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ARIMA (Autoregressive Integrated Moving Average) 모형×GARCH 모형 (변동성 예측)×고빈도 데이터와 시장 미시구조 분석×
분야계량경제학계량경제학재무학
계열Regression modelRegression modelRegression model
기원 연도201519862007
창시자Box & Jenkins (Box-Jenkins methodology)Tim BollerslevHasbrouck (2007); Aït-Sahalia & Jacod (2014)
유형Univariate time-series modelConditional volatility modelMarket microstructure / high-frequency econometrics
원전Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press. ISBN: 978-0195301649
별칭Box-Jenkins model, ARIMA(p,d,q), ARIMA ModeliGARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)market microstructure, high-frequency financial econometrics, tick data analysis, Yüksek Frekanslı Veri ve Piyasa Mikro Yapısı
관련555
요약ARIMA 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).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.Market microstructure analysis studies how prices form from tick-level trade and quote data, examining order-book dynamics, the bid-ask spread, and price discovery. The modern econometric framework was set out by Hasbrouck (2007) and extended for high-frequency data by Aït-Sahalia and Jacod (2014).
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