fitted well to time-series returns. For instance, Omari, et al. (2017) employed APARCH, GJR, EGARCH to capture volatility cluster and leverage effect on exchange rate. The work clearly proved that APARCH, GJR and EGARCH best fit to explore volatility clusters. ...
This paper proposes empirical analysis specifically on Volatility Clustering using GARCH class models-GARCH, EGARCH, GJR, APARCH and AGARCH estimation considering samples of Asian stock markets, i.e., Japan, China, India, Philippines, Sri Lanka and Indonesia using data set from January 1990 to March 2020. Result finds the performance of symmetric and asymmetric models for selected samples of Asian Stock Markets indicating yearly volatility forecast based on historical data, average volatility parametric and presence of leverage effects with high degree of volatility clustering.
... The assumptions of ARCH is also related to GARCH model too. (Dritsaki, (2018) [8] , Omari et al., (2017) [19] and Abdalla, (2012) [1] . ...
... The assumptions of ARCH is also related to GARCH model too. (Dritsaki, (2018) [8] , Omari et al., (2017) [19] and Abdalla, (2012) [1] . ...
... The estimation of exchange rate log return is based on previous studies. According to Talwar & Bhat, (2018) [23] Dritsaki, (2018) [8] , Omari et al., (2017) [19] , Epaphra, (2017) [10] and Abdalla, (2012) [1] mentioned as in most of empirical finance literature, the variable to be modelled is percentage monthly exchange rate return which is the first difference of the natural logarithm of the exchange rate and the equation can written as follows. ...
This study considers the generalized autoregressive conditional heteroskedastic approach in modeling exchange rate volatility of the five major currencies of Sri Lanka using daily observations over the period of 7th of May 2010 to 31st December 2019. The currencies considered are United States Dollar
(USD), Euro (EUR), British Pound (GBP), Japanese Yen (JPY) and Indian Rupees (INR), all against
Sri Lanka Rupee (LKR). The study applied symmetric ARCH (q) and GARCH (p, q) models that
estimate exchange rate volatility with the normal distribution. The result of the study shows that the
three currencies fulfill the conditions of volatility models and these currencies modeled by GARCH.
ARIMA (2, 1, 2,) -GARCH (1, 1) specification is proven to be the best model to estimate GBP
exchange rate volatility. ARMA (1, 1) –GARCH (1, 1) is more appreciate for JPY and USD exchange
rate volatility and it is required to have a mean-reverting variance process for JPY exchange rate.
ARMA (2, 2) -GARCH (2, 1) is a best fit model for INR exchange rate volatility Finally the study
concluded that the exchange rate volatility can be adequately modeled by GARCH model.
Keywords: Exchange rate, Volatility, GARCH Model, Heteroscedasticity
... ing dummy, was the best model in terms of forecasting exchange rate volatility. The model also succeeded to control the leverage effect. The three models of forecasting, ARIMA, SARIMA, and SVAR had been evaluated. The comparison of prediction techniques through RMSE and MAE formulas showed that SARIMA model was much accurate against the rest. And ;Omari et. al. (2017) in their paper applied GARCH family models in modelling exchange rate volatility of the USD against Kenyan Shilling exchange rate of a data set of daily prices of USD/KES over a timeframe from January 2003 to December 2015. The performance of GARCH (1,1) and GARCH-M symmetric models in addition to EGARCH (1,1), APARCH (1,1), and GJR-GAR ...
... The main characteristic of financial time-series which are high-frequency values, volatility clustering, excess kurtosis, heavy-tailed distribution, leverage effect, and long memory properties (Omari et al, 2017) have been examined using the Autoregressive Conditional Heteroscedasticity (ARCH) and its Generalised form GARCH models. In this paper different models under the GARCH ISSN: 2548-0162 © 2021 Gazi Akademik Yayıncılık 4 family models have been used. ...
... e ARCH model could be developed to capture the characteristics and dynamics of the time series much better. Thereafter, the GARCH (p, q) model was first presented by Bolleslev (1986). New GARCH related models have been invented to include the incompetence of the original GARCH and capture the different characteristics of the financial time series. (Omari et. al., 2017). ...
This paper aims to model the volatility of USD and EUR exchange rates against TRY for the period from January 2005 to December 2019 using the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models. Both symmetric and asymmetric models have been applied to measure factors that are related to the exchange rate returns such as leverage effect and volatility clustering. The symmetric GARCH (1,1) model and the asymmetric EGARCH (1,1), GJR- GARCH (1,1), and PGARCH (1,1) have been applied to each currency against TRY. The results of this paper conclude that the most adequate model for estimating volatility of the USD/TRY exchange rates are the symmetric GARCH (1,1) and asymmetric GJR-GARCH (1,1) models. Moreover in USD/TRY returns, GARCH (1,1) and GJR-GARCH (1,1) models are the most appropriate models along with PGARCH (1,1) in EUR/TRY as well. Regarding forecasting volatility, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) tests have been used. Based on the results, the static forecast of GJR-GARCH (1,1) is the best model in predicting the future pattern for both USD and EUR.
... The GARCH model provides a good technique for analysing financial time series and estimating conditional variance. [6]. ...
... The asymmetric GARCH showed the present of leverage effect for majority of the currencies. [6], examine GARCH model of USD/KES exchange rate return and fund that the asymmetric power autoregressive conditional variance heteroscedasticity (APARCH)model is adequatefor exchange rate series. ...
s: This study applied Autoregressive conditional heteroscedasticity (ARCH) models in modelling Nigeria inflation rate. The time plot of the original series showed the present of seasonality and logarithm transformation of return series make it stationary. The return was estimated using both the conditional mean and conditional variance. The study applied both symmetric and asymmetric (GARCH) model that capture the feature of a financial series, such as volatility clustering and leverage effect in modelling the return series of inflation. However, four models were estimated for the conditional mean and seven models were estimated for the conditional variance and asymmetric power autoregressive conditional heteroscedasticity (APARCH (1,1)) was adopted as the best model for the return series and for the conditional mean follow an ARMA (1,1). Finally, the most adequate model for estimating volatility of the inflation rates is the asymmetric APARCH (1.1) model.
... (Abdalla, 2012) conducted finding to model exchange rate of nineteen Arab countries while using GARCH models find out the existence This Journal is licensed under a Creative Commons Attribution 4.0 International License leverage effect which indicates that negative effect have large effect on volatility than positive change of same magnitude. There is presence of volatility clustering and leverage effect on USD against Kenyan shilling as confirmed by the work of (Omari et al., 2017). (Epaphra, 2016) find that current exchange rate volatility depends on its previous fluctuation and presence of leverage effect although positive shocks have more effect on volatility than negative shocks of same size as he elaborated by using Tanzania shilling against USD. ...
The main aim of this investigation was to model the volatility of Somali shilling against US dollar by using monthly data covering from 1950 to 2010. Further to that, this finding has adopted both symmetric and asymmetric generalized autoregressive conditional heteroscedastic (GARCH) family models in order to capture volatility clustering and leverage effect as the most stylized facts of exchange rate returns. Result from ARCH indicates presence of conditional heteroscedasticity in the residual series of exchange rate. Symmetric GARCH(1,1) model shows presence of volatility clustering and persistent coefficients of greater than one indicating that volatility is an explosive process. Results from asymmetric TCHARCH(1,1) and EGARCH(1,1) indicates presence of leverage effect in the series of exchange rate where positive news have large effect on volatility than bad news of same magnitude. This study has an important implication to investors, business and risk managers. Nevertheless, this study suggest monetary authority to print new currency and de-dollarize the economy in order to be able influence exchange rate volatility. The outcome from this finding also suggests that GARCH family models sufficiently capture the volatility of Somali shilling against US dollar.
... Using symmetric lost functions (MAE, RMAE, MAPE and Thiel's U), their results further showed that TGARCH provided accurate forecasts. Omari et al. (2017) used data on daily returns of KES/USD between 2003 and 2015 to investigate stylized facts about exchange rates in both symmetric and asymmetric sets of models. They specifically investigated GARCH (1,1) and GARCH-M (1,1) for symmetric models and EGARCH, GJR-GARCH (1,1) and APARCH (1,1) for the asymmetric set under different distributions. ...
Symmetric and asymmetric GARCH models-GARCH (1,1); PARCH(1;1); EGARCH(1,1,); TARCH(1,1) and IGARCH(1,1)- were used to examine stylized facts of daily USD/UGX return series from September 1st, 2005 to August 30th, 2018. Modeling and forecasting were performed based on Gaussian, Student’s t and GED distribution densities with a view to identifying the best distribution for examining stylized facts about the volatility of returns. Initial tests of heteroscedasticity (ARCH-LM), autocorrelation and stationarity were carried out to establish specific data requirements before modeling. Results for conditional variance indicated the presence of significant asymmetries, volatility clustering, leptokurtic distribution, and leverage effects. Effectively, PARCH (1,1) under GED distribution provided highly significant results free from serial correlation and ARCH effects, thus revealing the asymmetric responsiveness and persistence to shocks. Forecasting was performed across distributions & assessed based on symmetric lost functions (RMSE, MAE, MAPE & Thiel’s U) and information criteria (AIC, SBC & Loglikelihood). The information criteria offered a preference for EGARCH (1,1) under GED distribution while symmetric lost functions provided very competitive choices with very slight precedence for GARCH (1,1) and EGARCH (1,1) under GED distribution. Following these results, it’s recommended that PARCH (1,1) and EGARCH (1,1) be respectively preferred for modeling and forecasting volatility with GED as the choice distribution. Given the asymmetric responsiveness and persistence of conditional variance, macroeconomic & fiscal adjustments in addition to stabilization of the internal political environment are advised for Uganda. Keywords: Forecasting volatility, GARCH family Models, Probability Distribution Density, Forecast accuracy.JEL Classifications: C58, C53, G17, F31DOI: https://doi.org/10.32479/ijefi.9016
