Wavelet Based Detection of Outliers in Volatility Time Series Models

We introduce a new wavelet based procedure for detecting outliers in financial discrete time series.The procedure focuses on the analysis of residuals obtained from a model fit,and applied to the Generalized Autoregressive Conditional Heteroskedasticity(GARCH)like model,but not limited to these models.We apply the Maximal-Overlap Discrete Wavelet Transform(MODWT)to the residuals and compare their wavelet coefficients against quantile thresholds to detect outliers.Our methodology has several advantages over existing methods that make use of the standard Discrete Wavelet Transform(DWT).The series sample size does not need to be a power of 2 and the transform can explore any wavelet filter and be run up to the desired level.Simulated wavelet quantiles from a Normal and Student t-distribution are used as threshold for the maximum of the absolute value of wavelet coefficients.The performance of the procedure is illustrated and applied to two real series:the closed price of the Saudi Stock market and the S&P 500 index respectively.The efficiency of the proposed method is demonstrated and can be considered as a distinct important addition to the existing methods.

Comparative study on yields and quality parameters of argan oils extracted by conventional and green extraction techniques

Argan oil is most frequently sold as pure oil,which can be directly applied topically due to its cosmetological proprieties or ingested in order to provide several health benefits.It's also commonly mixed into a number of cosmetic products like shampoos,soaps and conditioners.In this study we aimed to improve the argan oil extraction yield and quality parameters by comparing the effects of different extraction technologies.Argan kernel oils were extracted using four methods:mechanical cold press,Soxhlet extraction with n-hexane,supercritical fluid extraction(SFE),and enzyme assisted extraction with three different enzyme solutions cellulase(cellulast),pectinase(Pectinex)and a mixture of carbohydrase enzymes(Viscozyme®).The quality parameters was evaluated by determining the acid,peroxide and iodine values aswell as the extinction coefficients K_(232) and K_(270) as measures of conjugated dienes and trienes,respectively.The results showed that the highest yield(66.37%±3.3%)was obtained by enzyme assisted extraction using the carbohydrases enzymes mixture(Viscozyme®),followed by Soxhlet extraction(59.5%±3.1%)and pectinase extraction(52.03%±3.55%).All argan oils samples obtained by the different methods showed a good oxidation stability,with acid,peroxide and iodine values lower than 0.8 mg/g,15 meq/kg and 110 g/100 g according to the official argan oil norm,respectively.The results of argan oils quality parameters demonstrated that the enzyme extracted argan oils showed low oxidation,with the following quality parameters:acid values(0.4-0.6 mg/g),iodine values(95-100 g/100 g),dienes(K_(232)<2),trienes(K_(270)<0.35),and peroxide values(<1.5 meq/kg).The results proved that the enzyme assisted extraction method can be applied to improve the argan oil yield without affecting the oil quality.The enzyme extraction method may be a great alternative to solvent and cold press extractions for this eco-friendly processing approach.

Image analysis of Moroccan carob seeds(Ceratonia siliqua L.)revealed substantial intraspecific variations depending on climate and geographic origin

Background:The carob tree(Ceratonia siliqua L.)is one of the most iconic tree species of the Mediterranean region,with valuable economic,ecological and cultural value.Carob has been exploited around the Mediterranean region since antiquity and has been regarded as an important component of natural habitats and traditional agroecosystems.Several studies have focused on its morphological,biochemical,and genetic diversity.However,less is known about the intraspecific variation of seed traits.In this regard,and as an overall objective,we intend to evaluate the amplitude and the expression of intraspecific variations of carob seed traits at different ecological scales ranging from individual trees to different geographical landscapes.In addition,we investigated how the climate along the study area affects the extent of carob seed variability.Using image analysis techniques,we measured seven traits related to the size and the shape of 1740 seeds collected from 18 populations of spontaneous C.siliqua distributed along a latitudinal transect in Morocco under different bioclimatic conditions.Results:The morphometric analysis of carob seed showed the effectiveness of adopted approach to highlight the amount and the amplitude of intraspecific variation according to geographic and climatic factors.Seed trait analysis revealed high intraspecific variability,explained by differences between and among carob populations and geographic zones.Seed area,perimeter,length,and width showed the largest variability between geographic zones.However,circularity,aspect ratio,and seed roundness showed higher variability at the tree level.Finally,our results show that seed traits vary depending on altitude and climate condition.Conclusions:Revealing the amount and the structure of intraspecific traits variability of carob seed provides interesting insights to understand the mechanisms underlying trees adaptation to various environmental and ecological conditions.Therefore,intraspecific variation of seed traits should be integrated into trait‑based functional ecology to assess plant species responses to environmental changes.