1,720,979 research outputs found
Can Volatility Models Explain Extreme Events?
No full textThis paper revisits several existing volatility models by the light of extremal dependence, that is, serial dependence in extreme returns. First, we investigate the extremal properties of different high-frequency-based volatility processes and show that only a subset of them can generate dependence in the extremes. Second, we corroborate the empirical evidence on extremal dependence in financial returns, showing that extreme returns present strong and persistent correlation and that extreme negative returns are much more correlated than positive ones. Finally, a large empirical analysis suggests that only models exhibiting extremal dependence and endowed with a leverage component can appropriately explain extreme events
Structural change to the persistence of the urban heat island
Full text linkThe 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
Full text linkExtreme 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
Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review
No full textOne of the key components of financial risk management is risk measurement. This typically requires modeling, estimating and forecasting tail-related quantities of the asset returns’ conditional distribution. Recent advances in the financial econometrics literature have developed several models based on Extreme Value Theory (EVT) to carry out these tasks. The purpose of this paper is to review these methods
A simple approach to the estimation of Tukey's gh distribution
No full textThe Tukey's gh distribution is widely used in situations where skewness and elongation are important features of the data. As the distribution is defined through a quantile transformation of the normal, the likelihood function cannot be written in closed form and exact maximum likelihood estimation is unfeasible. In this paper we exploit a novel approach based on a frequentist reinterpretation of Approximate Bayesian Computation for approximating the maximum likelihood estimates of the gh distribution. This method is appealing because it only requires the ability to sample the distribution. We discuss the choice of the input parameters by means of simulation experiments and provide evidence of superior performance in terms of Root-Mean-Square-Error with respect to the standard quantile estimator. Finally, we give an application to operational risk measurement
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
Mixed-frequency extreme value regression: Estimating the effect of mesoscale convective systems on extreme rainfall intensity
Full text linkUnderstanding 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
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
Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective
Full text linkThis 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
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