Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review
Bootstrapping High-Frequency Jump Tests. / Dovonon, Prosper; Gonçalves, Sílvia; Hounyo, Ulrich et al.
In: Journal of the American Statistical Association, Vol. 114, No. 526, 2019, p. 793-803.Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review
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TY - JOUR
T1 - Bootstrapping High-Frequency Jump Tests
AU - Dovonon, Prosper
AU - Gonçalves, Sílvia
AU - Hounyo, Ulrich
AU - Meddahi, Nour
PY - 2019
Y1 - 2019
N2 - The main contribution of this article is to propose a bootstrap test for jumps based on functions of realized volatility and bipower variation. Bootstrap intraday returns are randomly generated from a mean zero Gaussian distribution with a variance given by a local measure of integrated volatility (which we denote by {vˆni}). We first discuss a set of high-level conditions on {vˆni} such that any bootstrap test of this form has the correct asymptotic size and is alternative-consistent. We then provide a set of primitive conditions that justify the choice of a thresholding-based estimator for {vˆni}. Our cumulant expansions show that the bootstrap is unable to mimic the higher-order bias of the test statistic. We propose a modification of the original bootstrap test which contains an appropriate bias correction term and for which second-order asymptotic refinements are obtained.
AB - The main contribution of this article is to propose a bootstrap test for jumps based on functions of realized volatility and bipower variation. Bootstrap intraday returns are randomly generated from a mean zero Gaussian distribution with a variance given by a local measure of integrated volatility (which we denote by {vˆni}). We first discuss a set of high-level conditions on {vˆni} such that any bootstrap test of this form has the correct asymptotic size and is alternative-consistent. We then provide a set of primitive conditions that justify the choice of a thresholding-based estimator for {vˆni}. Our cumulant expansions show that the bootstrap is unable to mimic the higher-order bias of the test statistic. We propose a modification of the original bootstrap test which contains an appropriate bias correction term and for which second-order asymptotic refinements are obtained.
KW - Asymptotic refinements
KW - Bias correction
KW - Jump tests
KW - Thresholding volatility bootstrap
KW - PRICES
KW - MODELS
KW - STOCHASTIC VOLATILITY
UR - http://www.scopus.com/inward/record.url?scp=85048315829&partnerID=8YFLogxK
U2 - 10.1080/01621459.2018.1447485
DO - 10.1080/01621459.2018.1447485
M3 - Journal article
AN - SCOPUS:85048315829
VL - 114
SP - 793
EP - 803
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
SN - 0162-1459
IS - 526
ER -