Newest papers

Structured SDEs for Realized GARCH: Continuous-Time Volatility Modelling and Component-Level Analysis

Hongyu Yao, Zhengyang Chi, Junbin Gao · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.17732 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

我们提出了一个结构化连续时间的斯托加斯特波动性模型,用于一天前的集成波动预测。该模型引入了一种共同演变的实现测量状态,该模型直接输入隐形波动性动态,而相关的布朗尼创新捕捉了回报相关的依赖性。参数通过模拟的基于预测的学习进行估计,而蒙特卡洛模拟预测了集成波动性。在17个股票指数中,拟议的模型显示了与GARCH、GARCH-Itô、Realized GARCH、HAR和HEAVY基准的较强的样本性能。在QLIKE、MSE和MAE下,基准参数被估计。

Machine translation (Meta M2M100); the English original follows.

Abstract

We propose a structured continuous-time stochastic volatility model for one-day-ahead integrated-variance forecasting. The model introduces a jointly evolving realized-measure state that feeds directly into latent variance dynamics, while correlated Brownian innovations capture return-linked dependence. Parameters are estimated through simulation-based predictive learning, and Monte Carlo simulation forecasts integrated variance. Across 17 equity indices, the proposed model shows strong out-of-sample performance relative to GARCH, GARCH-Itô, Realized GARCH, HAR, and HEAVY benchmarks under QLIKE, MSE, and MAE. Ablation results show that realized-measure feedback provides substantial incremental forecasting value within the proposed framework, while return-linked Brownian dependence contributes more modest complementary gains. The results support incorporating measurement-driven information directly into continuous-time latent volatility dynamics to improve integrated-variance forecasting.

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Topics: Stochastic volatility, Volatility (finance), Econometrics, Monte Carlo method, Realized variance, Computer science

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