A Comparison of Linear and Nonlinear Models in Forecasting Market Risk: The Evidence from Turkish Derivative Exchange

Akarim, Yasemin Deniz

doi:10.22610/jebs.v5i3.391

“…Indeed, it is commonly stated that the neural network model remains the most performant compared to heteroscedastic models. Deniz,Y. et Akkoc,S.…”

Section: Introductionmentioning

confidence: 99%

Cross-Cultural Lessons & Implications for Marketing Soccer in the USA

Devasagayam¹,

Corcione²

2016

IJARBSS

0

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Modeling and forecasting of dynamics nominal exchange rate has long been a focus of financial and economic research. Artificial Intelligence (IA) modeling has recently attracted much attention as a new technique in economic and financial forecasting. This paper proposes an alternative approach based on artificial neural network (ANN) to predict the daily exchange rates. Our empirical study is based on a series of daily data in Tunisia. In order to evaluate this approach, we compare it with a generalized autoregressive conditional heteroskedasticity (GARCH) model in terms of their performance. Results indicate that the proposed nonlinear autoregressive (NAR) model is an accurate and a quick prediction method. This finding helps businesses and policymakers to plan more appropriately.

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“…This makes ANNs well suited for volatility prediction and has led to increase research in this area. The volatility forecast models such as: ARCH and GARCH were compared with ANNs for Istanbul Stock Exchange 30 (ISE30) by (AkarÄ±m, 2013). Returns series of BP/USD, DEM/USD, JPY/USD, and EUR/USD were modeled and forecasted via the family of GARCH models; ARCH, GARCH, Integrated (IGARCH), GARCH (1, 1)-M and EGARCH models (Dhamija and Bhalla, 2010).…”

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confidence: 99%

Epedimiological Survey on Trypanosomiasis in Dromedary Camels of District Tank Khyber Pakhtunkhwa, Pakistan

Khan¹,

Amin²,

Safiullah³

et al. 2023

VSSR

0

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“…This makes ANNs well suited for volatility prediction and has led to increase research in this area. The volatility forecast models such as: ARCH and GARCH were compared with ANNs for Istanbul Stock Exchange 30 (ISE30) by (AkarÄ±m, 2013). Returns series of BP/USD, DEM/USD, JPY/USD, and EUR/USD were modeled and forecasted via the family of GARCH models; ARCH, GARCH, Integrated (IGARCH), GARCH (1, 1)-M and EGARCH models (Dhamija and Bhalla, 2010).…”

mentioning

confidence: 99%