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Abstract
This paper proposes a simple two-step nonparametric procedure to estimate the intraday jump tail and measure the jump tail risk in asset price with noisy high frequency data. We first propose the pre-averaging threshold approach to estimate the intraday jumps occurred, and then use the peaks-over-threshold (POT) method and generalized Pareto distribution (GPD) to model the intraday jump tail and further measure the jump tail risk. Finally, an empirical example further demonstrates the power of the proposed method to measure the jump tail risk under the effect of microstructure noise.
Highlights
It’s well recognized that the financial asset returns are not normally distributed, but instead exhibit more slowly decaying and asymmetric tails
This paper proposes a simple two-step nonparametric procedure to estimate the intraday jump tail and measure the jump tail risk in asset price with noisy high frequency data
With the availability of reliable financial high frequency data over the last two decades, many closer researches on the dynamics of financial asset prices have documented the presence of jumps; see Barndorff-Nielsen and Shephard
Summary
It’s well recognized that the financial asset returns are not normally distributed, but instead exhibit more slowly decaying and asymmetric tails. With the availability of reliable financial high frequency data over the last two decades, many closer researches on the dynamics of financial asset prices have documented the presence of jumps; see Barndorff-Nielsen and Shephard [3] [4], Huangand Tauchen [5], Aït-Sahalia and Jacod [6], Lee and Hannig [7], Lee and Mykland [8], and so on While both components can account for the extreme tail behavior, they have different mechanisms and further have very different implications on pricing and risk management, as recently explored by Bollerslev and Todorov [9].
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