We present a new class of Bayesian dynamic models for bivariate price-realized volatility time series in financial forecasting. A novel dynamic gamma process model adopted for realized volatility is integrated with traditional Bayesian dynamic linear models (DLMs) for asset price series. This represents reduced-form volatility leverage and feedback effects through use of realized volatility proxies in conditional DLMs for prices or returns, coupled with the synthesis of higher frequency data to track and anticipate volatility fluctuations. Analysis is computationally straightforward, extending conjugate-form Bayesian analyses for sequential filtering and model monitoring with simple and direct simulation for forecasting. A main applied setting is equity return forecasting with daily prices and realized volatility from high-frequency, intraday data. Detailed empirical studies of multiple S&P sector ETFs highlight the improvements achievable in asset price forecasting relative to standard models and deliver contextual insights on the nature and practical relevance of volatility leverage and feedback effects. The analytic structure and negligible extra computational cost will enable scaling to higher dimensions for multivariate price series forecasting for decouple/recouple portfolio construction and risk management applications.

翻译：我们提出了一类新的贝叶斯动态模型，用于金融预测中的二元价格-已实现波动时间序列分析。该模型将一种针对已实现波动的新型动态伽马过程与传统的资产价格序列贝叶斯动态线性模型（DLMs）相结合。通过将已实现波动代理变量引入价格或收益的条件DLM，并结合高频数据合成来追踪和预判波动起伏，这种模型简化了波动率杠杆效应与反馈效应的表征。分析过程在计算上简洁高效，延续了共轭形式的贝叶斯分析方法以实现序贯滤波与模型监控，并借助简单直接的仿真方法进行预测。该模型的主要应用场景是基于日度价格与高频日内数据生成的已实现波动进行权益收益预测。对多个标普行业ETF的详细实证研究揭示了相较于标准模型在资产价格预测中可实现的改进，并提供了关于波动率杠杆效应与反馈效应的本质及实际相关性的情境化洞察。解析结构与可忽略的额外计算成本将使其能够扩展至多元价格序列预测的更高维度，用于解耦/重耦合投资组合构建与风险管理应用。