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Abstract:
We analyze the factors driving the widely-noted persistence in asset return
volatility using a unique dataset on global euro-dollar exchange rate
trading. We propose a new simple empirical specification of volatility,
based on the Kyle-model, which links volatility to the information flow,
measured as the order flow in the market, and the price sensitivity to that
information. Through the use of high-frequency data, we are able to estimate
the time-varying market sensitivity to information, and movements in
volatility can therefore be directly related to movements in two observable
variables, the order flow and the market sensitivity. The empirical results
are very strong and show that the model is able to explain almost all of the
long-run variation in volatility. Our results also show that the variation
over time of the market's sensitivity to information plays at least as
important a role in explaining the persistence of volatility as does the
rate of information arrival itself. The econometric analysis is conducted
using novel estimation techniques which explicitly take into account the
persistent nature of the variables and allow us to properly test for
long-run relationships in the data.
Full paper (696 KB PDF)
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Keywords
Volatility persistence, news sensitivity, exchange rates, long memory,
fractional cointegration, narrow band spectral regression
PDF files: Adobe Acrobat Reader ZIP files: PKWARE
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