Basics for Forecasting a Stationary Time Series Using Information from Its Past
Since market behavior is volatile, this research intends to help investors and business organizations make forecasts with certainty and, as a consequence, with the least possible error in order to succeed in the management of their projects and operations. Elements such as inflation rate, exchange r...
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2020 |
| Country: | Perú |
| Institution: | Universidad Nacional Mayor de San Marcos |
| Repository: | Revistas - Universidad Nacional Mayor de San Marcos |
| Language: | Spanish English |
| OAI Identifier: | oai:revistasinvestigacion.unmsm.edu.pe:article/16504 |
| Online Access: | https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/16504 |
| Access Level: | Open access |
| Keyword: | time series stationarity unit root white noise variance series de tiempo estacionariedad raíz unitaria ruido blanco varianza |
| Summary: | Since market behavior is volatile, this research intends to help investors and business organizations make forecasts with certainty and, as a consequence, with the least possible error in order to succeed in the management of their projects and operations. Elements such as inflation rate, exchange rate, stock prices, economic and financial results, sales, among other variables, are causes of concern for investors. Due to their data structure, these financial instruments correspond to time series, which take values or realizations along time and are spaced over time. The previous behavior of the series is used to forecast its value, return and volatility. It must be taken into consideration that forecasting using traditional techniques might result in imprecisions, so it is necessary to forecast using econometric models because of their robustness and precision. These are also known as univariate time series models. |
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