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Title: Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall Authors:  Vincenzo Candila - University of Salerno (Italy) [presenting]
Lea Petrella - Sapienza University of Rome (Italy)
Giampiero Gallo - NYU in Florence (Italy)
Abstract: The use of quantile regression to calculate risk measures has been widely recognized in the financial econometrics literature. When data are observed at mixed-frequency, the standard quantile regression models are no longer adequate. We develop a model built on a mixed-frequency quantile regression to directly estimate the Value-at-Risk (VaR) and the Expected Shortfall (ES) measures. In particular, the low-frequency component incorporates information coming from variables observed at, typically, monthly or lower frequencies, while the high-frequency component can include a variety of daily variables, like market indices or realized volatility measures. The validity of the proposed model is then explored through a real data application using two energy commodities, that is, Crude Oil and Gasoline futures. We show that our model outperforms other competing specifications, using the most popular VaR and ES backtesting procedures.