Modelling and Forecasting the Realized Range Conditional Quantiles

TL;DR

This paper introduces a quantile regression model for directly modeling and forecasting the conditional quantiles of realized range volatility, incorporating macroeconomic and financial predictors without distributional assumptions.

Contribution

It is the first to model and forecast realized range volatility quantiles directly, capturing dynamic relationships across different market conditions without assuming a specific distribution.

Findings

Key macroeconomic and financial variables significantly impact volatility quantiles.

Relationships between variables vary across quantiles, especially during market stress.

Model demonstrates improved forecast accuracy over traditional methods.

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.