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.

摘要

预测鲑鱼市场波动对于减少市场参与者未来的不确定性至关重要。本研究探讨了长短期记忆 (LSTM) 网络（一种深度学习技术）在预测鲑鱼市场多步波动方面的功效。LSTM 的性能根据构建的波动率代理和自回归移动平均 (ARMA) 模型（时间序列分析的传统基准）进行评估。使用不同的预测误差度量在不同的预测范围内进行评估。我们的研究结果表明，ARMA 模型在预测鲑鱼市场波动性方面优于 LSTM，这表明鲑鱼市场波动中的任何非线性模式对于 LSTM 模型来说可能都太微不足道，无法有效利用。然而，