Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index

Nguyen Vo and Robert Ślepaczuk
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Nguyen Vo: University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group

No 2021-25, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: This research aims to compare the performance of ARIMA as linear model with that of the combination of ARIMA and GARCH family models to forecast S&P500 log returns in order to construct algorithmic investment strategies on this index. We use the data collected from Yahoo Finance. The dataset has daily frequency and covers the period from 01/01/2000 to 31/12/2019. By using rolling window approach, we compare ARIMA with the hybrid models to examine whether hybrid ARIMA-SGARCH and ARIMA-EGARCH can really reflect the specific time-series characteristics and have better predictive power than simple ARIMA model. In order to assess the precision and quality of these models in forecasting, we decide to compare their equity lines, their forecasting error metrics (MAE, MAPE, RMSE, MAPE) as well as their performance metrics (annualized return compounded, annualized standard deviation, maximum drawdown, information ratio and adjusted information ratio). The results show that the hybrid models outperform ARIMA and the benchmark (Buy&Hold strategy on S&P500 index). These results are not sensitive to varying window sizes, the type of distribution and the type of the GARCH model.

Keywords: algorithmic investment strategies; ARIMA; ARIMA-SGARCH; ARIMA-EGARCH; hybrid model; forecast stock returns; model robustness (search for similar items in EconPapers)
JEL-codes: C14 C4 C45 C53 C58 G13 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2021-25

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