Vpliv likvidnostnega tveganja na izračun tvegane vrednosti

V članku uvajamo likvidnost v standardno analizo tvegane vrednosti. Osnovne VaR modele nadgradimo z informacijami o cenovnem razponu med ponujeno in povpraševano ceno naložbe. Nadgrajene modele testiramo na podlagi domačega in tujih naborov delnic. Ugotavljamo, da likvidnostni VaR modeli ob upoštevanju predpostavk raziskave primerno ocenjujejo tržna tveganja. Le-ti metodološko na eni strani predstavljajo napredek v okviru obravnave tržnih tveganj, vendar na drugi strani rezultati testiranj modelov kažejo pomanjkanje robustnosti. Glede primerjave rezultatov po naborih delnic pa ugotavljamo, da so rezultati za slovenski nabor kljub manjši globini trga primerljivi s tistimi iz tujine.In this article we implement liquidity in the standard value-at-risk framework. We incorporate bid-ask spread into basic VaR models. We then test these models on three foreign markets and on a domestic one. We conclude that liquidity VaR models adequately measure market risk. On one hand, the liquidity VaR methodology represents advancement in market risk analysis, but on the other hand, those models are not yet robust enough to pass all back tests. Comparing the results between markets we conclude that the results for the domestic market are comparable to those of foreign ones despite their size difference

AutoML for Log File Analysis (ALFA) in a Production Line System of Systems pointed towards Predictive Maintenance

Automated machine learning and predictive maintenance have both become prominent terms in recent years. Combining these two fields of research by conducting log analysis using automated machine learning techniques to fuel predictive maintenance algorithms holds multiple advantages, especially when applied in a production line setting. This approach can be used for multiple applications in the industry, e.g., in semiconductor, automotive, metal, and many other industrial applications to improve the maintenance and production costs and quality. In this paper, we investigate the possibility to create a predictive maintenance framework using only easily available log data based on a neural network framework for predictive maintenance tasks. We outline the advantages of the ALFA (AutoML for Log File Analysis) approach, which are high efficiency in combination with a low entry border for novices, among others. In a production line setting, one would also be able to cope with concept drift and even with data of a new quality in a gradual manner. In the presented production line context, we also show the superior performance of multiple neural networks over a comprehensive neural network in practice. The proposed software architecture allows not only for the automated adaption to concept drift and even data of new quality but also gives access to the current performance of the used neural networks

The Inclusion of Liquidity Risk Into a Parametric Value-at-Risk Framework

V magistrskem delu vpeljujemo likvidnost kot dejavnik tveganja v standardno analizo tvegane vrednosti. Osnovne parametrične VaR modele nadgradimo z informacijami o cenovnem razponu med ponujeno in povpraševano ceno določene naložbe. Pri tem nestanovitnost donosov izračunavamo na dva načina, in sicer z uporabo ne–tehtane metodologije in z uporabo GARCH metodologije. Rezultate nadgrajenih modelov testiramo na podlagi štirih naborov delnic: treh tujih in slovenskega. Ugotavljamo, da likvidnostni VaR modeli, ob upoštevanju predpostavk raziskave, primerno ocenjujejo tržna tveganja in da se v primerjavi s klasičnimi VaR modeli izkažejo za bolj ustrezne. Do takšnih ugotovitev pridemo pri uporabi obeh metodologij za izračun nestanovitnosti donosov. Ob primerjavi rezultatov modelov po obeh metodologijah med sabo pa se kot primernejši izkažejo likvidnostni VaR modeli, izračunani na podlagi GARCH metodologije. Metodološko na eni strani Likvidnostni VaR modeli predstavljajo napredek v okviru obravnave tržnih tveganj, vendar na drugi strani rezultati testiranj modelov kažejo, da slednji še niso dovolj robustni, da bi zadovoljili vsem statističnim testom. Razlog za to je v večji podatkovni zahtevnosti likvidnostnih VaR modelov. Glede primerjave rezultatov po naborih delnic pa ugotavljamo, da so rezultati za slovenski nabor, kljub manjši globini trga, primerljivi s tistimi iz tujine.In this masters thesis we implement liquidity as a risk factor in the standard value-at-risk framework. We incorporate bid-ask spread information into the basic VaR models. In the models we calculate the volatility of returns in two ways: with the use of non–weighted methodology and with the use of GARCH methodology. We then test these models on four different markets: on three foreign and on a domestic one. We conclude that based on assumptions of our research, liquidity VaR models adequately measure market risk and even prove themselves superior to ordinary VaR models. We conclude this for all the models, based on both types of volatility methodologies. In comparison between the models based on different volatility methodologies we conclude that those using the GARCH methodology prove themselves superior to those using non–weighted methodology. On one hand the liquidity VaR models represent a step in the right direction in market risk analysis but on the other hand those models are not yet robust enough to pass all backtests. Reason for this is that liquidity VaR models have a higher demand for input data quality and quantity wise. About the comparison of results between markets we conclude that the results for the domestic market are comparable to those of foreign ones despite the domestic one being smaller

Spread and Liquidity Issues: A markets comparison

The financial crises are closely connected with spread changes and liquidity issues. After defining and addressing spread considerations, we research in this paper the topic of liquidity issues in times of economic crisis. We analyse the liquidity effects as recorded on spreads of securities from different markets. We stipulate that higher international risk aversion in times of financial crises coincides with widening security spreads. The paper then introduces liquidity as a risk factor into the standard value-at-risk framework, using GARCH methodology. The comparison of results of these models suggests that the size of the tested markets does not have a strong effect on the models. Thus, we find that spread analysis is an appropriate tool for analysing liquidity issues during a financial crisis

A Knowledge Graph-Based Data Integration Framework Applied to Battery Data Management

Today, the automotive and transportation sector is undergoing a transformation process to meet the requirements of sustainable and efficient operations. This transformation mainly reveals itself by electric vehicles, hybrid electric vehicles, and electric vehicle sharing. One significant, and the most expensive, component in electric vehicles is the batteries, and the management of batteries is crucial. It is essential to perform constant monitoring of behavior changes for operational purposes and quickly adjust components and operations to these changes. Thus, to address these challenges, we propose a knowledge graph-based data integration framework for simplifying access and analysis of data accumulated through the operations of vehicles and related transportation systems. The proposed framework aims to enable the effortless analysis and navigation of integrated knowledge and the creation of additional data sets from this knowledge to use during the application of data analysis and machine learning. The knowledge graph serves as a significant component to simplify the extraction, enrichment, exploration, and generation of data in this framework. We have developed it according to the human-centered design, and various roles of the data science and machine learning life cycle can use it. Its main objective is to streamline the exploration and interaction with the integrated data to maximize human productivity. Finally, we present a battery use case to show the feasibility and benefits of the proposed framework. The use case illustrates the usage of the framework to extract knowledge from raw data, navigate and enrich it with additional knowledge, and generate data sets