7,249 research outputs found
Bayesian outlier detection in Capital Asset Pricing Model
We propose a novel Bayesian optimisation procedure for outlier detection in
the Capital Asset Pricing Model. We use a parametric product partition model to
robustly estimate the systematic risk of an asset. We assume that the returns
follow independent normal distributions and we impose a partition structure on
the parameters of interest. The partition structure imposed on the parameters
induces a corresponding clustering of the returns. We identify via an
optimisation procedure the partition that best separates standard observations
from the atypical ones. The methodology is illustrated with reference to a real
data set, for which we also provide a microeconomic interpretation of the
detected outliers
Extreme Value Theory Filtering Techniques for Outlier Detection
We introduce asymptotic parameter-free hypothesis tests based on extreme value theory to detect outlying observations in finite samples. Our tests have nontrivial power for detecting outliers for general forms of the parent distribution and can be implemented when this is unknown and needs to be estimated. Using these techniques this article also develops an algorithm to uncover outliers masked by the presence of influential observations
Robust estimation of dimension reduction space
Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions. We show that the recently proposed methods by Xia et al. (2002) can be made robust in such a way that preserves all advantages of the original approach. Their extension based on the local one-step M-estimators is su±ciently robust to outliers and data from heavy tailed distributions, it is relatively easy to implement, and surprisingly, it performs as well as the original methods when applied to normally distributed data.Dimension reduction, Nonparametric regression, M-estimation
Intraday forecasts of a volatility index: Functional time series methods with dynamic updating
As a forward-looking measure of future equity market volatility, the VIX
index has gained immense popularity in recent years to become a key measure of
risk for market analysts and academics. We consider discrete reported intraday
VIX tick values as realisations of a collection of curves observed sequentially
on equally spaced and dense grids over time and utilise functional data
analysis techniques to produce one-day-ahead forecasts of these curves. The
proposed method facilitates the investigation of dynamic changes in the index
over very short time intervals as showcased using the 15-second high-frequency
VIX index values. With the help of dynamic updating techniques, our point and
interval forecasts are shown to enjoy improved accuracy over conventional time
series models.Comment: 29 pages, 5 figures, To appear at the Annals of Operations Researc
Two Procedures for Robust Monitoring of Probability Distributions of Economic Data Streams induced by Depth Functions
Data streams (streaming data) consist of transiently observed, evolving in
time, multidimensional data sequences that challenge our computational and/or
inferential capabilities. In this paper we propose user friendly approaches for
robust monitoring of selected properties of unconditional and conditional
distribution of the stream basing on depth functions. Our proposals are robust
to a small fraction of outliers and/or inliers but sensitive to a regime change
of the stream at the same time. Their implementations are available in our free
R package DepthProc.Comment: Operations Research and Decisions, vol. 25, No. 1, 201
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