Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Модел GARCH (Прогнозиране на волатилността)× | Модел ARIMA (Autoregressive Integrated Moving Average)× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|---|
| Област | Иконометрия | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 1986 | 2015 | 2019 |
| Създател≠ | Tim Bollerslev | Box & Jenkins (Box-Jenkins methodology) | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Conditional volatility model | Univariate time-series model | Linear regression |
| Основополагащ източник≠ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Други названия≠ | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани | 5 | 5 | 5 |
| Резюме≠ | The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series. | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateНабор от данни ↗ |
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