Forecasting the Oil Volatility Index Using Factors of Uncertainty

The oil volatility index (OVX) has attracted the attention of investors, as oil prices have been subject to high degrees of variation in the last few decades, and investors would therefore benefit from obtaining accurate forecasts of OVX. In this paper, we aim to develop models that can accurately generate OVX forecasts. The contribution of our study to the literature lies in the incorporation of different factors that reflect uncertainty as potential drivers of OVX. For example, implied volatility (IV) indices, such as the VIX and GVZ are examined. Apart from the inclusion of IV indices, we investigate whether other uncertainty indicators play a significant role in generating OVX forecasts. Our results show that the predictive ability of the models is not enhanced by the inclusion of most of the aforementioned factors of uncertainty, with the single exception of the U.S. economic policy uncertainty index, which seems to improve the forecasting ability of a simple model that focuses on the OVX as a target variable

Uplink NOMA for UAV-Aided Maritime Internet-of-Things

Maritime activities are vital for economic growth, being further accelerated by various emerging maritime Internet of Things (IoT) use cases, including smart ports, autonomous navigation, and ocean monitoring systems. However, broadband, low-delay, and reliable wireless connectivity to the ever-increasing number of vessels, buoys, platforms and sensors in maritime communication networks (MCNs) has not yet been achieved. Towards this end, the integration of unmanned aerial vehicles (UAVs) in MCNs provides an aerial dimension to current deployments, relying on shore-based base stations (BSs) with limited coverage and satellite links with high latency. In this work, a maritime IoT topology is examined where direct uplink communication with a shore BS cannot be established due to excessive pathloss. In this context, we employ multiple UAVs for end-to-end connectivity, simultaneously receiving data from the maritime IoT nodes, following the non-orthogonal multiple access (NOMA) paradigm. In contrast to other UAV-aided NOMA schemes in maritime settings, dynamic decoding ordering at the UAVs is used to improve the performance of successive interference cancellation (SIC), considering the rate requirements and the channel state information (CSI) of each maritime node towards the UAVs. Moreover, the UAVs are equipped with buffers to store data and provide increased degrees of freedom in opportunistic UAV selection. Simulations reveal that the proposed opportunistic UAV-aided NOMA improves the average sum-rate of NOMA-based maritime IoT communication, leveraging the dynamic decoding ordering and caching capabilities of the UAVs

What should be taken into consideration when forecasting oil implied volatility index?

Crude oil is considered a key commodity in all the economies around the world. This study forecasts the oil volatility index (OVX), which is the market’s expectation of future oil volatility, by incorporating information from other asset classes. The literature does not extensively test the long memory of the targeted volatility. Thus, we estimate the Hurst exponent implementing a rolling window rescaled analysis. We provide evidence for a strong long memory in the implied volatility (IV) indices which justifies the use of the HAR model in obtaining multiple days ahead OVX forecasts. We also define a dynamic model averaging (DMA) structure in the HAR model in order to allow for IV indices from other asset classes to be applicable at different time periods. The implementation of the DMA-HAR models informs forecasters to focus on the major stock market IV indices, and more specifically on the DJIA Volatility Index. Our results lead us to the conclusion that accurate OVX forecasts are obtained for short- and mid-run forecasting horizons. The evaluation framework is not limited to statistical loss functions but also embodies an options straddle trading strategy

Learning to Fulfill the User Demands in 5G-enabled Wireless Networks through Power Allocation: a Reinforcement Learning approach

The goal of the study presented in this paper is to evaluate the performance of a proposed Reinforcement Learning (RL) power allocation algorithm. The algorithm follows a demand-driven power adjustment approach aiming at maximizing the number of users inside a coverage area that experience the requested throughput to accommodate their needs. In this context, different Quality of Service (QoS) classes, corresponding to different throughput demands, have been taken into account in various simulation scenarios. Considering a realistic network configuration, the performance of the RL algorithm is tested under strict user demands. The results suggest that the proposed modeling of the RL parameters, namely the state space and the rewarding system, is promising when the network controller attempts to fulfill the user requests by regulating the power of base stations. Based on comparative simulations, even for strict demands requested by multiple users (2.5 – 5 Mbps), the proposed scheme achieves a performance rate of about 96%

A Stacking Ensemble Learning Model for Waste Prediction in Offset Printing

The production of quality printing products requires a highly complex and uncertain process, which leads to the unavoidable generation of printing defects. This common phenomenon has severe impacts on many levels for Offset Printing manufacturers, ranging from a direct economic loss to the environmental impact of wasted resources. Therefore, the accurate estimation of the amount of paper waste expected during each press run, will minimize the paper consumption while promoting environmentally sustainable principles. This work proposed a Machine Leaning (ML) framework for proactively predicting paper waste for each printing order. Based on a historical dataset extracted by an Offset Printing manufacturer, a two-level stacking ensemble learning model combining Support Vector Machine (SVM), Kernel Ridge Regression (KRR) and Extreme Gradient Boosting (XGBoost) as base learners, and Elastic Net as a meta-learner, was trained and evaluated using cross-validation. The evaluation outcomes demonstrated the ability of the proposed framework to accurately estimate the amount of waste expected to be generated for each printing run, by significantly outperforming the rest of the benchmarking models

Development, validation and evaluation of a patient information booklet for rectal cancer survivors with a stoma:A three-step approach

OBJECTIVE: Quantitatively measure the degree of patient satisfaction and perceived acquired knowledge through the development of a patient information booklet for rectal cancer survivors with a stoma, according to a novel three-step approach. METHODS: The study included a systematic literature review to identify relevant information for the booklet, which was validated by experts based on relevance, clarity and essentiality. It underwent testing on quality, readability, and layout and design and was quantitatively evaluated by rectal cancer survivors with a stoma. RESULTS: In total, 145 articles were used for the development of the booklet. It scored 91% for relevance according to 17 experts, 70% for readability, 75.63% for quality and 23 out of 32 for design. The mean score of patient satisfaction was 8.03 out of 10. All 20 patients found the booklet 'useful' and 95% felt better informed. CONCLUSIONS: The booklet scored high for patient satisfaction and increased perceived acquired information. It ensured satisfactory levels of quality, readability, and layout and design. PRACTICE IMPLICATIONS: This study offers a novel three-step approach for development of informational tools for cancer survivors, assuring that a variety of newly created written patient materials would be of increased quality and relevance to any target population

A hybrid optimization scheme for self-potential measurements due to multiple sheet-like bodies in arbitrary 2D resistivity distributions

Self-potential (SP) is a passive geophysical method that can be applied in a straightforward manner with minimum requirements in the field. Nonetheless, interpretation of SP data is particularly challenging due to the inherited nonuniqueness present in all potential methods. Incorporating information regarding the target of interest can facilitate interpretation and increase the reliability of the final output. In the current paper, a novel method for detecting multiple sheet-like targets is presented. A numerical framework is initially described that simulates sheet-like bodies in an arbitrary 2D resistivity distribution. A scattered field formulation based on finite-differences is employed that allows the edges of the sheet to be independent of the grid geometry. A novel analytical solution for two-layered models is derived and subsequently used to validate the accuracy of the proposed numerical scheme. Lastly, a hybrid optimization is proposed that couples linear least-squares with particle-swarm optimization (PSO) in order to effectively locate the edges of multiple sheet-like bodies. Through numerical and real data, it is proven that the hybrid optimization overcomes local minimal that occur in complex resistivity distributions and converges substantially faster compared to traditional PSO

The Use of Stock Options for Resource Allocation and Management on Content Delivery Networks

Η παρούσα διπλωματική εργασία πραγματεύεται τη χρήση δικαιωμάτων προαίρεσης ως μία αποτελεσματική μέθοδο κατανομής και διαχείρισης πόρων, οι οποίοι διανέμονται από σύγχρονα δίκτυα διανομής περιεχομένων. Επιχειρείται η αλληγορική συσχέτιση μεταξύ των επιστημών των Οικονομικών και της Πληροφορικής εξετάζοντας τη χρησιμότητα ενός καθαρά οικονομικού όρου - όπως είναι τα δικαιώματα προαίρεσης - για τη δημιουργία ενός προδραστικού μοντέλου διαχείρισης πόρων, το οποίο μπορεί να εφαρμοστεί άμεσα στο χώρο των δικτύων διανομής περιεχομένου, δηλαδή στην πλειοψηφία των υποδομών παρόχων περιεχομένου της καθημερινότητας της ψηφιακής εποχής. Τα εισαγωγικά κεφάλαια παρουσιάζουν τον ορισμό των σύγχρονων δικτύων παροχής περιεχομένου και υπογραμμίζουν την ανάγκη για αποτελεσματική διαχείριση πόρων. Ταυτόχρονα, παρουσιάζεται η έννοια και η σημασία της παροχής ποιότητας υπηρεσιών στην παρούσα εποχή, μαζί με τις βασικές τεχνικές με τις οποίες αυτή επιτυγχάνεται στη σύγχρονη αγορά. Στη συνέχεια παρουσιάζεται το υπάρχον μοντέλο πρόβλεψης για την προσομοίωση της αγοράς, μαζί με τους μηχανισμούς και τα εμπλεκόμενα μέρη. Το μοντέλο αυτό χρησιμεύει ως θεμελιώδης ιδέα για την περαιτέρω έρευνα και το μοντέλο που προτείνεται στο πλαίσιο της εργασίας. Αναφέρονται επίσης οι βασικές έννοιες με τις οποίες ασχολείται η εργασία - δηλαδή οι ορισμοί της Δευτερεύουσας Αγοράς και των Δικαιωμάτων Προαίρεσης, οι οποίοι αναλύονται σε βάθος στα κεφάλαια 3 και 4 αντίστοιχα. Παρουσιάζεται η εξίσωση Black-Scholes για την αξιολόγηση των δικαιωμάτων προαίρεσης αγοράς μετοχών, στην οποία βασίζεται το προτεινόμενο μοντέλο, το οποίο με τη σειρά του παρουσιάζεται στα επόμενα κεφάλαια. Στο κεφάλαιο 5 αναλύονται τα σενάρια της αγοράς με βάση την ανάγκη χρήσης και εκμετάλλευσης των δικαιωμάτων προαίρεσης αγοράς μετοχών, υπογραμμίζοντας τη σημασία τους, ενώ στο κεφάλαιο 6 παρουσιάζονται οι βασικές μέθοδοι και πολιτικές κοστολόγησης και τιμολόγησης από την πλευρά των δικτύων διανομής περιεχομένου. Το κεφάλαιο 7 παρουσιάζει το προτεινόμενο μοντέλο έχοντας ως βάση όλες τις προαναφερθείσες έννοιες. Ο προτεινόμενος αλγόριθμος παρουσιάζεται βήμα προς βήμα και υπογραμμίζει τη διαφοροποίηση του μοντέλου σε σχέση με άλλα μοντέλα. Τα σενάρια προσομοίωσης του μοντέλου παρουσιάζονται στο κεφάλαιο 8, όπου καταγράφονται οι μετρήσεις και αναλύονται τα αποτελέσματα στο πλαίσιο της συμβολής του μοντέλου προς το στόχο της αποτελεσματικότερης διαχείρισης των πόρων στην αγορά των δικτύων διανομής περιεχομένου. Το κεφάλαιο 9 καταγράφει τα συμπεράσματα και παρουσιάζει ιδέες και ερωτήματα που μπορεί να αποτελέσουν αντικείμενο περαιτέρω μελέτης, ενώ στο τέλος της εργασίας αναφέρονται το βιβλιογραφικό έργο και άλλες πηγές, οι οποίες συνέβαλαν στην εκπόνηση της διπλωματικής εργασίας. Επίσης εμπεριέχεται ένα τριμερές υπόμνημα. Στο πρώτο μέρος παρουσιάζονται πολιτικές γνωστών παρόχων της εποχής για την κοστολόγηση πόρων. Το δεύτερο μέρος περιέχει τις βασικές συναρτήσεις του πηγαίου κώδικα Java του προγράμματος, το οποίο αναπτύχθηκε για τις προσομοιώσεις του προτεινόμενου μοντέλου ενώ το τρίτο μέρος περιέχει τον πίνακα τιμολόγησης του δικτύου διανομής περιεχομένου, όπως αυτός χρησιμοποιείται στις προσομοιώσεις.This thesis presents and examines the use of Stock Options as an efficient allocation and management method of resources, which are distributed from modern infrastructure Content Delivery Networks. The allegorical correlation between the scientific fields of Economics and Computer Science is attempted by examining the usefulness of a purely financial term - such as Stock Options - for the creation of a proactive resource management model, which can be directly applied to content distribution networks, namely the majority of content providers' infrastructures in the everyday life of the digital age. The introductory chapters present the definition of modern Content Delivery Networks and highlight the need for efficient resource management. The concept and the importance of providing service quality in the present era are also presented, along with the basic techniques by which it is achieved in the modern CDN ecosystem. Next, an existing resource prediction model of the market’s simulation is presented, along with its mechanisms and the parties involved. This model serves as the fundamental idea on our further research and our own proposed model. The basic concepts that the thesis is deeply concerned with - namely the definitions of the Secondary Market and the Stock Options - are also introduced. The core concepts, namely the Secondary Market and the Stock Options are analyzed in detail in chapters 3 and 4 respectively. We present the Black-Scholes options pricing formula for the evaluation of stock options, based on which the proposed model of work is presented, which in turn is presented in the following chapters. Chapter 5 analyzes market scenarios based on the need to use and exploit stock options, highlighting their importance, while in chapter 6 fundamental CDN cost planning pricing methods and policies are presented. Chapter 7 presents the proposed model on the basis of all the previously mentioned concepts. The proposed algorithm is laid out step by step and highlights the model’s differentiation compared to other models. The conversion of the algorithm into actual, scenarios of our proposed framework is presented in chapter 8, where the metrics are presented and the results of the simulated scenarios are analyzed in the context of the model’s contribution towards the goal of more efficient resource management in the area of Content Delivery Networks. Chapter 9 serves as the conclusion of the thesis and presents ideas and questions that may be the subject of further study, while at the end of the thesis references of the literature work and of all other sources that contributed to the creation of the thesis are recorded. Τhere is also a three-part annex. In the first part, examples of cost pricing policies of real-life CDN’s are presented. The second part contains the fundamental functions of the Java source code that was developed for the simulation of the proposed model. Finally, the third part contains the CDN pricing table used in the simulation