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Publications (38)
Motivation: Real estate valuation is critical for various market participants and is influenced by numerous factors such as geographic location, property characteristics, and market trends. Traditional valuation methods often struggle to handle the complexity, scale, and dynamic nature of real estate markets. Automated valuation models (AVMs) have...
The banking sector is increasingly recognising the need to implement robo-advisory. The introduction of this service may lead to increased efficiency of banks, improved quality of customer service, and a strengthened image of banks as innovative institutions. Robo-advisory uses data relating to customers, their behaviors and preferences obtained by...
Research background: Robo-advisory is a modern and rapidly developing area of implementing artificial intelligence to support customer decision-making. The current significance of robo-advisory to the financial sector is minor or marginal, and boils down to formulating recommendations and implementing investment strategies. However, the ongoing dig...
Forecasting cryptocurrency volatility can help investors make better-informed investment decisions in order to minimize risks and maximize potential profits. Accurate forecasting of cryptocurrency price fluctuations is crucial for effective portfolio management and contributes to the stability of the financial system by identifying potential threat...
This paper presents a comprehensive study of statistical and machine learning methods for predicting daily and weekly volatility of the following four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Monero. Several methods, i.e., HAR, ARFIMA, GARCH, LASSO, ridge regression, SVR, MLP, fuzzy neighbourhood model, random forest, and LSTM, are compar...
Skuteczne prognozowanie procesów ekonomicznych i finansowych ma fundamentalne znaczenie dla uczestników i analityków życia gospodarczego. Jednocześnie jednak można wskazać wiele obszarów, gdzie wciąż brakuje narzędzi prognostycznych, dających wyniki o zadowalającej trafności. Z tego względu trwają nieustanne prace nad nowymi koncepcjami metodologic...
The relationships between crude oil prices and exchange rates have always been of interest to academics and policy analysts. There are theoretical transmission channels that justify such links; however, the empirical evidence is not clear. Most of the studies on causal relationships in this area have been restricted to a linear framework, which can...
The COVID-19 pandemic seems to be the most important phenomenon observed from March 2020 in virtually all countries of the world. The necessity to prevent the spread of COVID-19 and keep health care systems efficient resulted in the forced, drastic limitation of economic activity. Many service sectors were hit particularly hard with this but indust...
Support vector regression is a promising method for time-series prediction, as it has good generalisability and an overall stable behaviour. Recent studies have shown that it can describe the dynamic characteristics of financial processes and make more accurate forecasts than other machine learning techniques. The first main contribution of this pa...
We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one...
Poster regarding: Fałdziński, M.; Fiszeder, P.; Orzeszko, W. Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression. Energies 2021, 14, 6. https://doi.org/10.3390/en14010006
In the literature concerning stock exchanges authors often refer to the concept of market development. At the same time, in most cases they do not provide a precise definition of this term and measures for distinguishing between developed and emerging markets. In fact, there is no single criterion that can be used to measure and assess stock market...
https://wydawnictwo.umk.pl/pl/products/5427/wybrane-zastosowania-badan-operacyjnych-w-finansach
https://wydawnictwo.umk.pl/pl/products/5432/uczenie-maszynowe-w-podejmowaniu-decyzji-prognostycznych
Country-specific determinants of intra-industry trade between the Czech Republic and its European Union trading partners in pharmaceutical products in 2004–2017 were evaluated. The Bayesian Model Averaging (BMA) method was used, where the bilateral intra-industry index, calcd. according to the Grubel-Lloyd formula, was adopted as the explained vari...
Nonparametric regression is an alternative to the parametric approach, which consists of applying parametric models, i.e. models of the certain functional form with a fixed number of parameters. As opposed to the parametric approach, nonparametric models have a general form, which can be approximated increasingly precisely when the sample size grow...
Linear and nonlinear Granger causality between three grains: corn, soybean, wheat and two livestock commodities: live cattle and lean hogs, was verified. Weak evidence of linear causal relationships was found, supporting the results published in other studies. However, strong nonlinear causal relationships between grain and livestock returns were f...
Celem pracy jest ocena wybranych jądrowych estymatorów funkcji regresji jako narzędzi prognozowania indeksu WIG. Prognozie poddano dwa szeregi czasowe: logarytmiczne stopy zmian indeksu oraz ich kwadraty. W badaniu zastosowano cztery metody prognozowania: estymator Nadarai-Watsona, lokalną jądrową regresję liniową oraz - dla porównania - model regr...
The aim of the paper is to assess the usefulness of the selected kernel smoothers to predict the Warsaw Stock Exchange WIG Index. Two time series were analysed: log returns and squares of log returns. The four following forecasting methods were applied in the research: the Nadaraya-Watson kernel estimator, the local-linear kernel estimator and, for...
Regresja nieparametryczna stanowi obiecujące, lecz jednocześnie wciąż niedoceniane podejście do modelowania finansowych i ekonomicznych szeregów czasowych. Polega ona na konstrukcji modeli nieparametrycznych, w których zależność pomiędzy zmiennymi nie jest wyrażona w postaci funkcji o określonej postaci analitycznej lub parametry charakteryzujące t...
Serial independence is tested using two measures of the effects of noise reduction in chaotic data, proposed by Orzeszko (2005). The extensive Monte Carlo simulations on the size and power of the new permutation-based tests are performed. Four popular nonparametric tests for serial independence are employed as a benchmark. The conducted simulations...
Nieliniowa analiza szeregów czasowych jest obiecującą i szybko rozwijającą się dziedziną ekonometrii. Wśród narzędzi stosowanych do analizy nieliniowości coraz większe uznanie zyskują metody nieparametryczne. Metody te nie wymagają apriorycznego określenia rodzaju nieliniowych zależności obecnych w danych, cechują się zwykle większą uniwersalnością...
The BDS test is one of the most important and most commonly used tools for detection of nonlinearity in time series. In the paper, the size of the BDS test is assessed using Monte Carlo simulations. The simulation uses pseudo-random series of different length, generated from seven distributions with different properties. In the research, the approx...
Streszczenie
Naturalnym uogólnieniem współczynnika korelacji liniowej Pearsona jest współczynnik informacji wzajemnej. Współczynnik ten umożliwia pomiar zależności różnego typu, również o charakterze nieliniowym. Podobnie jak współczynnik korelacji liniowej Pearsona, może być zastosowany do pojedynczego szeregu czasowego w celu identyfikacji autoza...
The iid property of the model's residuals is a crucial criterion for assessing the fit of the model to the data. GARCH-class models are the most commonly used nonlinear models in financial econometrics. In this paper various uni- and multivariate GARCHclass models were applied to selected Polish financial series. In the research the iid property of...
A presence of a noise is typical for real-world data. In order to avoid its negative impact on methods of time series analysis, noise reduction procedures may be used. The achieved results of an application of such procedures in identification of chaos or nonlinearity seem to be encouraging. One of the noise reduction methods is the Schreiber metho...
A presence of a noise is typical for real-world data. In order to avoid its negative impact on methods of time series analysis, noise reduction procedures may be used. The achieved results of an application of such procedures in identification of chaos or nonlinearity seem to be encouraging. It has been shown that noise reduction methods are able t...
W artykule scharakteryzowano wymiar fraktalny jako miarę ryzyka inwestowania w papiery wartościowe. Przedstawiono dwie metody obliczania wymiaru fraktalnego szeregu czasowego – analizę R/S oraz metodę segmentowo-wariacyjną, które następnie zastosowano do indeksów Giełdy Papierów Wartościowych w Warszawie.
Construction, estimation and application of the mutual information measure have been presented in this paper. The simulations have been carried out to verify its usefulness to detect nonlinear serial dependencies. Moreover, the mutual information measure has been applied to the indices and the sector sub-indices of the Warsaw Stock Exchange.
A concept of fractal dimension as a measure of risk in securities trading is presented in this paper. The two methods of calculating fractal dimension of time series – R/S analysis and segment-variation method are described and applied to indices of the Warsaw Stock Exchange.
W artykule scharakteryzowano konstrukcje, estymacje oraz mozliwości zastosowania wspolczynnika informacji wzajemnej. Przedstawiono wyniki symulacji, prowadzących do weryfikacji jego przydatności w procesie identyfikacji zalezności nieliniowych w szeregach czasowych. Ponadto zaprezentowano wyniki zastosowania tego wspolczynnika do analizy indeksow G...
The presence of a noise, which is typical for real data, makes methods of chaotic signals analysis much more difficult to apply to. That is why algorithms of noise reduction in chaotic time series have been recently developed. A lot of existing algorithms require setting values of specified parameters and in consequence lead to many outputs. Thus o...
