Estimation and prediction for nonlinear time series

In verschillende situaties verschijnt informatie als een rij van opeenvolgende metingen, een zogenaamde tijdreeks. De analyse van zulke tijdreeksen is gericht op het modelleren de onderliggende correlaties. Op deze manier probeert men inzicht te verkrijgen in de achterliggende mechanismes. Een belangrijke toepassing is het voorspellen van toekomstige waardes. Traditioneel werden lineaire modellen gebruikt, zoals AR en ARMA, waarin een volgende observatie een lineaire combinatie van voorgaande observaties is, samen met een onafhankelijke verstoring. Er zijn desalniettemin voorbeelden van tijdreeksen, zoals tijdreeksen gerelateerd aan chaotische dynamische systemen, waarvoor lineaire modellen zijn geschikt zijn wegens de sterke invloed van niet-lineariteiten. Dit werpt een aantal vragen op: hoe zulke tijdreeksen te herkennen en wat voor niet-lineaire methoden te gebruiken voor modellering en voorspelling. Dit proefschrift is een poging enkele nieuwe bijdragen te leveren aan de theorie van niet-lineaire tijdreeksen. ... Zie: Samenvatting

Properties of Xylan-Alginate Composites Reinforced with Ultrasound-Activated Birch Cellulose

Впервые для получения полимерных композиционных материалов (пленок) предложено использовать полисахариды: целлюлозу и ксилан, выделенные из древесины березы методом пероксидной делигнификации в среде «уксусная кислота – вода» в присутствии катализатора (NH4)6Mo7O24. Исходные компоненты: целлюлоза, ксилан, альгинат натрия охарактеризованы с использованием методов ИКС, ГХ, ГПХ, лазерной дифракции и химического анализа. Показано, что введение целлюлозы березы, активированной ультразвуком в состав ксилан-альгинатных композитов, приводит к увеличению прочности пленок, увеличению их барьерных свойств относительно паров воды, а также к уменьшению растворимости пленок в водеFor the first time, polysaccharides: cellulose and xylan, isolated from birch wood by peroxide delignification in “acetic acid-water” medium in the presence of catalyst (NH4)6Mo7O24, were proposed to be used to obtain polymer composites (films). The initial components: cellulose, xylan, sodium alginate were characterized using the methods of FTIR, GC, GPC, laser diffraction and chemical analysis.It is shown that the introduction of ultrasound-activated birch bark cellulose into the composition of xylan-alginate composites leads to an increase in the strength of the films, an increase in their barrier properties with respect to water vapor, and also reduces the solubility of the films in wate

RISK MANAGEMENT WITH TAIL COPULAS FOR EMERGING MARKET PORTFOLIOS

We study the tail dependence of emerging markets in South-East Asia and we show that this tail dependence increased during the financial crisis of 2008-2010. After applying ARMA-GARCH models to individual markets, we fit various copulas to the pairs of market returns and find that in most cases tail copulas such as the t-copula and Symmetrised Joe-Clayton provide the best fit. During the crisis, nonlinear dependence measures (such as rank correlations) and the tail dependence coefficients typically increased by tenfold or even more. We apply our method to portfolio Value-at-Risk estimation and show that the copula-based Value-at-Risk performs remarkably well for South-East Asian market portfolios

Three-factor commodity forward curve model and its joint P and Q dynamics

In this paper, we propose a new framework for modeling commodity forward curves. The proposed model describes the dynamics of fundamental driving factors simultaneously under physical (P) and risk-neutral (Q) probability measures. Our model is an extension of the forward curve model by Borovkova and Geman (2007), into several directions. It is a three-factor model, incorporating the synthetic spot price, based on liquidly traded futures, stochastic level of mean reversion and an analog of the stochastic convenience yield. We develop an innovative calibration mechanism based on the Kalman filtering technique and apply it to a large set of Brent oil futures. Additionally, we investigate properties of the time-dependent market price of risk in oil markets. We apply the proposed modeling framework to derivatives pricing, risk management and counterparty credit risk. Finally, we outline a way of adjusting the proposed model to account for negative oil futures prices observed recently due to coronavirus pandemic

An ensemble of LSTM neural networks for high-frequency stock market classification

We propose an ensemble of long–short-term memory (LSTM) neural networks for intraday stock predictions, using a large variety of technical analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible nonstationarities in an innovative way. The performance of the models is measured by area under the curve of the receiver operating characteristic. We evaluate the predictive power of our model on several US large-cap stocks and benchmark it against lasso and ridge logistic classifiers. The proposed model is found to perform better than the benchmark models or equally weighted ensembles

Analysis and Modelling of Electricity Futures Prices

We model electricity futures prices using a seasonal forward curve model, quantifying seasonalities by a deterministic seasonal forward premium. Stochastic features of the futures prices are contained in the stochastic forward premium: a quantity analogous to the well-known convenience yield. The model parameters are estimated from the historical data of IPE electricity futures prices and the spark spread, and electricity forward curves are deseasonalized to reveal their underlying stochastic structure. We apply principal component analysis to the deseasonalized forward curves and develop trading strategies using indicators based on these principal components.