পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| Realized Volatility এবং HAR মডেল× | দীর্ঘ-স্মৃতি মডেল (ARFIMA, FIGARCH)× | |
|---|---|---|
| ক্ষেত্র | অর্থায়ন | অর্থায়ন |
| পরিবার | Regression model | Regression model |
| উদ্ভবের বছর≠ | 2009 | 1980 |
| প্রবর্তক≠ | Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility) | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) |
| ধরন≠ | Time-series regression of realized variance | Fractionally integrated time series model |
| মৌলিক উৎস≠ | Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196. DOI ↗ | Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29. DOI ↗ |
| অপর নাম | realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV | ARFIMA, FIGARCH, fractionally integrated models, fractional integration |
| সম্পর্কিত≠ | 5 | 4 |
| সারসংক্ষেপ≠ | Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility framework of Andersen, Bollerslev, Diebold and Labys (2003), forecasts this measure by combining daily, weekly, and monthly volatility components, and is a strong alternative to GARCH for volatility prediction. | Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration. |
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