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Hybrid forecasting models for S&P 500 index returns

Hybrid forecasting models for S&P 500 index returns 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Risk Finance Emerald Publishing

Hybrid forecasting models for S&P 500 index returns

The Journal of Risk Finance , Volume 12 (4): 14 – Aug 16, 2011

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Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
1526-5943
DOI
10.1108/15265941111158497
Publisher site
See Article on Publisher Site

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.

Journal

The Journal of Risk FinanceEmerald Publishing

Published: Aug 16, 2011

Keywords: S&P 500 index; Kurtosis; Serial correlation of volatilities; Long‐term investment; Financial forecasting

References