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已实现波动率的建模与预测AI 解读

This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of…

方差比统计量与高频数据：日内波动模式变化检验AI 解读

ABSTRACT Variance‐ratio tests are routinely employed to assess the variation in return volatility over time and across markets. However, such tests are not statistically robust and can be seriously misleading within a high‐frequency context. We develop improved inference procedures using a Fourier Flexible Form regression framework. The practical significance is illustrated through tests for changes in the FX intraday volatility pattern following the removal of trading…

高频数据分析的若干思考AI 解读

Finance is arguably the most empirically oriented of all the social sciences. This is in part due to the deliberate practical orientation and the ready availability of high-quality financial market data. In recent years, the ever lower costs of data recording and storage have driven the phenomenon to the ultimate limit for some markets: We may have access to time-stamped observations on all quotes and transactions, denoted ultra-high-frequency data by Engle (in press). These…

《基于模拟的计量经济学方法》书评AI 解读

The accessibility of high-performance computing power has always influenced theoretical and applied econometrics. Gouriéroux and Monfort begin their recent offering, Simulation-Based Econometric Methods , with a stylized three-stage classification of the history of statistical econometrics. In the first stage, lasting through the 1960's, models and estimation methods were designed to produce closed-form expressions for the estimators. This spurred thorough investigation of…

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金融市场计量经济学AI 解读

The abundance of high-frequency financial data and the rapid development of computer hardware have combined to transform financial economics into, arguably, the most empirically oriented field within the social sciences. At the same time, as a result of the difficulty of conducting genuine market experiments, empirical finance remains firmly grounded in the tradition of model-driven statistical inference that is characteristic of economics. Even so, the richness of data has…

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随机波动率模型的 GMM 估计：蒙特卡洛研究AI 解读

We examine alternative generalized method of moments procedures for estimation of a stochastic autoregressive volatility model by Monte Carlo methods. We document the existence of a tradeoff between the number of moments, or information, included in estimation and the quality, or precision, of the objective function used for estimation. Furthermore, an approximation to the optimal weighting matrix is used to explore the impact of the weighting matrix for estimation,…

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