在資產定價領域中，理解資產預期報酬與波動性關係是重要議題。在本文中，我們擴展基於 affine GARCH 框架非對稱雙指數跳躍-擴散模型，在 affine GARCH 設定下提出創新模型，該模型使用兩個指數分佈來描述好與壞的跳躍。此外，我們為此模型配置推導出選擇權定價之封閉形式解。我們研究發現，將跳躍成分納入變異數過程可以提高模型估計性能，其中壞跳躍成分貢獻遠大於其好的對應部分。在我們實證分析中，通過模型估計，我們推斷出由這些好與壞跳躍產生之變異數風險溢價。通過橫斷面迴歸，我們確定了這兩種變異數風險溢價都作為已定價風險因子。時間序列分析進一步確認，壞跳躍方差風險溢價在預測報酬方面佔主導地位。

The understanding of the relationship between an asset’s expected return and its volatility is pivotal in asset pricing. In this paper, we extend the asymmetric double exponential jump-diffusion model grounded in the affine generalized autoregressive conditional heteroskedastic (GARCH) framework. We propose a model within the affine GARCH setting that uses two exponential distributions to separately model good and bad jumps. Furthermore, we deduce a closed-form solution for option pricing within this model structure. Our results suggest that the integration of jump components into the variance process significantly bolsters model estimation performance—the bad jump component markedly outstrips its good counterpart in contribution. In our empirical evaluation, we discern the variance risk premiums attributable to these good and bad jumps through model estimation. A cross-sectional regression reveals that both variance risk premiums serve as priced risk factors. Moreover, a time-series examination underscores the prevailing role of the bad jump variance risk premium in forecasting returns.

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