流動性の予測にエントロピーの損失を組み込むことで予測を向上させる
Renaldas Urniezius1, Rytis Petrauskas1, Vygandas Vaitkus1
1Department of Automation, Kaunas University of Technology, Studentu St. 48, 51367 Kaunas, Lithuania.
Entropy (Basel, Switzerland)
|August 28, 2025
まとめ
この研究では,予測精度に関する異質自回帰モデル (HAR) を評価しています. エントロピー損失関数と堅固な線形モデルは,特にRealized QuarticityとVIXインデックスデータを追加することで,さまざまな地平線で優れたパフォーマンスを示しています.
科学分野:
- 定量金融
- 経済学
- 金融モデリング
背景:
- 正確な金融市場の予測は,リスク管理と投資戦略に不可欠です.
- ヘテロゲノス・オートレグレッシブ (HAR) モデルは,ボラティリティの予測に広く使用されています.
- HARモデルのパフォーマンスを最適化するために,異なる推定技術と視野を評価することが不可欠です.
研究 の 目的:
- 様々な見積もり技術と時間軸を用いた異質自回帰モデル (HAR) の予測精度を比較する.
- HARモデルの最適な推定方法と予測の地平線を特定する.
- 外因的な変数とモデル仕様の改善が予測の精度に与える影響を評価する.
主な方法:
- 5つの見積もり技術と4つの予測の地平線を持つ3つのHAR型モデルを検証した.
- 標準・プアーズ500 (SPX) インデックスとVIXインデックスの5分間のイントラデイデータを外部変数として利用した.
- 業績評価の準確率 (QLIKE),平均絶対誤差 (MAE),平均二乗誤差 (MSE) を採用した.
主要な成果:
- エントロピー損失関数は,すべての時間帯,特に週間の時間帯で一貫して最高の準確率 (QLIKE) の結果を出しました.
- 頑丈な線形モデルは,平均絶対誤差 (MAE) と平均二乗誤差 (MSE) で強いパフォーマンスを示す競争力のある代替案であることが判明しました.
- REALIZED QUARTICITY (HARQモデル) とVIXインデックスを導入することで,モデル全体の予測精度が大幅に改善されました.
結論:
- エントロピー損失関数と堅固な線形モデルは,HARモデルの予測の正確性を示しています.
- 推定技術と予測の地平線の選択は,HARモデルの性能に重大な影響を及ぼします.
- VIXのような情報的なレイグと外因的な変数で HARモデルを強化すると,予測力が向上します.
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