Hybrid forecasting models for S&P 500 index returns

Akihiro Fukushima

Journal of Risk Finance, 2011, vol. 12, issue 4, 315-328

Abstract: Purpose - The purpose of this paper is to propose two hybrid forecasting models which integrate available ones. A hybrid contaminated normal distribution (CND) model accurately reflects the non‐normal features of monthly S&P 500 index returns, and a hybrid GARCH model captures a serial correlation with respect to volatility. The hybrid GARCH model potentially enables financial institutions to evaluate long‐term investment risks in the S&P 500 index more accurately than current models. Design/methodology/approach - The probability distribution of an expected investment outcome is generated with a Monte Carlo simulation. A taller peak and fatter tails (kurtosis), which the probability distribution of monthly S&P 500 index returns contains, is produced by integrating a CND model and a bootstrapping model. The serial correlation of volatilities is simulated by applying a GARCH model. Findings - The hybrid CND model can simulate the non‐normality of monthly S&P 500 index returns, while avoiding the influence of discrete observations. The hybrid GARCH model, by contrast, can simulate the serial correlation of S&P 500 index volatilities, while generating fatter tails. Long‐term investment risks in the S&P 500 index are affected by the serial correlation of volatilities, not the non‐normality of returns. Research limitations/implications - The hybrid models are applied only to the S&P 500 index. Cross‐sectional correlations among different asset groups are not examined. Originality/value - The proposed hybrid models are unique because they combine available ones with a decision tree algorithm. In addition, the paper clearly explains the strengths and weaknesses of existing forecasting models.

Keywords: S&P 500 index; Kurtosis; Serial correlation of volatilities; Long‐term investment; Financial forecasting (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:eme:jrfpps:15265941111158497

DOI: 10.1108/15265941111158497

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