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Clustering macroeconomic time series

100%
Augustyński I., Laskoś-Grabowski P.
Econometrics. Ekonometria. Advances in Applied Data Analytics
|
2018
|
issue vol. 22 no. 2
74-88
EN
The data mining technique of time series clustering is well established. However, even when recognized as an unsupervised learning method, it does require making several design decisions that are nontrivially influenced by the nature of the data involved. By extensively testing various possibilities, we arrive at a choice of a dissimilarity measure (compression-based dissimilarity measure, or CDM) which is particularly suitable for clustering macroeconomic variables. We check that the results are stable in time and reflect large-scale phenomena, such as crises. We also successfully apply our findings to the analysis of national economies, specifically to identifying their structural relations.
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Clustering companies listed on the Warsaw Stock Exchange according to time-varying beta

100%
Szczepocki P.
Econometrics. Ekonometria. Advances in Applied Data Analytics
|
2019
|
issue vol. 23 no. 2
63-79
EN
The beta parameter is a popular tool for the evaluation of portfolio performance. The Sharpe single-index model is a simple regression model in which the stock’s returns are regressed against the returns of a broader index. The beta parameter is a measure of the strength of this relation. Extensive recent research has proved that the beta is not constant in time and should be modelled as a time-variant coefficient. One of the most popular methods of the estimation of a time-varying beta is the Kalman filter. As the output of the Kalman filter, one obtains a sequence of the estimates of a time-varying beta. This sequence shows the historical dynamics of sensitivity of a company’s returns to the variations of market returns. The article proposes a method of clustering companies listed on the Warsaw Stock Exchange according to time-varying betas.
3

Co-movement of Commodity Prices – Results from Dynamic Time Warping Classification

63%
Śmiech S.
Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie
|
2015
|
issue 4(940)
117-130
EN
Several factors are responsible for difficulties in describing the behaviour of commodity prices. Firstly, there are numerous different categories of commodities. Secondly, some categories overlap with other categories, while others indirectly compete in the market. Thirdly, although essentially commodity prices react to changes in economic conditions or exchange rates, to a large extent these prices depend on supply disturbances. However, in recent years commodity prices co-move, and researchers, beginning with Pindyck and Rotemberg (1990), have been trying to explain this phenomenon. The objective of the article is to conduct the classification of the series of commodity prices in the pre-crisis and after-crisis periods. The results of such classification will reveal whether co-movement of commodity prices is the same in both periods. The analysis is based on monthly data from the period January 2001 to February 2014. All prices and price indices are published by the World Bank. The results obtained in dynamic time warping clustering reveal that co-movement of commodity prices is more evident in the pre-crisis period. There are only several paths which determine commodity prices.
PL
Wiele czynników powoduje problemy dotyczące modelowania zachowania cen towarów na rynkach światowych. Wśród nich wymienić można rozmaitość kategorii towarów – które dodatkowo nie są rozłączne, powiązania cen towarów z różnych kategorii – część towarów może być traktowana jako komplementarne, przyczyny wahań cen towarów. Pewne przyczyny (głównie po stronie popytowej), takie jak aktywność gospodarcza, stopy procentowe czy kursy walut, są wspólne dla wielu kategorii towarów, inne z kolei – związane z podażą – są specyficzne. Mimo to w ostatnich dziesięcioleciach ceny towarów zachowują się podobnie (co-move), co doczekało się wielu opracowań. W pracy przeprowadzono i oceniono grupowanie cen towarów w okresie przed kryzysem oraz po globalnym kryzysie finansowym w celu sprawdzenia, czy ceny towarów przed kryzysem i po kryzysie grupują się w podobne skupiska i czy homogeniczność tych skupisk jest podobna. Analiza została przeprowadzona na danych miesięcznych z okresu styczeń 2001–luty 2014. Wszystkie ceny oraz indeksy cen zaczerpnięto z bazy Banku Światowego. W badaniu wykorzystano metodę dynamic time warping, dzięki której wykazano, że wspólne zachowanie cen było silniejsze w okresie przed globalnym kryzysem finansowym. Ustalono także, że liczba skupisk jest niewielka, co oznacza, że można zauważyć tylko kilka tendencji w zakresie zachowania się cen na światowych rynkach towarowych.
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