Global financial crisis and multiscale systematic risk: Evidence from selected European stock markets

In this paper, we have investigated the impact of the global financial crisis on the multi-horizon nature of systematic risk and market risk using daily data of eight major European equity markets over the period of 2005-2018. The method is based on a wavelet multiscale approach within the framework of a capital asset pricing model. Empirical results demonstrate that beta coefficients have a multiscale tendency and betas tend to increase at higher scales (lower frequencies). In addition, the size of betas and R2s tend to increase during the crisis period compared with the pre-crisis period. The multiscale nature of the betas is consistent with the fact that stock market investors have different time horizons due to different trading strategies. Our results based on scale dependent value-at-risk (VaR) suggest that market risk tends to be more concentrated at lower time scales (higher frequencies) of the data. Moreover, the scale-by-scale estimates of VaR have increased almost three fold for every market during the crisis period compared with the pre-crisis period. Finally, our approach allows for accurately forecasting time-dependent betas and VaR

Wavelet Neural Network Methodology for Ground Resistance Forecasting

Motivated by the need of engineers for a flexible and reliable tool for estimating and predicting grounding systems behavior, this study developed a model that accurately describes and forecasts the dynamics of ground resistance variation. It is well-known that grounding systems are a key of high importance for the safe operation of electrical facilities, substations, transmission lines and, generally, electric power systems. Yet, in most cases, during the design stage, electrical engineers and researchers have limited information regarding the terrain’s soil resistivity variation. Moreover, the periodic measurement of ground resistance is hindered very often by the residence and building infrastructure. The model, developed in the present study, consists of a nonlinear, nonparametric Wavelet Neural Network (WNN), trained in field measurements of soil resistivity and rainfall height, observed the past four years. The proposed framework is tested in five different grounding systems with different ground enhancing compounds, so that can be used for the evaluation of the behavior of several ground enhancing compounds, frequently used in grounding practice. The research results indicate that the WNN can constitute an accurate model for ground resistance forecasting and can be a useful tool in the disposal of electrical engineers. Therefore, this paper introduces the wavelet analysis in the field of ground resistance evaluation and endeavors to take advantage of the benefits of computational intelligence

Wavelet Neural Networks: A Practical Guide

Wavelet networks (WNs) are a new class of networks which have been used with great success in a wide range of application. However a general accepted framework for applying WNs is missing from the literature. In this study, we present a complete statistical model identification framework in order to apply WNs in various applications. The following subjects were thorough examined: the structure of a WN, training methods, initialization algorithms, variable significance and variable selection algorithms, model selection methods and finally methods to construct confidence and prediction intervals. In addition the complexity of each algorithm is discussed. Our proposed framework was tested in two simulated cases, in one chaotic time series described by the Mackey-Glass equation and in three real datasets described by daily temperatures in Berlin, daily wind speeds in New York and breast cancer classification. Our results have shown that the proposed algorithms produce stable and robust results indicating that our proposed framework can be applied in various applications

Real Estate valuation and forecasting in non-homogeneous markets: A case study in Greece during the financial crisis

In this paper we develop an automatic valuation model for property valuation using a large database of historical prices from Greece. The Greek property market is an inefficient, nonhomogeneous market, still at its infancy and governed by lack of information. As a result modelling the Greek real estate market is a very interesting and challenging problem. The available data cover a wide range of properties across time and include the financial crisis period in Greece which led to tremendous changes in the dynamics of the real estate market. We formulate and compare linear and non-linear models based on regression, hedonic equations and artificial neural networks. The forecasting ability of each method is evaluated out-of-sample. Special care is given on measuring the success of the forecasts but also on identifying the property characteristics that lead to large forecasting errors. Finally, by examining the strengths and the performance of each method we apply a combined forecasting rule to improve forecasting accuracy. Our results indicate that the proposed methodology constitutes an accurate tool for property valuation in a non-homogeneous, newly developed market

Hedging Performance of Multiscale Hedge Ratios

In this study, the wavelet multiscale model is applied to selected assets to hedge time-dependent exposure of an agent with a preference for a certain hedging horizon. Based on the in-sample and out-of-sample portfolio variances, the wavelet-based GARCH model produces the lowest variances. From a utility standpoint, wavelet networks combined with GARCH have the highest utility. Finally, the wavelet GARCH model has the lowest minimum capital risk requirements (MCRR). Overall, the wavelet GARCH and wavelet networks offer improvements over traditional hedging models

Non-Destructive Early Detection and Quantitative Severity Stage Classification of Tomato Chlorosis Virus (ToCV) Infection in Young Tomato Plants Using Vis–NIR Spectroscopy

Tomato chlorosis virus (ToCV) is a serious, emerging tomato pathogen that has a significant impact on the quality and quantity of tomato production worldwide. Detecting ToCV via means of spectral measurements in an early pre-symptomatic stage offers an alternative to the existing laboratory methods, leading to better disease management in the field. In this study, leaf spectra from healthy and diseased leaves were measured with a spectrometer. The diseased leaves were subjected to RT-qPCR for the detection and quantification of the titer of ToCV. Neighborhood component analysis (NCA) algorithm was employed for the feature selection of the effective wavelengths and the most important vegetation indices out of the 24 that were tested. Two machine learning methods, namely XY-fusion network (XY-F) and multilayer perceptron with automated relevance determination (MLP–ARD), were employed for the estimation of the disease existence and viral load in the tomato leaves. The results showed that before outlier elimination, the MLP–ARD classifier generally outperformed the XY-F network with an overall accuracy of 92.1% against 88.3% for the XY-F. Outlier elimination contributed to the performance of the classifiers as the overall accuracy for both XY-F and MLP–ARD reached 100%