How to Forecast Long-Run Volatility: Regime Switching and the Estimation of Multifractal Processes

We propose a discrete-time stochastic volatility model in whichregime switching serves three purposes. First, changes in regimescapture low-frequency variations. Second, they specify intermediate-frequencydynamics usually assigned to smooth autoregressive transitions.Finally, high-frequency switches generate substantial outliers.Thus a single mechanism captures three features that are typicallyviewed as distinct in the literature. Maximum-likelihood estimationis developed and performs well in finite samples. Using exchangerates, we estimate a version of the process with four parametersand more than a thousand states. The multifractal outperformsGARCH, MS-GARCH, and FIGARCH in- and out-of-sample. Considerablegains in forecasting accuracy are obtained at horizons of 10to 50 days.     

遵从科创规律：再谈科创成果与年龄的关系

本文回顾了“成果-年龄”研究的历史和逻辑，系统梳理了相关研究，讨论了该理论模型存在的问题和下一步发展方向。认为“成果-年龄”理论模型在宏观和中观层次上具有一定同构性，可以借鉴“介尺度”思想方法，将威布尔分布统计规律在微观层次尝试。建议要遵从科创规律，保障科创群体工作时间和专注力，在最适合出成果的年龄专心搞研究。