发表机构
Graduate School of Economics, The University of Tokyo; Mizuho-DL Financial Technology Co., Ltd.; Graduate School of Business Administration, Hosei University（东京大学经济学研究科; 瑞穗DL金融科技有限公司; 法政大学经营学研究科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究从标准普尔500指数期权价格和市场数据估计随机贴现因子，提出由时变波动率缩放的SDF框架，恢复稳定非单调SDF，减轻观测噪声，其股权溢价预测性能优于现有基准，揭示到期对SDF形状的影响及理论依据。
AI 中文摘要
本文提出了一个由时变波动率缩放的随机贴现因子（SDF）。通过利用仅从标准普尔500指数期权隐含的价格和市场数据，该框架恢复了一个稳定、非单调的SDF，它捕捉了市场参与者纯粹的前瞻性预期，同时减轻了观测噪声。实证分析表明，SDF在浅看跌期权一侧呈现出独特的驼峰，随着到期时间增加，转变为更清晰的W形，表明到期是影响中心驼峰强度的关键因素。结果表明，这种结构特征在恒定风险市场价格下可通过随机波动率动态从理论上合理化。与时变波动率缩放SDF得出的股权溢价相对于现有基准（如马丁边界）表现出卓越的样本外预测性能。
英文摘要
This paper proposes a stochastic discount factor (SDF) scaled by time-varying volatility. By utilizing prices and market data implied solely from S\&P 500 options, the proposed framework recovers a stable, non-monotonic SDF that captures the pure forward-looking expectations of market participants while mitigating observation noise. Our empirical analysis reveals that the SDF exhibits a distinctive hump on the shallow put side, which transitions into a more clearly defined W-shape as the time to maturity increases, identifying maturity as a key factor influencing the intensity of the central hump. We show that this structural feature can be theoretically rationalized by stochastic volatility dynamics under a constant market price of risk. The equity premium derived from the time-varying volatility scaled SDF demonstrates superior out-of-sample predictive performance relative to existing benchmarks, such as the Martin bounds.
Comments34 page, 5 figures
