The Impact of External Factors on Stock Return Volatility in the European Banking Sector

Purpose: The main aim of the paper is to examine the impact of external determinants on the banking stock return volatility to evaluate it in terms of the stock market capitalization. Design/methodology/approach: The research was conducted on 182 banks from 26 countries. The sample selected for the study includes all European banks listed on the stock exchange. Quarterly data from the period between 2004 and 2016 was used; it was collected and compiled over a period of 2 years. The research method applied was the panel data model with fixed effects (with or without a robust estimator) and random effects. Findings: Determinants that have a major and statistically significant impact on the analyzed dependent variables are: the unemployment rate, the real interest rate, the beta in Sharpe’s Single-Index Model and the implied volatility of the S&P 500 index and the EURO STOXX50 index. Research limitations/implications: Insights about the strength and direction of influence of these variables on stock return volatility are a valuable addition to the existing body of knowledge that investors resort to when making decisions relating to the capital market. Limitations: The main limitation of this study lies in the fact that the results of the analysis apply solely to the banking sector. Originality/value: Insights about the strength and direction of influence of these variables on stock return volatility are a valuable addition to the existing body of knowledge that investors resort to when making decisions relating to the capital market.

Developing a photometric estimate of quasar redshifts: An independent measurement of the distances to the most powerful objects in the Universe

<p>While spectroscopy is the standard method of measuring the redshift of luminous objects, it is a time-intensive technique, requiring, in some cases, hours of telescope time for a single source. Additionally, spectroscopy favours brighter objects, and therefore introduces an intrinsic bias towards luminous or closer sources. A simple method of estimating the redshift through photometry would prove invaluable to forthcoming surveys on the next generation of large radio telescopes, as well as alleviating the inherent bias towards the most optically bright sources. While there is a well-established correlation between the near-infrared K-band magnitude and redshift for galaxies, we find that the K-z relation breaks down for samples dominated by quasi-stellar objects (QSOs). Current methods of estimating photometric redshift rely either on template spectra, which requires a high number of infrared photometry points, or computationally intensive machine learning methods. Using photometric data from the Sloan Digital Sky Survey (SDSS) we investigate the relationship between combinations of magnitudes of a group of quasars, and their redshift. We find a high correlation between the colour relation (I-W2)/(W3-U) and redshift for a group of broad-line emission sources from the SDSS, and we conclude that this could be a robust estimator of the redshift.</p>

Developing a photometric estimate of quasar redshifts: An independent measurement of the distances to the most powerful objects in the Universe

<p>While spectroscopy is the standard method of measuring the redshift of luminous objects, it is a time-intensive technique, requiring, in some cases, hours of telescope time for a single source. Additionally, spectroscopy favours brighter objects, and therefore introduces an intrinsic bias towards luminous or closer sources. A simple method of estimating the redshift through photometry would prove invaluable to forthcoming surveys on the next generation of large radio telescopes, as well as alleviating the inherent bias towards the most optically bright sources. While there is a well-established correlation between the near-infrared K-band magnitude and redshift for galaxies, we find that the K-z relation breaks down for samples dominated by quasi-stellar objects (QSOs). Current methods of estimating photometric redshift rely either on template spectra, which requires a high number of infrared photometry points, or computationally intensive machine learning methods. Using photometric data from the Sloan Digital Sky Survey (SDSS) we investigate the relationship between combinations of magnitudes of a group of quasars, and their redshift. We find a high correlation between the colour relation (I-W2)/(W3-U) and redshift for a group of broad-line emission sources from the SDSS, and we conclude that this could be a robust estimator of the redshift.</p>

Robust Multivariate Shewhart Control Chart Based on the Stahel-Donoho Robust Estimator and Mahalanobis Distance for Multivariate Outlier Detection

While researchers and practitioners may seamlessly develop methods of detecting outliers in control charts under a univariate setup, detecting and screening outliers in multivariate control charts pose serious challenges. In this study, we propose a robust multivariate control chart based on the Stahel-Donoho robust estimator (SDRE), whilst the process parameters are estimated from phase-I. Through intensive Monte-Carlo simulation, the study presents how the estimation of parameters and presence of outliers affect the efficacy of the Hotelling T2 chart, and then how the proposed outlier detector brings the chart back to normalcy by restoring its efficacy and sensitivity. Run-length properties are used as the performance measures. The run length properties establish the superiority of the proposed scheme over the default multivariate Shewhart control charting scheme. The applicability of the study includes but is not limited to manufacturing and health industries. The study concludes with a real-life application of the proposed chart on a dataset extracted from the manufacturing process of carbon fiber tubes.

Comparison of Robust Estimators’ Performance for Detecting Outliers in Multivariate Data

In multivariate data, outliers are difficult to detect especially when the dimension of the data increase. Mahalanobis distance (MD) has been one of the classical methods to detect outliers for multivariate data. However, the classical mean and covariance matrix in MD suffered from masking and swamping effects if the data contain outliers. Due to this problem, many studies used a robust estimator instead of the classical estimator of mean and covariance matrix. In this study, the performance of five robust estimators namely Fast Minimum Covariance Determinant (FMCD), Minimum Vector Variance (MVV), Covariance Matrix Equality (CME), Index Set Equality (ISE), and Test on Covariance (TOC) are investigated and compared. FMCD has been widely used and is known as among the best robust estimator. However, there are certain conditions that FMCD still lacks. MVV, CME, ISE and TOC are innovative of FMCD. These four robust estimators improve the last step of the FMCD algorithm. Hence, the objective of this study is to observe the performance of these five estimator to detect outliers in multivariate data particularly TOC as TOC is the latest robust estimator. Simulation studies are conducted for two outlier scenarios with various conditions. There are three performance measures, which are pout, pmask and pswamp used to measure the performance of the robust estimators. It is found that the TOC gives better performance in pswamp for most conditions. TOC gives better results for pout and pmask for certain conditions.

High-dimensional semi-supervised learning: in search of optimal inference of the mean

A fundamental challenge in semi-supervised learning lies in the observed data’s disproportional size when compared with the size of the data collected with missing outcomes. An implicit understanding is that the dataset with missing outcomes, being significantly larger, ought to improve estimation and inference. However, it is unclear to what extent this is correct. We illustrate one clear benefit: root-n inference of the outcome’s mean is possible while only requiring a consistent estimation of the outcome, possibly at a rate slower than root-n. This is achieved by a novel k-fold cross-fitted, double robust estimator. We discuss both linear and nonlinear outcomes. Such an estimator is particularly suited for models that naturally do not admit root-n consistency, such as high-dimensional, nonparametric, or semiparametric models. We apply our methods to the heterogeneous treatment effects.

Applicability of Accurate Ground Motion Estimation Using Initial P Wave for Earthquake Early Warning

The earthquake early warning (EEW) system is capable of mitigating seismic hazards and reducing deaths, injuries, and economic losses. Although EEW approaches have already been developed worldwide, improving the accuracy and applicability is still controversial. Aiming at the ground motion estimation using the initial P wave, we investigated eight representative characteristic parameters, i.e., the peak measurements and integral quantities, using the database of the 2008 Wenchuan earthquake, where the aftershocks with the criteria that 4.0 ≤ Ms ≤ 6.5 and epicentral distance less than 150 km are analyzed. We established the relationships between the eight characteristic parameters and four ground motion parameters, respectively, based on which the estimation accuracy and reliability and the extent to which the increasingly expanding time windows could affect the estimates are analyzed accordingly. We found that the integral quantities could also be a robust estimator for peak ground acceleration (PGA), peak ground velocity (PGV), and spectral intensity (SI), while the peak measurement is more useful in estimating peak ground displacement (PGD). In addition, for estimating the ground motion of events with magnitudes less than 6.5, a 2-s window could effectively improve the estimation accuracy by approximately 11.5–18.5% compared with using a 1-s window, as the window increases to 3 s, the accuracy would further improve while the growth rate will be reduced to around 3.0–8.0%.

Outlier detection in non-elliptical data by kernel MRCD

The minimum regularized covariance determinant method (MRCD) is a robust estimator for multivariate location and scatter, which detects outliers by fitting a robust covariance matrix to the data. Its regularization ensures that the covariance matrix is well-conditioned in any dimension. The MRCD assumes that the non-outlying observations are roughly elliptically distributed, but many datasets are not of that form. Moreover, the computation time of MRCD increases substantially when the number of variables goes up, and nowadays datasets with many variables are common. The proposed kernel minimum regularized covariance determinant (KMRCD) estimator addresses both issues. It is not restricted to elliptical data because it implicitly computes the MRCD estimates in a kernel-induced feature space. A fast algorithm is constructed that starts from kernel-based initial estimates and exploits the kernel trick to speed up the subsequent computations. Based on the KMRCD estimates, a rule is proposed to flag outliers. The KMRCD algorithm performs well in simulations, and is illustrated on real-life data.

Global Optimization of Redescending Robust Estimators

Robust estimation has proved to be a valuable alternative to the least squares estimator for the cases where the dataset is contaminated with outliers. Many robust estimators have been designed to be minimally affected by the outlying observations and produce a good fit for the majority of the data. Among them, the redescending estimators have demonstrated the best estimation capabilities. It is little known, however, that the success of a robust estimation method depends not only on the robust estimator used but also on the way the estimator is computed. In the present paper, we show that for complicated cases, the predominant method of computing the robust estimator by means of an iteratively reweighted least squares scheme may result in a local optimum of significantly lower quality than the global optimum attainable by means of a global optimization method. Further, the sequential use of the proposed global robust estimation proves to successfully solve the problem of M-split estimation, that is, the determination of parameters of different functional models implicit in the data.