The Identification of Price Jumps

Abstract

We performed an extensive simulation study to compare the relative performance of many price-jump indicators with respect to false positive and false negative probabilities. We simulated twenty different time series specifications with different intraday noise volatility patterns and price-jump specifications. The double McNemar (1947) non-parametric test has been applied on constructed artificial time series to compare fourteen different price-jump indicators that are widely used in the literature. The results suggest large differences in terms of performance among the indicators, but we were able to identify the best-performing indicators. In the case of false positive probability, the best-performing price-jump indicator is based on thresholding with respect to centiles. In the case of false negative probability, the best indicator is based on bipower variation. Abstrakt Provedli jsme extenzivní simulační studii a srovnali výkonnost mnoha různých indikátorů cenových skoků na základě kritéria rozlišné pravděpodobnosti falešné pozitivní a falešné negativní identifikace. Simulovali jsme dvacet různých typů časových řad s různě specifikovaným chováním vnitrodenní volatility tvořené bílým šumem a ne-normálními cenovými skoky. Na simulovaných časových řadách jsme aplikovali dvojitý McNemarův (1947) neparametrický test a porovnali14 různých indikátorů cenových skoků, které se nejvíce používají ve finanční ekonometrii. Výsledky ukazují velmi odlišné vlastnosti indikátorů, přičemž jsme byli schopni identifikovat ty nejpřesnější z nich. V případě kritéria porovnávajícího pravděpodobnosti falešné pozitivní indikace cenových skoků se jako nejlepší nástroj ukázal indikátor využívající filtrování pomocí centilů. V případě pravděpodobností falešné negativní indikace nejlépe fungoval indikátor založený na dvojčlenném rozptylu (bipower variance).

Something in the air: Information density, news surprises, and price jumps

“…With the approach that was suggested byLee and Mykland (2008), the number of misspecified jumps is minimized. This confirms the results ofHanousek, Kocenda, and Novotny (2012).13 We further applied alternative window sizes ranging from three to 20 days. At a window size of five trading days, the amount of spuriously identified jumps is minimized.…”

Price Jump Indicators: Stock Market Empirics During the Crisis

“…We proceed in the following manner to achieve our goals. First, we introduce a set of common price jump indicators previously developed in the literature and discuss their known relative performance, which is provided in Hanousek et al (2012). Second, we formally introduce a cluster methodology to study the relative mutual performance of price jump indicators using real data.…”

“…This issue was brought up by several recent studies that evaluate jump indicators via the Monte Carlo approach. Hanousek et al (2012) studied 14 different specifications (which are also used in this study) and they classified the indicators by their ability to predict price jumps (with no penalty for false identification) and by their ability not to incorrectly identify jumps (with no attention paid to missed identification). Basically, their approach compares border cases for the optimality of price jump indicators with respect to Type I and Type II errors.…”

“…The contribution of our paper is threefold. First, we apply a large set of price jump indicators, whose prediction accuracy and performance was compared in the extensive simulation study by Hanousek et al (2012), to nine stock market indices from both mature and emerging markets.…”

Profitability of Trading in the Direction of Asset Price Jumps - Analysis of Multiple Assets and Frequencies

“…The most commonly used are the Z-estimator (Barndorff-Nielsen and Shephard, 2004), L-estimator (Lee and Mykland, 2008) and the JO-estimator (Jiang and Oomen, 2008). Simulation studies comparing the performance of different non-parametric jump estimators can be found in Hanousek et al (2011) or Dumitru and Urga (2012).…”