Nearest Neighbor Predictions of Realized Volatility for S&P 100 Options Trading

42 Pages Posted: 2 May 2013 Last revised: 11 Sep 2019

See all articles by Julian Andrada-Felix

Julian Andrada-Felix

University of Las Palmas de Gran Canaria

Fernando Fernandez-Rodriguez

University of Las Palmas de Gran Canaria

Ana-Maria Fuertes

Cass Business School, City University of London

Date Written: July 23, 2013

Abstract

The increasing availability of intraday financial data has led to improvements in daily volatility forecasting through long-memory models of realized volatility. This paper demonstrates the merit of the non-parametric Nearest Neighbor (NN) approach for S&P 100 realized variance forecasting. A priori the NN approach is appealing because it can reproduce complex dynamic dependencies while largely avoiding misspecification and parameter estimation uncertainty, unlike model-based methods. We evaluate the forecasts through straddle trading profitability metrics and using conventional statistical accuracy criteria. The ranking of individual forecasts confirms that statistical accuracy does not have a one-to-one mapping into profitability. In turbulent markets, the NN forecasts lead to higher risk-adjusted profitability even though the model-based forecasts are statistically superior. In both calm and turbulent market conditions, the directional combination of NN and model-based forecasts is more profitable than any of the individual forecasts.

Keywords: Realized variance, Volatility forecasting, Options trading, Long memory, Nonlinear dependence, Nearest neighbour, Non-parametric

JEL Classification: C22, C53, G15

Suggested Citation

Andrada-Felix, Julian and Fernandez-Rodriguez, Fernando and Fuertes, Ana-Maria, Nearest Neighbor Predictions of Realized Volatility for S&P 100 Options Trading (July 23, 2013). International Journal of Forecasting, Forthcoming, Available at SSRN: https://ssrn.com/abstract=2258457 or http://dx.doi.org/10.2139/ssrn.2258457

Julian Andrada-Felix

University of Las Palmas de Gran Canaria ( email )

Campus de Tafira
35017 Las Palmas
Spain

Fernando Fernandez-Rodriguez

University of Las Palmas de Gran Canaria ( email )

Campus de Tafira
35017 Las Palmas
Spain

Ana-Maria Fuertes (Contact Author)

Cass Business School, City University of London ( email )

Faculty of Finance
106 Bunhill Row
London, EC1Y 8TZ
United Kingdom
+44 207 477 0186 (Phone)
+44 207 477 8881 (Fax)

HOME PAGE: http://www.city.ac.uk/people/academics/ana-maria-fuertes

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