1,720,971 research outputs found
Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective
Get PDFThis article applies realized volatility forecasting to Extreme Value Theory (EVT). We propose a two-step approach where returns are first pre-whitened with a high-frequency based volatility model, and then an EVT based model is fitted to the tails of the standardized residuals. This realized EVT approach is compared to the conditional EVT of McNeil & Frey (2000). We assess both approaches' ability to filter the dependence in the extremes and to produce stable out-of-sample VaR and ES estimates for one-day and ten-day time horizons. The main finding is that GARCH-type models perform well in filtering the dependence, while the realized EVT approach seems preferable in forecasting, especially at longer time horizons
US stock returns: are there seasons of excesses?
No full textThis article explores the existence of seasonality in the tails of stock returns. We use a parametric model to describe the returns, and obtain a proxy of the innovation distribution via a pre-processing model. Then, we develop a change-point algorithm capturing changes in the tails of the innovations. We confirm the good performance of the procedure through extensive Monte Carlo experiments. An empirical investigation using US stocks data shows that while the lower tail of the innovations is approximately constant over the year, the upper tail is larger in Winter than in Summer, in 9 out of 12 industries
Structural change to the persistence of the urban heat island
Get PDFThe term urban heat island (UHI) is used to describe the effect of urban temperatures rising several
degrees above concurrent temperatures in surrounding suburban or rural areas. This is typically
assessed through records of daily extreme temperatures. However, on a hot day the temperature
can exceed an extreme threshold for several consecutive hours, forming a cluster of extremes. We
use the statistical theory of extreme values combined with a model that allows structural breaks to
show that there has been a significant upward shift in the length of clusters in New York City. No
such shift is found at a Connecticut location where the usual UHI assessment indicates that the two
sites are comparable. Our study is the first to highlight this danger of the UHI. Prolonged exposure
to extreme temperatures has deleterious effects on both health and the environment
Modeling panels of extremes
Get PDFExtreme value applications commonly employ regression techniques to capture
cross-sectional heterogeneity or time-variation in the data. Estimation of the
parameters of an extreme value regression model is notoriously challenging due
to the small number of observations that are usually available in applications.
When repeated extreme measurements are collected on the same individuals, i.e.,
a panel of extremes is available, pooling the observations in groups can
improve the statistical inference. We study three data sets related to risk
assessment in finance, climate science, and hydrology. In all three cases, the
problem can be formulated as an extreme value panel regression model with a
latent group structure and group-specific parameters. We propose a new
algorithm that jointly assigns the individuals to the latent groups and
estimates the parameters of the regression model inside each group. Our method
efficiently recovers the underlying group structure without prior information,
and for the three data sets it provides improved return level estimates and
helps answer important domain-specific questions
Mixed-frequency extreme value regression: Estimating the effect of mesoscale convective systems on extreme rainfall intensity
Get PDFUnderstanding and modeling the determinants of extreme hourly rainfall intensity is of utmost importance for the management of flash-flood risk. Increasing evidence shows that mesoscale convective systems (MCS) are the principal driver of extreme rainfall intensity in the United States. We use extreme value statistics to investigate the relationship between MCS activity and extreme hourly rainfall intensity in Greater St. Louis, an area particularly vulnerable to flash floods. Using a block maxima approach with monthly blocks, we find that the impact of MCS activity on monthly maxima is not homogeneous within the month/block. To appropriately capture this relationship, we develop a mixed-frequency extreme value regression framework accommodating a covariate sampled at a frequency higher than that of the extreme observation
Ground-level ozone: Evidence of increasing serial dependence in the extremes
No full textAs exposure to successive episodes of high ground-level ozone concentrations can result in larger changes in respiratory function than occasional exposure buffered by lengthy recovery periods, the analysis of extreme values in a series of ozone concentrations requires careful consideration of not only the levels of the extremes but also of any dependence appearing in the extremes of the series. Increased dependence represents increased health risks and it is thus important to detect any changes in the temporal dependence of extreme values. In this paper we establish the first test for a change point in the extremal dependence of a stationary time series. The test is flexible, easy to use and can be extended along several lines. The asymptotic distributions of our estimators and our test are established. A large simulation study verifies the good finite sample properties. The test allows us to show that there has been a significant increase in the serial dependence of the extreme levels of ground-level ozone concentrations in Bloomsbury (UK) in recent years
Realized peaks over threshold: A time-varying extreme value approach with high-frequency-based measures
No full textRecent contributions to the financial econometrics literature exploit high-frequency (HF) data to improve models for daily asset returns. This paper proposes a new class of dynamic extreme value models that profit from HF data when estimating the tails of daily asset returns. Our realized peaks-over-threshold approach provides estimates for the tails of the time-varying conditional return distribution. An in-sample fit to the S&P 500 index returns suggests that HF data convey information on daily extreme returns beyond that included in low frequency (LF) data. Finally, out-of-sample forecasts of conditional risk measures obtained with HF measures outperform those obtained with LF measures
Realized extreme quantile: A joint model for conditional quantiles and measures of volatility with EVT refinements
Get PDFWe propose a new framework exploiting realized measures of volatility to estimate and forecast extreme quantiles. Our realized extreme quantile (REQ) combines quantile regression with extreme value theory and uses a measurement equation that relates the realized measure to the latent conditional quantile. Model estimation is performed by quasi maximum likelihood, and a simulation experiment validates this estimator in finite samples. An extensive empirical analysis shows that high-frequency measures are particularly informative of the dynamic quantiles. Finally, an out-of-sample forecast analysis of quantile-based risk measures confirms the merit of the REQ
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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