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

Nguyen Vo,Robert Ślepaczuk

doi:10.3390/e24020158

Abstract

This research aims to compare the performance of ARIMA as a 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 used the data collected from Yahoo Finance with daily frequency for the period from 1 January 2000 to 31 December 2019. By using a rolling window approach, we compared 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 the simple ARIMA model. In order to assess the precision and quality of these models in forecasting, we compared their equity lines, their forecasting error metrics (MAE, MAPE, RMSE, MAPE), and their performance metrics (annualized return compounded, annualized standard deviation, maximum drawdown, information ratio, and adjusted information ratio). The main contribution of this research is to show that the hybrid models outperform ARIMA and the benchmark (Buy&Hold strategy on S&P500 index) over the long term. These results are not sensitive to varying window sizes, the type of distribution, and the type of the GARCH model.

Highlights

Over the past few decades, time series forecasting in finance has been an interesting and important research area
The hybrid model outperforms autoregressive integrated moving average (ARIMA) and benchmark strategy evaluated based on error metrics and performance statistics
These results are much better even if compared with the ensemble models built from ML techniques (LSTM model) for S&P500 index (Michanków et al.) [40] or rather complex approach using pair trading strategies for the constituents of the Nasdaq 100 index (Bui and Slepaczuk) [41]

Summary

Introduction

Over the past few decades, time series forecasting in finance has been an interesting and important research area. It has attracted the attention of the researcher community and investors, speculators, and governments who are interested in verification of various models and approaches in predicting of future prices of various types of assets (Sakowski and Turovtseva, 2020 [1], Torre-Torres et al (2021) [2]) with the use of more standard tools (Goubuzaite and Teresiene, 2021 [3], Ivanyuk, 2021 [4]) or the newest ML techniques (Chlebus et al, 2021) [5] The main aim of time series modeling is to carefully measure and analyze the historical observations of the time series in order to develop the most appropriate models. The analyses of the time series of an essential US stock index, such as the S&P500 have never failed to get attention and efforts from those interested in quantitative finance

Objectives

Methods

Results