Publication: The relationship between the volatility of returns and the number of jumps in financial markets
Loading...
Identifiers
Publication date
2009-12
Defense date
Authors
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Impact
Export
Abstract
The 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.
Description
Keywords
Volatility forecasts, High-frequency data, Implied volatility, VIX, Jumps, Microstructure noise