Vertaile menetelmiä

	ARIMA (Autoregressive Integrated Moving Average) -malli	Ehdollinen arvo riskissä (Expected Shortfall)	Realisoitu volatiliteetti ja HAR-malli
Tieteenala	Ekonometria	Rahoitus	Rahoitus
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	2015	2000	2009
Kehittäjä	Box & Jenkins (Box-Jenkins methodology)	Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)
Tyyppi	Univariate time-series model	Coherent tail-risk measure	Time-series regression of realized variance
Alkuperäislähde	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	Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-41.	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.
Rinnakkaisnimet	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	CVaR, expected shortfall, average value-at-risk, tail VaR	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV
Liittyvät	5	5	5
Tiivistelmä	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).	Conditional Value-at-Risk (CVaR), also called Expected Shortfall, is a coherent tail-risk measure that quantifies the conditional expectation of losses beyond the Value-at-Risk threshold. It was introduced for optimization by Rockafellar and Uryasev (2000) and shown to be coherent by Acerbi and Tasche (2002), and it has replaced VaR as the regulatory standard under Basel III/IV.	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.