VaR Estimation with Power EWMA Model - Conservativeness, Accuracy and Efficiency

Liu, Mei-Ying; Wu, Kenny; Lee, Hsien-Feng

doi:10.2139/ssrn.494285

Cited by 3 publications

(1 citation statement)

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“…Value at Risk (VaR) has emerged as a widely used statistical tool for risk management of financial institutions [40]. This method is most commonly used by financial institutions to determine the occurrence probability of thee potential losses in their financial portfolios.…”

Section: Extended Min-max Normalization a Value At Riskmentioning

confidence: 99%

Mixture of Activation Functions With Extended Min-Max Normalization for Forex Market Prediction

Munkhdalai

Park

et al. 2019

IEEE Access

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An accurate exchange rate forecasting and its decision-making to buy or sell are critical issues in the Forex market. Short-term currency rate forecasting is a challenging task due to its inherent characteristics, which include high volatility, trend, noise, and market shocks. We propose a novel deep learning architecture consisting of an adaptive activation function selection mechanism to achieve higher predictive accuracy. The proposed architecture is composed of seven neural networks that have different activation functions as well as softmax layer and multiplication layer with a skip connection, which are used to generate the dynamic importance weights that decide which activation function is preferred. In addition, we introduce an extended Min-Max smoothing technique to further normalize financial time series that have non-stationary properties. In our experimental evaluation, the results showed that our proposed model not only outperforms deep neural network baselines but also other classic machine learning approaches. The extended Min-Max smoothing technique is step towards forecasting non-stationary financial time series with deep neural networks. INDEX TERMS Neural networks, activation function, value at risk, min-max normalization, forex market.

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Section: Extended Min-max Normalization a Value At Riskmentioning

confidence: 99%