Cartea, ÁlvaroKaryampas, DimitriosUniversidad Carlos III de Madrid. Departamento de Economía de la Empresa2009-12-022009-12-022009-12https://hdl.handle.net/10016/5903The contribution of this paper is two-fold. First we show how to estimate the volatility of high frequency log-returns where the estimates are not a affected by microstructure noise and the presence of Lévy-type jumps in prices. The second contribution focuses on the relationship between the number of jumps and the volatility of log-returns of the SPY, which is the fund that tracks the S&P 500. We employ SPY high frequency data (minute-by-minute) to obtain estimates of the volatility of the SPY log-returns to show that: (i) The number of jumps in the SPY is an important variable in explaining the daily volatility of the SPY log-returns; (ii) The number of jumps in the SPY prices has more explanatory power with respect to daily volatility than other variables based on: volume, number of trades, open and close, and other jump activity measures based on Bipower Variation; (iii) The number of jumps in the SPY prices has a similar explanatory power to that of the VIX, and slightly less explanatory power than measures based on high and low prices, when it comes to explaining volatility; (iv) Forecasts of the average number of jumps are important variables when producing monthly volatility forecasts and, furthermore, they contain information that is not impounded in the VIX.application/pdfengAtribución-NoComercial-SinDerivadas 3.0 EspañaVolatility forecastsHigh-frequency dataImplied volatilityVIXJumpsMicrostructure noiseworking paperC53G12G14C22Empresaopen accesswb097508