2,682 research outputs found

    Analysing behavioural factors that impact financial stock returns. The case of COVID-19 pandemic in the financial markets.

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    This thesis represents a pivotal advancement in the realm of behavioural finance, seamlessly integrating both classical and state-of-the-art models. It navigates the performance and applicability of the Irrational Fractional Brownian Motion (IFBM) model, while also delving into the propagation of investor sentiment, emphasizing the indispensable role of hands-on experiences in understanding, applying, and refining complex financial models. Financial markets, characterized by ’fat tails’ in price change distributions, often challenge traditional models such as the Geometric Brownian Motion (GBM). Addressing this, the research pivots towards the Irrational Fractional Brownian Motion Model (IFBM), a groundbreaking model initially proposed by (Dhesi and Ausloos, 2016) and further enriched in (Dhesi et al., 2019). This model, tailored to encapsulate the ’fat tail’ behaviour in asset returns, serves as the linchpin for the first chapter of this thesis. Under the insightful guidance of Gurjeet Dhesi, a co-author of the IFBM model, we delved into its intricacies and practical applications. The first chapter aspires to evaluate the IFBM’s performance in real-world scenarios, enhancing its methodological robustness. To achieve this, a tailored algorithm was crafted for its rigorous testing, alongside the application of a modified Chi-square test for stability assessment. Furthermore, the deployment of Shannon’s entropy, from an information theory perspective, offers a nuanced understanding of the model. The S&P500 data is wielded as an empirical testing bed, reflecting real-world financial market dynamics. Upon confirming the model’s robustness, the IFBM is then applied to FTSE data during the tumultuous COVID-19 phase. This period, marked by extraordinary market oscillations, serves as an ideal backdrop to assess the IFBM’s capability in tracking extreme market shifts. Transitioning to the second chapter, the focus shifts to the potentially influential realm of investor sentiment, seen as one of the many factors contributing to fat tails’ presence in return distributions. Building on insights from (Baker and Wurgler, 2007), we examine the potential impact of political speeches and daily briefings from 10 Downing Street during the COVID-19 crisis on market sentiment. Recognizing the profound market impact of such communications, the chapter seeks correlations between these briefings and market fluctuations. Employing advanced Natural Language Processing (NLP) techniques, this chapter harnesses the power of the Bidirectional Encoder Representations from Transformers (BERT) algorithm (Devlin et al., 2018) to extract sentiment from governmental communications. By comparing the derived sentiment scores with stock market indices’ performance metrics, potential relationships between public communications and market trajectories are unveiled. This approach represents a melding of traditional finance theory with state-of-the-art machine learning techniques, offering a fresh lens through which the dynamics of market behaviour can be understood in the context of external communications. In conclusion, this thesis provides an intricate examination of the IFBM model’s performance and the influence of investor sentiment, especially under crisis conditions. This exploration not only advances the discourse in behavioural finance but also underscores the pivotal role of sophisticated models in understanding and predicting market trajectories

    Air Pollution and Economic Development in KSA: Environmental Kuznets Curve and Implications for Sustainable Development

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    In a global context increasingly concerned about climate hazards, understanding the impact of economic growth on the environment is becoming crucial, especially for developing countries. This paper addresses this issue through the lens of carbon dioxide (CO2) emissions and examines how economic growth affects the environment in the case of KSA. The objective of this study is to test the validity of the environmental Kuznets curve over the period 1990-2020 using the Granger causality test. The results of the test indicate a long-term relationship between the variables, and the results of the long-term model confirm the environmental Kuznets curve hypothesis for the Saudi economy with a GDP per capita turning point of US$ 19977,794. The results suggest that there is a bi-directional, short-term causal relationship. The long-term causality is unidirectional from income to CO2 emissions. Our results suggest that the KSA should distinguish between economic policy and environmental policy to improve its environmental quality. Economic expansion leads to an increase in the level of pollution and the concentration of GHGs in the atmosphere if the government does not take the necessary precautions

    Downscaling Epidemiological Time Series Data for Improving Forecasting Accuracy: An Algorithmic Approach

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    Data scarcity and discontinuity are common occurrences in the healthcare and epidemiological dataset and often need help in forming an educative decision and forecasting the upcoming scenario. Often, these data are stored as monthly/yearly aggregate where the prevalent forecasting tools like Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and TBATS often fail to provide satisfactory results. Artificial data synthesis methods have been proven to be a powerful tool for tackling these challenges. The paper aims to propose a downscaling data algorithm based on the underlying distribution. Our findings show that the synthesized data is in agreement with the original data in terms of trend, seasonality, and residuals, and the synthesized data provides a stable foothold for the forecasting tools to generate a much more accurate forecast of the situation.Comment: 33 page

    Uncovering gravitational-wave backgrounds from noises of unknown shape with LISA

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    Detecting stochastic background radiation of cosmological origin is an exciting possibility for current and future gravitational-wave (GW) detectors. However, distinguishing it from other stochastic processes, such as instrumental noise and astrophysical backgrounds, is challenging. It is even more delicate for the space-based GW observatory LISA since it cannot correlate its observations with other detectors, unlike today's terrestrial network. Nonetheless, with multiple measurements across the constellation and high accuracy in the noise level, detection is still possible. In the context of GW background detection, previous studies have assumed that instrumental noise has a known, possibly parameterized, spectral shape. To make our analysis robust against imperfect knowledge of the instrumental noise, we challenge this crucial assumption and assume that the single-link interferometric noises have an arbitrary and unknown spectrum. We investigate possible ways of separating instrumental and GW contributions by using realistic LISA data simulations with time-varying arms and second-generation time-delay interferometry. By fitting a generic spline model to the interferometer noise and a power-law template to the signal, we can detect GW stochastic backgrounds up to energy density levels comparable with fixed-shape models. We also demonstrate that we can probe a region of the GW background parameter space that today's detectors cannot access

    Essays on noncausal and noninvertible time series

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    Over the last two decades, there has been growing interest among economists in nonfundamental univariate processes, generally represented by noncausal and non-invertible time series. These processes have become increasingly popular due to their ability to capture nonlinear dynamics such as volatility clustering, asymmetric cycles, and local explosiveness - all of which are commonly observed in Macroeconomics and Finance. In particular, the incorporation of both past and future components into noncausal and noninvertible processes makes them attractive options for modeling forward-looking behavior in economic activities. However, the classical techniques used for analyzing time series models are largely limited to causal and invertible counterparts. This dissertation seeks to contribute to the field by providing theoretical tools robust to noncausal and noninvertible time series in testing and estimation. In the first chapter, "Quantile Autoregression-Based Non-causality Testing", we investigate the statistical properties of empirical conditional quantiles of non-causal processes. Specifically, we show that the quantile autoregression (QAR) estimates for non-causal processes do not remain constant across different quantiles in contrast to their causal counterparts. Furthermore, we demonstrate that non-causal autoregressive processes admit nonlinear representations for conditional quantiles given past observations. Exploiting these properties, we propose three novel testing strategies of non-causality for non-Gaussian processes within the QAR framework. The tests are constructed either by verifying the constancy of the slope coefficients or by applying a misspecification test of the linear QAR model over different quantiles of the process. Some numerical experiments are included to examine the finite sample performance of the testing strategies, where we compare different specification tests for dynamic quantiles with the Kolmogorov-Smirnov constancy test. The new methodology is applied to some time series from financial markets to investigate the presence of speculative bubbles. The extension of the approach based on the specification tests to AR processes driven by innovations with heteroskedasticity is studied through simulations. The performance of QAR estimates of non-causal processes at extreme quantiles is also explored. In the second chapter, "Estimation of Time Series Models Using the Empirical Distribution of Residuals", we introduce a novel estimation technique for general linear time series models, potentially noninvertible and noncausal, by utilizing the empirical cumulative distribution function of residuals. The proposed method relies on the generalized spectral cumulative function to characterize the pairwise dependence of residuals at all lags. Model identification can be achieved by exploiting the information in the joint distribution of residuals under the iid assumption. This method yields consistent estimates of the model parameters without imposing stringent conditions on the higher-order moments or any distributional assumptions on the innovations beyond non-Gaussianity. We investigate the asymptotic distribution of the estimates by employing a smoothed cumulative distribution function to approximate the indicator function, considering the non-differentiability of the original loss function. Efficiency improvements can be achieved by properly choosing the scaling parameter for residuals. Finite sample properties are explored through Monte Carlo simulations. An empirical application to illustrate this methodology is provided by fitting the daily trading volume of Microsoft stock by autoregressive models with noncausal representation. The flexibility of the cumulative distribution function permits the proposed method to be extended to more general dependence structures where innovations are only conditional mean or quantile independent. In the third chapter, "Directional Predictability Tests", joint with Carlos Velasco, we propose new tests of predictability for non-Gaussian sequences that may display general nonlinear dependence in higher-order properties. We test the null of martingale difference against parametric alternatives which can introduce linear or nonlinear dependence as generated by ARMA and all-pass restricted ARMA models, respectively. We also develop tests to check for linear predictability under the white noise null hypothesis parameterized by an all-pass model driven by martingale difference innovations and tests of non-linear predictability on ARMA residuals. Our Lagrange Multiplier tests are developed from a loss function based on pairwise dependence measures that identify the predictability of levels. We provide asymptotic and finite sample analysis of the properties of the new tests and investigate the predictability of different series of financial returns.This thesis has been possible thanks to the financial support from the grant BES-2017-082695 from the Ministerio de Economía Industria y Competitividad.Programa de Doctorado en Economía por la Universidad Carlos III de MadridPresidente: Miguel ángel Delgado González.- Secretario: Manuel Domínguez Toribio.- Vocal: Majid M. Al Sadoo

    Asymptotics of stochastic learning in structured networks

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    Non-Intrusive Disaggregation of Advanced Metering Infrastructure Signals for Demand-Side Management

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    As intermittent renewable energy generation resources become more prevalent, innovative ways to manage the electric grid are sought. In the past, much of the grid balancing effort has been focused on the supply side or on demand-side management of large commercial or industrial electricity customers. Today, with the increase in enabling technologies such as Internet-connected appliances, home energy management systems, and advanced metering infrastructure (AMI) smart meters, residential demand-side management is also a possibility. For a utility to assess the potential capacity of residential demand-side flexibility, power data from controllable appliances from a large sample of houses is required. These data may be collected by installing time- and cost-intensive monitoring equipment at every site, or, alternatively, by disaggregating the signals communicated to the utility by AMI meters. In this study, non-intrusive load monitoring algorithms are used to disaggregate low-resolution real power signals from AMI smart meters. Disaggregation results using both supervised and unsupervised versions of a graph signal processing (GSP) -based algorithm are presented. The effects of varying key parameters in each GSP algorithm, including scaling factor, sequence, and classifier threshold are also presented, and limitations of the algorithm based on energy use patterns are discussed. FM values greater than 0.8 were achieved for the electric resistance water heater and electric vehicle charger using the unsupervised GSP algorithm. The disaggregated signals are then used to develop energy forecasting models for predicting the load of controllable appliances over a given demand response period. ARIMA, SVR, and LSTM forecasting methods were evaluated and compared to a baseline model developed using the mean hourly power draw values. The minimum MAAPE was achieved for the water heater, with an approximate range of 10 < MAAPE < 20. The total energy flexibility of each appliance and the associated uncertainty of the combined disaggregation and forecast are characterized to assess the feasibility of this approach for demand-side management applications. The framework presented in this study may be used to characterize the ability of signals to be disaggregated from a larger dataset of AMI data, based on the whole-house signal characteristics. This analysis can aid grid managers in assessing the viability of selected devices, such as the water heater, for demand response activities.Ph.D

    Limit Theory under Network Dependence and Nonstationarity

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    These lecture notes represent supplementary material for a short course on time series econometrics and network econometrics. We give emphasis on limit theory for time series regression models as well as the use of the local-to-unity parametrization when modeling time series nonstationarity. Moreover, we present various non-asymptotic theory results for moderate deviation principles when considering the eigenvalues of covariance matrices as well as asymptotics for unit root moderate deviations in nonstationary autoregressive processes. Although not all applications from the literature are covered we also discuss some open problems in the time series and network econometrics literature.Comment: arXiv admin note: text overlap with arXiv:1705.08413 by other author

    Essays on Portfolio Risk Management and Weather Derivatives

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    Denne avhandlingen handler om utvikling og praktisk implementering av risikostyringsmetoder for investeringsporteføljer, energiporteføljer, og håndtering av vær- og forurensningsrisiko. Avhandlingen inkluderer tre vitenskapelige artikler som hver tar for seg ulike aspekter av finansiell risikostyring. Den første fokuserer på metoder for aktivaallokering når det eksisterer asymmetrisk avhengighet mellom avkastningene for eiendelene i en investeringsportefølje. Den andre artikkelen omhandler energiprisrisikostyring, og introduserer et åpen kildekodeverktøy for energiporteføljeforvaltning som er utviklet som en del av doktorgradsprosjektet. Den siste artikkelen presenterer et teoretisk rammeverk for håndtering av forurensningsrisiko ved hjelp av finansielle derivatkontrakter, som bygger på den eksisterende teorien om værderivater. Disse arbeidene bidrar alle til det overordnede temaet for avhandlingen, som er utvikling av risikostyringsmetoder for ulike typer porteføljer og utforskingen av rollen til finansielle derivater i håndtering av risiko knyttet til markedspriser, vær og forurensning. For å sette bidragene inn i en teoretisk kontekst har vi inkludert et kort kapittel som presenterer alternative metoder for avhengighetsmodellering, og hvordan disse kan utnyttes når man forvalter investeringsporteføljer. Ett av disse målene, lokal gaussisk korrelasjon, brukes til å utvide det klassiske mean-variance-rammeverket for aktivaallokering i den første artikkelen. Deretter følger et kort introduksjonskapittel til spot- og forwardmarkeder for energi. Hovedfokuset her er råvareprisrisiko, og hvordan denne kan håndteres med finansielle derivatkontrakter. Vi demonstrerer hvordan forvaltning av energiporteføljer kan gjennomføres med vårt åpen kildekodeverktøy ved bruk av data fra det europeiske kraftmarkedet. Til slutt inkluderes et kapittel om værderivater. Dette inneholder en introduksjon til værrelatert risiko, en kort introduksjon til værmarkedet, vanlige kontraktstyper og alternative metoder for prising. For å sikre reproduserbarhet har vi også lagt til et kapittel om programkode. Her finnes lenker til Git-repositorier med alle data og R-kode for å gjennomføre analysene som presenteres i avhandlingen.This thesis is concerned with the development and practical implementation of risk management methods for investment portfolios, energy portfolios, and weather and pollution risk. The thesis includes three scientific papers that each address different aspects of financial risk management. The first paper focuses on portfolio allocation in the presence of asymmetric dependence between asset returns. The second paper examines energy price risk management, and introduces an open source toolkit for energy portfolio management which has been developed as a part of the PhD project. The final paper present a theoretical framework for managing pollution risk using financial derivatives contracts, which builds upon the existing theory of weather derivatives. These papers all contribute to the overall theme, which is the development of risk management methods for various types of portfolios and the exploration of the role of financial derivatives in managing risks related to market prices, weather and pollution. In order to provide a theoretical context, we have included a brief chapter exploring alternative methods for dependence modelling and how these may be utilized when managing investment portfolios. One of these measures, the local Gaussian correlation, is used to extend the classical mean-variance framework for asset allocation in the first paper. Thereafter, a short introduction to spot and forward energy markets is provided. The primary focus here is commodity market price risk, and how this can be managed with financial derivatives contracts. We demonstrate how portfolio management may be performed with our open source toolkit using European energy market data. Finally, we include a chapter on weather derivatives. This contains a introduction to weather related risk, a brief introduction to the weather markets, frequently used contract types and pricing methods. To ensure reproducibility, we have also added a chapter on computer code, where the interested reader may find links to Git repositories with all data and the R code needed to run the analysis presented in the thesis.Doktorgradsavhandlin

    Study of Climate Variability Patterns at Different Scales – A Complex Network Approach

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    Das Klimasystem der Erde besteht aus zahlreichen interagierenden Teilsystemen, die sich über verschiedene Zeitskalen hinweg verändern, was zu einer äußerst komplizierten räumlich-zeitlichen Klimavariabilität führt. Das Verständnis von Prozessen, die auf verschiedenen räumlichen und zeitlichen Skalen ablaufen, ist ein entscheidender Aspekt bei der numerischen Wettervorhersage. Die Variabilität des Klimas, ein sich selbst konstituierendes System, scheint in Mustern auf großen Skalen organisiert zu sein. Die Verwendung von Klimanetzwerken hat sich als erfolgreicher Ansatz für die Erkennung der räumlichen Ausbreitung dieser großräumigen Muster in der Variabilität des Klimasystems erwiesen. In dieser Arbeit wird mit Hilfe von Klimanetzwerken gezeigt, dass die Klimavariabilität nicht nur auf größeren Skalen (Asiatischer Sommermonsun, El Niño/Southern Oscillation), sondern auch auf kleineren Skalen, z.B. auf Wetterzeitskalen, in Mustern organisiert ist. Dies findet Anwendung bei der Erkennung einzelner tropischer Wirbelstürme, bei der Charakterisierung binärer Wirbelsturm-Interaktionen, die zu einer vollständigen Verschmelzung führen, und bei der Untersuchung der intrasaisonalen und interannuellen Variabilität des Asiatischen Sommermonsuns. Schließlich wird die Anwendbarkeit von Klimanetzwerken zur Analyse von Vorhersagefehlern demonstriert, was für die Verbesserung von Vorhersagen von immenser Bedeutung ist. Da korrelierte Fehler durch vorhersagbare Beziehungen zwischen Fehlern verschiedener Regionen aufgrund von zugrunde liegenden systematischen oder zufälligen Prozessen auftreten können, wird gezeigt, dass Fehler-Netzwerke helfen können, die räumlich kohärenten Strukturen von Vorhersagefehlern zu untersuchen. Die Analyse der Fehler-Netzwerk-Topologie von Klimavariablen liefert ein erstes Verständnis der vorherrschenden Fehlerquelle und veranschaulicht das Potenzial von Klimanetzwerken als vielversprechendes Diagnoseinstrument zur Untersuchung von Fehlerkorrelationen.The Earth’s climate system consists of numerous interacting subsystems varying over a multitude of time scales giving rise to highly complicated spatio-temporal climate variability. Understanding processes occurring at different scales, both spatial and temporal, has been a very crucial problem in numerical weather prediction. The variability of climate, a self-constituting system, appears to be organized in patterns on large scales. The climate networks approach has been very successful in detecting the spatial propagation of these large scale patterns of variability in the climate system. In this thesis, it is demonstrated using climate network approach that climate variability is organized in patterns not only at larger scales (Asian Summer Monsoon, El Niño-Southern Oscillation) but also at shorter scales, e.g., weather time scales. This finds application in detecting individual tropical cyclones, characterizing binary cyclone interaction leading to a complete merger, and studying the intraseasonal and interannual variability of the Asian Summer Monsoon. Finally, the applicability of the climate network framework to understand forecast error properties is demonstrated, which is crucial for improvement of forecasts. As correlated errors can arise due to the presence of a predictable relationship between errors of different regions because of some underlying systematic or random process, it is shown that error networks can help to analyze the spatially coherent structures of forecast errors. The analysis of the error network topology of a climate variable provides a preliminary understanding of the dominant source of error, which shows the potential of climate networks as a very promising diagnostic tool to study error correlations
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