Forecasting salmon market volatility using long short-term memory (LSTM)

Abstract

Forecasting salmon market volatility is crucial for reducing future uncertainty for market participants. This study explores the efficacy of the Long Short-term Memory (LSTM) network, a deep learning technique, in forecasting multi-step ahead salmon market volatility. The performance of the LSTM is assessed against a constructed volatility proxy and the Autoregressive Moving Average (ARMA) model, a traditional benchmark in time-series analysis. Evaluation is performed across various forecasting horizons using different forecast error measures. Our findings indicate that the ARMA model outperforms the LSTM in predicting salmon market volatility, suggesting that any non-linear patterns in the salmon market volatility might be too insignificant for an LSTM model to exploit effectively. However, we observed a significant discrepancy between the actual volatility values and the forecasts obtained by both models, indicating the complexity of accurately predicting salmon market volatility.