Modelling and Forecasting the Realized Range Conditional Quantiles

42 Pages Posted: 12 Oct 2014

See all articles by Giovanni Bonaccolto

Giovanni Bonaccolto

University "Kore" of Enna

Massimiliano Caporin

University of Padua - Department of Statistical Sciences

Date Written: October 11, 2014

Abstract

Several studies have focused on the Realized Range Volatility, an estimator of the quadratic variation of financial prices, taking into account the impact of microstructure noise and jumps. However, none has considered direct modeling and forecasting of the Realized Range conditional quantiles. This study carries out a quantile regression analysis to fill this gap. The proposed model takes into account as quantile predictors both the lagged values of the estimated volatility and some key macroeconomic and financial variables, which provide important information about the overall market trend and risk. In this way, and without distributional assumptions on the realized range innovations, it is possible to assess the entire conditional distribution of the estimated volatility. This issue is a critical one for financial decision-makers in terms of pricing, asset allocation, and risk management. The quantile regression approach allows how the links among the involved variables change across the quantiles levels to be analyzed. In addition, a rolling analysis is performed in order to determine how the relationships that characterize the proposed model evolve over time. The analysis is applied to sixteen stocks issued by companies that operate in differing economic sectors of the U.S. market, and the forecast accuracy is validated by means of suitable tests. The results show evidence of the selected variables relevant impacts and, particularly during periods of market stress, highlights heterogeneous effects across quantiles.

Keywords: Realized Range Volatility, Quantile regression, Volatility quantiles forecasting

JEL Classification: C58, C52, C22, G14

Suggested Citation

Bonaccolto, Giovanni and Caporin, Massimiliano, Modelling and Forecasting the Realized Range Conditional Quantiles (October 11, 2014). Available at SSRN: https://ssrn.com/abstract=2508627 or http://dx.doi.org/10.2139/ssrn.2508627

Giovanni Bonaccolto

University "Kore" of Enna ( email )

Viale delle Olimpiadi
Enna, 94100
Italy

Massimiliano Caporin (Contact Author)

University of Padua - Department of Statistical Sciences ( email )

Via Battisti, 241
Padova, 35121
Italy

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