Modeling USD/KES Exchange Rate Volatility using GARCH Models

Omari, Cyprian Ondieki; Mwita, Peter N.; Waititu, Antony Gichuhi

doi:10.9790/5933-0801011526

Cited by 9 publications

(9 citation statements)

References 13 publications

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“…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 family models have been used.…”

Section: Methodsmentioning

confidence: 99%

“…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).…”

Section: Symmetric Garch Modelsmentioning

confidence: 99%

“…When the value of I is one it indicates bad news and negative errors are leveraged. Negative or bad news has a greater effect than the positive ones (Omari et. al., 2017).…”

Section: The Glosten Jagannathan and Runkle Garch (Gjr-garch Model)mentioning

confidence: 99%

“…The comparison of prediction techniques through RMSE and MAE formulas showed that SARIMA model was much accurate against the rest. And; Omari et. al.…”

Section: Introductionmentioning

confidence: 98%

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Modelling exchange rate volatility using GARCH models

ALMISSHAL¹

2021

GJEB

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Symmetric Garch Modelsmentioning

confidence: 99%

“…When the value of I is one it indicates bad news and negative errors are leveraged. Negative or bad news has a greater effect than the positive ones (Omari et. al., 2017).…”

Section: The Glosten Jagannathan and Runkle Garch (Gjr-garch Model)mentioning

confidence: 99%

“…The comparison of prediction techniques through RMSE and MAE formulas showed that SARIMA model was much accurate against the rest. And; Omari et. al.…”

Section: Introductionmentioning

confidence: 98%

See 2 more Smart Citations

Modelling exchange rate volatility using GARCH models

ALMISSHAL¹

2021

GJEB

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“…However, current finance literature appears to focus mainly on univariate approaches. Very few studies have addressed multivariate approaches (Omari et al , 2017) since it is relatively complex to select an appropriate model and subsequently assess its performance. For instance, VaR and ES models constructed for a portfolio of financial assets will need to capture the fluctuations of the underlying assets and also model the dependence structure between them, which can prove to be a major challenge for researchers.…”

Section: Introductionmentioning

confidence: 99%

Forecasting multivariate VaR and ES using MC-GARCH-Copula model

Badaye

Narsoo

2020

JRF

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Purpose This study aims to use a novel methodology to investigate the performance of several multivariate value at risk (VaR) and expected shortfall (ES) models implemented to assess the risk of an equally weighted portfolio consisting of high-frequency (1-min) observations for five foreign currencies, namely, EUR/USD, GBP/USD, EUR/JPY, USD/JPY and GBP/JPY. Design/methodology/approach By applying the multiplicative component generalised autoregressive conditional heteroskedasticity (MC-GARCH) model on each return series and by modelling the dependence structure using copulas, the 95 per cent intraday portfolio VaR and ES are forecasted for an out-of-sample set using Monte Carlo simulation. Findings In terms of VaR forecasting performance, the backtesting results indicated that four out of the five models implemented could not be rejected at 5 per cent level of significance. However, when the models were further evaluated for their ES forecasting power, only the Student’s t and Clayton models could not be rejected. The fact that some ES models were rejected at 5 per cent significance level highlights the importance of selecting an appropriate copula model for the dependence structure. Originality/value To the best of the authors’ knowledge, this is the first study to use the MC-GARCH and copula models to forecast, for the next 1 min, the VaR and ES of an equally weighted portfolio of foreign currencies. It is also the first study to analyse the performance of the MC-GARCH model under seven distributional assumptions for the innovation term.

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Empirical performance of GARCH, GARCH-M, GJR-GARCH and log-GARCH models for returns volatility

Nugroho

Kurniawati

Panjaitan

et al. 2019

J. Phys.: Conf. Ser.

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Volatility plays an important role in the field of financial econometrics as one of the risk indicators. Many various models address the problem of modeling the volatilities of financial asset returns. This study provides a new empirical performance comparison of the four different GARCH-type models, namely GARCH, GARCH-M, GJR-GARCH, and log-GARCH models based on simulated data and real data such as the DJIA, S&P 500, and S&P CNX Nifty indices on a daily period from January 2000 to December 2017. We also investigate the estimation results obtained using Solver’Excel and verify those results against the results obtained using a Markov chain Monte Carlo method. The simulation study showed that the GARCH model is outperformed by other models. Meanwhile, the empirical study provides evidence that the GJR-GARCH model provides the best fitting, followed by the GARCH-M, GARCH, and log-GARCH models. Furthermore, this study recommends the use of Excel’s Solver in practice when the parameter estimates for GARCH-type model do not close to zero.

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Modeling USD/KES Exchange Rate Volatility using GARCH Models

Cited by 9 publications

References 13 publications

Modelling exchange rate volatility using GARCH models

Modelling exchange rate volatility using GARCH models

Forecasting multivariate VaR and ES using MC-GARCH-Copula model

Empirical performance of GARCH, GARCH-M, GJR-GARCH and log-GARCH models for returns volatility

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