损失选择还是模型选择？预测水平在加密货币波动率预测中的作用
Loss Choice or Model Choice? The Role of Forecast Level in Cryptocurrency Volatility Forecasting
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中文总结 AI 辅助
本研究比较七种损失函数和五种模型预测加密货币波动率，发现水平对齐后模型选择比损失选择影响更大，且VaR违规率差异缩小。
中文摘要 AI 辅助
波动率预测在金融风险管理中扮演核心角色，因为其整体水平及日常波动会影响下游决策。多数研究在固定训练损失的情况下比较预测模型。然而，损失函数强调不同的误差，并可能针对未来波动率的不同属性，因此原始比较可能将持续的预测水平差异与日常预测变动差异混在一起。这导致损失选择的重要性究竟主要来自其针对的预测水平，还是来自水平调整后仍存在的差异，这一问题悬而未决。我们通过比较七种损失函数和五种模型在主要加密货币上的表现来解决这一空白。基于验证的对齐在原始和对齐预测使用统计评分及一日风险价值评估之前调整预测水平。在对齐之前，边际评分变异在损失函数间更大。对齐之后，在完整的五种模型比较中，模型选择成为更大的变异来源，而跨损失的VaR违规率差异大幅缩小。我们的贡献是对损失和模型选择的全面评估，展示了为何损失在原始比较中显得如此有影响力，以及当明确考虑预测水平和下游风险时，这种解释如何变化。
英文摘要
Volatility forecasts play a central role in financial risk management because their overall level and day-to-day movements affect downstream decisions. Most studies compare forecasting models while keeping the training loss fixed. Yet losses emphasise different errors and can target different properties of future volatility, so raw comparisons may combine persistent forecast-level differences with differences in daily forecast movements. This leaves unresolved whether the importance of loss choice comes mainly from the forecast level it targets or from differences that remain after level adjustment. We address this gap through a comparison of seven losses and five models across major cryptocurrencies. Validation-based alignment adjusts the forecast level before the raw and aligned forecasts are evaluated using statistical scores and one-day Value-at-Risk. Before alignment, marginal score variation is greater across losses. After alignment, model choice becomes the larger source of variation in the full five-model comparison, while cross-loss differences in VaR breach rates narrow substantially. Our contribution is a comprehensive evaluation of loss and model choice that shows why losses can appear so influential in raw comparisons and how this interpretation changes when forecast level and downstream risk are considered explicitly.
发表机构
- Warsaw University of Technology（华沙理工大学）
机构由 AI 辅助整理，请以论文原文为准。
