Partner: dr Adam Marszałek

Cracow University of Technology (PL)

Ostatnie publikacje
1.Marszałek A., Burczyński T., Modeling and forecasting financial time series with ordered fuzzy candlesticks, INFORMATION SCIENCES, ISSN: 0020-0255, DOI: 10.1016/j.ins.2014.03.026, Vol.273, pp.144-155, 2014

Streszczenie:

The goal of the paper is to present an experimental evaluation of fuzzy time series models which are based on ordered fuzzy numbers to predict financial time series. Considering this approach the financial data is modeled using Ordered Fuzzy Numbers (OFNs) called further by Ordered Fuzzy Candlesticks (OFCs). The use of them allows modeling uncertainty associated with financial data and maintaining more information about price movement at assumed time interval than comparing to commonly used price charts (e.g. Japanese Candlestick chart). Thanks to well-defined arithmetic of OFN, one can construct models of fuzzy time series, such as an Ordered Fuzzy Autoregressive Process (OFAR), where all input values are OFC, while the coefficients and output values are arbitrary OFN; in the form of classical equations, without using rule-based systems. In an empirical study ordered fuzzy autoregressive models are applied to modeling and predict price movement of futures contracts on Warsaw Stock Exchange Top 20 Index.

Słowa kluczowe:

Ordered fuzzy number, Directional predictability, Fuzzy autoregressive, Financial time series, Stock return

Afiliacje autorów:

Marszałek A.-Cracow University of Technology (PL)
Burczyński T.-IPPT PAN
45p.

Lista rozdziałów w ostatnich monografiach
1.
562
Marszałek A., Burczyński T., Theory and Applications of Ordered Fuzzy Numbers, rozdział: Ordered Fuzzy Candlesticks, Springer International Publishing, pp.183-194, 2017

Prace konferencyjne
1.Marszałek A., Burczyński T., Ordered Fuzzy GARCH Model for Volatility Forecasting, Includes the proceedings of the 10th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2017), 2017-09-11/09-15, Warszawa (PL), No.XI, pp.480-492, 2018

Streszczenie:

A volatility forecasting comparative study between the most popular original GARCH model and the same model defined based on concepts of Ordered Fuzzy Numbers and Ordered Fuzzy Candlsticks is presented. These approaches offer a suitable tool to handle both imprecision of measurements and uncertainty associated with financial data. Therefore, they are particularly useful for volatility forecasting, since the volatility is unobservable and a proxy for it is used (realised volatility). In presented study, based on intra-daily data of theWarsaw Stock Exchange Top 20 Index (WIG 20), one showed that based on the adjusted-R squared and several prediction measurements, the fuzzy approach does perform better than the original GARCH model and forecasts more precisely in both the in-sample and out-of-sample predictions

Słowa kluczowe:

Volatility forecasting, Realized volatility, Ordered fuzzy number, Kosinski’s fuzzy number, Ordered fuzzy candlestick, Ordered fuzzy GARCH model, Financial high-frequency data

Afiliacje autorów:

Marszałek A.-Cracow University of Technology (PL)
Burczyński T.-IPPT PAN
2.Marszałek A., Burczyński T., Fuzzy Portfolio Diversification with Ordered Fuzzy Numbers, 16th International Conference, ICAISC 2017, 2017-06-11/06-15, Zakopane (PL), pp.279-291, 2017

Streszczenie:

In this paper, we consider a multi-objective portfolio diversification problem under real constraints in fuzzy environment, where the objective is to minimize the variance of portfolio and maximize expected return rate of portfolio. The return rates of assets are modeled using concept of Ordered Fuzzy Candlesticks, which are Ordered Fuzzy Numbers. The use of them allows modeling uncertainty associated with financial data based on high-frequency data. Thanks to well-defined arithmetic of Ordered Fuzzy Numbers, the estimators of fuzzy-valued expected value and covariance can be computed in the same way as for real random variables. In an empirical study, 20 assets included in the Warsaw Stock Exchange Top 20 Index are used to compare considered fuzzy model with crisp mean-variance model

Słowa kluczowe:

Ordered fuzzy number, Kosinski’s fuzzy number, Ordered fuzzy candlestick, Fuzzy portfolio diversification, Fuzzy returns, Multi-objective optimization, Financial high-frequency data

Afiliacje autorów:

Marszałek A.-Cracow University of Technology (PL)
Burczyński T.-IPPT PAN