Time Series: Analysis, Model, and Forecasting

Cheng-Few Lee (), Hong-Yi Chen () and John Lee ()
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Cheng-Few Lee: Rutgers University, Department of Finance and Economics, Rutgers Business School
Hong-Yi Chen: National Chengchi University, Department of Finance
John Lee: Center for PBBEF Research

Chapter Chapter 10 in Financial Econometrics, Mathematics and Statistics, 2019, pp 279-316 from Springer

Abstract: Abstract In this chapter, we discuss traditional time-series analysis method, which includes moving-average method, autoregressive forecasting modelAutoregressive forecasting model, and ARIMA model. In addition, autoregressive conditional heteroscedasticityHeteroscedasticity process is introduced. ARCHAutoregressive Conditional Heteroscedasticity (ARCH) and GARCHGeneralized Autoregressive Conditional Heteroscedasticity (GARCH) models and models related to ARCHAutoregressive Conditional Heteroscedasticity (ARCH) family are discussed in detail.

Keywords: Autoregressive forecasting model; Coincident indicators; Cross-sectional data; Cyclical component; Exponential smoothing; Exponential smoothing constant; Holt–Winters forecasting model; Irregular component; Lagging indicators; Leading indicators; Mean squared error; Percentage of moving average; Seasonal component; Seasonal index; Seasonal index method; Time-series data; Trend component; Trend–cycle component; Trading-day component; X-11 model (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4939-9429-8_10

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DOI: 10.1007/978-1-4939-9429-8_10

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