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Wyszukujesz frazę "Chlebus, Marcin" wg kryterium: Autor

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    Wyświetlanie 1-7 z 7
    Tytuł:
    One-day-ahead forecast of state of turbulence based on todays economic situation
    Autorzy:
    Chlebus, Marcin
    Powiązania:
    https://bibliotekanauki.pl/articles/22446545.pdf
    Data publikacji:
    2018
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    forecasting
    state of turbulence
    regime switching
    risk management
    risk measure
    market risk
    Opis:
    Research background: In the literature little discussion was made about predicting state of time series in daily manner. The ability to recognize the state of a time series gives, for example, an opportunity to measure the level of risk in a state of tranquility and a state of turbulence independently, which can provide more accurate measurements of the market risk in a financial institution. Purpose of the article: The aim of article is to find an appropriate tools to predict, based on today's economic situation, the state, in which time series of financial data will be tomorrow. Methods: This paper proposes an approach to predict states (states of tranquillity and turbulence) for a current portfolio in a one-day horizon. The prediction is made using 3 different models for a binary variable (Logit, Probit, Cloglog), 4 definitions of a dependent variable (1%, 5%, 10%, 20% of worst realization of returns), 3 sets of independent variables (un-transformed data, PCA analysis and factor analysis). Additionally, an optimal cut-off point analysis is performed. The evaluation of the models was based on the LR test, Hosmer-Lemeshow test, Gini coefficient analysis and CROC criterion based on the ROC curve. The analyses were performed for 43 individual shares and 5 portfolios of shares quoted on the Warsaw Stock Exchange. The study has been conducted for the period from 1 January 2006 to 31 January 2012. Findings & Value added: Six combinations of assumptions have been chosen as appropriate (any model for a binary variable, the dependent variable defined as 5% or 10% of worst realization of returns, untransformed data, 5% or 10% cut-off point respectively). Models built on these assumptions meet all the formal requirements and have a high predictive and discriminant ability to one-day-ahead forecast of state of turbulence based on today's economic situation.
    Źródło:
    Equilibrium. Quarterly Journal of Economics and Economic Policy; 2018, 13, 3; 357-389
    1689-765X
    2353-3293
    Pojawia się w:
    Equilibrium. Quarterly Journal of Economics and Economic Policy
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk
    Autorzy:
    Chlebus, Marcin
    Powiązania:
    https://bibliotekanauki.pl/articles/1357422.pdf
    Data publikacji:
    2018-12-18
    Wydawca:
    Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
    Tematy:
    value-at-risk
    state of turbulence
    GARCH
    tail distributions
    market risk
    Opis:
    In the study, the two-step EWS-GARCH models to forecast Value-at-Risk is presented. The EWS-GARCH allows different distributions of returns or Value-at-Risk forecasting models to be used in Value-at-Risk forecasting depending on a forecasted state of the financial time series. In the study EWS-GARCH with GARCH(1,1) and GARCH(1,1), with the amendment to the empirical distribution of random errors as a Value-at-Risk model in a state of tranquillity and empirical tail, exponential or Pareto distributions used to forecast Value-at-Risk in a state of turbulence were considered. The evaluation of Value-at-Risk forecasts was based on the Value-at-Risk forecasts and the analysis of loss functions. Obtained results indicate that EWS-GARCH models may improve the quality of Value-at-Risk forecasts generated using the benchmark models. However, the choice of best assumptions for the EWS-GARCH model should depend on the goals of the Value-at-Risk forecasting model. The final selection may depend on an expected level of adequacy, conservatism and costs of the model.
    Źródło:
    Central European Economic Journal; 2017, 3, 50; 1 - 25
    2543-6821
    Pojawia się w:
    Central European Economic Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Can Lognormal, Weibull or Gamma Distributions Improve the EWS-GARCH Value-at-Risk Forecasts?
    Czy zastosowanie rozkładów lognormalnego, Weibulla lub Gamma może poprawić prognozy wartości narażonej na ryzyko uzyskiwane na podstawie modeli EWS-GARCH?
    Autorzy:
    Chlebus, Marcin
    Powiązania:
    https://bibliotekanauki.pl/articles/1050535.pdf
    Data publikacji:
    2016-09-30
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    Value-at-Risk
    GARCH models
    regime switching
    forecasting
    market risk
    wartość zagrożona (Value-at-Risk)
    model GARCH
    modele zmiany stanu
    prognozowanie
    ryzyko rynkowe
    Opis:
    In the study, two-step EWS-GARCH models to forecast Value-at-Risk are analysed. The following models were considered: the EWS-GARCH models with lognormal, Weibull or Gamma distributions as a distributions in a state of turbulence, and with GARCH(1,1) or GARCH(1,1) with the amendment to empirical distribution of random error models as models used in a state of tranquillity. The evaluation of the quality of the Value-at-Risk forecasts was based on the Value-at-Risk forecasts adequacy (the excess ratio, the Kupiec test, the Christoffersen test, the asymptotic test of unconditional coverage and the backtesting criteria defined by the Basel Committee) and the analysis of loss functions (the Lopez quadratic loss function, the Abad & Benito absolute loss function, the 3rd version of Caporin loss function and the function of excessive costs). Obtained results show that the EWSGARCH models with lognormal, Weibull or Gamma distributions may compete with EWS-GARCH models with exponential and empirical distributions. The EWS-GARCH model with lognormal, Weibull or Gamma distributions are relatively less conservative, but using them is less expensive than using the other EWS-GARCH models.
    W badaniu analizie poddane zostały dwustopniowe modele EWS-GARCH służące do prognozowania wartości narażonej na ryzyko. W ramach analizy rozpatrywane były modele EWS-GARCH zakładające rozkłady lognormalny, Weibulla oraz Gamma w stanie turbulencji oraz modele GARCH(1,1) i GARCH(1,1) z poprawką na rozkład empiryczny w stanie spokoju. Ocena jakości prognoz Value-at-Risk uzyskanych na podstawie wspomnianych modeli została przeprowadzona na podstawie miar adekwatności (wskaźnik przekroczeń, test Kupca, test Christoffersena, test asymptotyczny bezwarunkowego pokrycia oraz kryteria backtestingu określone przez Komitet Bazylejski) oraz analizy funkcji strat (kwadratowa funkcja straty Lopeza, absolutna funkcja straty Abad i Benito, 3 wersja funkcji straty Caporina oraz funkcja nadmiernych kosztów). Uzyskane wyniki wskazują, że modele EWS-GARCH z rozkładem lognormalnym, Weibulla lub Gamma mogą konkurować z modelami EWS-GARCH z rozkładem wykładniczym lub empirycznym. Modele EWS-GARCH z rozkładem lognormalnym, Weibulla lub Gamma są nieco mniej konserwatywne, jednocześnie jednak koszt ich stosowania jest mniejszy niż modeli EWS-GARCH z rozkładem wykładniczym lub empirycznym.
    Źródło:
    Przegląd Statystyczny; 2016, 63, 3; 329-350
    0033-2372
    Pojawia się w:
    Przegląd Statystyczny
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Combining forecasts? Keep it simple
    Autorzy:
    Lis, Szymon
    Chlebus, Marcin
    Powiązania:
    https://bibliotekanauki.pl/articles/22443122.pdf
    Data publikacji:
    2023-10-31
    Wydawca:
    Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
    Tematy:
    Machine learning
    GARCH model
    combined forecasts
    commodities
    VaR
    Opis:
    This study contrasts GARCH models with diverse combined forecast techniques for Commodities Value at Risk (VaR)modeling, aiming to enhance accuracy and provide novel insights. Employing daily returns data from 2000 to 2020 forgold, silver, oil, gas, and copper, various combination methods are evaluated using the Model Confidence Set (MCS) procedure. Results show individual models excel in forecasting VaR at a 0.975 confidence level, while combined methods outperform at 0.99 confidence. Especially during high uncertainty, as during COVID-19, combined forecasts prove more effective. Surprisingly, simple methods such as mean or lowest VaR yield optimal results, highlighting their efficacy. This study contributes by offering a broad comparison of forecasting methods, covering a substantial period, and dissecting crisis and prosperity phases. This advances understanding in financial forecasting, benefiting both academia and practitioners.
    Źródło:
    Central European Economic Journal; 2023, 10, 57; 343-370
    2543-6821
    Pojawia się w:
    Central European Economic Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of Block Maxima and Peaks Over Threshold Value-at-Risk models for market risk in various economic conditions
    Autorzy:
    Szubzda, Filip
    Chlebus, Marcin
    Powiązania:
    https://bibliotekanauki.pl/articles/1356736.pdf
    Data publikacji:
    2020-03-20
    Wydawca:
    Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
    Tematy:
    Value-at-Risk
    extreme value theory
    forecasting
    market risk
    Opis:
    The aim of the presented study was to assess the quality of VaR forecasts in various states of the economic situation. Two approaches based on the extreme value theory were compared: Block Maxima and the Peaks Over Threshold. Forecasts were made on the daily closing prices of 10 major indices in European countries, divided into two groups: emerging countries (Bulgaria, Czech Republic, Lithuania, Latvia, Poland, Slovakia and Hungary) and developed countries (England, France and Germany). Three states of economic situation were analysed: the pre-crisis (2007), the crisis (2008) and the post-crisis (2009) period as out-of-sample. The main conclusion obtained is the too slow process of adapting static EVT-based forecasts to market movements. While in the pre-crisis period the results were satisfactory, in the period of crisis VaR forecasts were too often exceeded.
    Źródło:
    Central European Economic Journal; 2019, 6, 53; 70 - 85
    2543-6821
    Pojawia się w:
    Central European Economic Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ridesharing in the Polish Experience: A Study using Unified Theory of Acceptance and Use of Technology
    Autorzy:
    Cylwik, Stefan
    Gabryelczak, Renata
    Chlebus, Marcin
    Powiązania:
    https://bibliotekanauki.pl/articles/1356276.pdf
    Data publikacji:
    2020-12-18
    Wydawca:
    Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
    Tematy:
    sharing economy
    ridesharing
    vanpooling
    Technology Acceptance Model
    Unified Theory of Acceptance and Use of Technology
    Opis:
    The main aim of this article is to examine the factors that influence the acceptance of ridesharing technologies in Polish society, including dynamic vanpooling on demand. The study was conducted using the UTAUT 2 model (Theory of Acceptance and Use of Technology). We have employed statistical and econometric data analyses such as factor analysis and linear regression using the Partial Least Square (PLS) method. Based on the review of the publications on ridesharing in the context of sharing economy, we have modified the UTAUT 2 model by supplementing it with the trust factor, which is a significant contribution to the development of this theory when applied to the acceptance of ridesharing technologies. Further, the outcomes allowed us to identify the factors that influence people's attitudes in using shared-ride technology (performance expectancy, hedonistic motivation and habit) and the intention to use this technology (effort expectancy, performance expectancy, price value, habit and trust). This study has practical implications as it has helped identify the factors that affect the acceptance of ridesharing technologies in Poland and these factors are significant for the suppliers of these technologies. The findings can certainly become a starting point for further research on other communities and the application of other models of technology.
    Źródło:
    Central European Economic Journal; 2020, 7, 54; 279 - 299
    2543-6821
    Pojawia się w:
    Central European Economic Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nvidias Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem
    Autorzy:
    Chlebus, Marcin
    Dyczko, Michał
    Woźniak, Michał
    Powiązania:
    https://bibliotekanauki.pl/articles/1356517.pdf
    Data publikacji:
    2021-01-29
    Wydawca:
    Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
    Tematy:
    machine learning
    nvidia
    stock returns
    technical analysis
    fundamental analysis
    Opis:
    Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.
    Źródło:
    Central European Economic Journal; 2021, 8, 55; 44 - 62
    2543-6821
    Pojawia się w:
    Central European Economic Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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