Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

Results found: 3

first rewind previous Page / 1 next fast forward last

Search results

help Sort By:

help Limit search:
first rewind previous Page / 1 next fast forward last
1
Publication available in full text mode
Content available

Forecasting currency covariances using machine learning tree-based algorithms with low and high prices

100%
Bejger S., Fiszeder P.
Przegląd Statystyczny
|
2021
|
vol. 68
|
issue 3
1-15
EN
We combine machine learning tree-based algorithms with the usage of low and high prices and suggest a new approach to forecasting currency covariances. We apply three algorithms: Random Forest Regression, Gradient Boosting Regression Trees and Extreme Gradient Boosting with a tree learner. We conduct an empirical evaluation of this procedure on the three most heavily traded currency pairs in the Forex market: EUR/USD, USD/JPY and GBP/USD. The forecasts of covariances formulated on the three applied algorithms are predominantly more accurate than the Dynamic Conditional Correlation model based on closing prices. The results of the analyses indicate that the GBRT algorithm is the bestperforming method.
2
Publication available in full text mode
Content available

Estymacja wariancji arytmetycznego ruchu Browna na podstawie znanych wartości minimum, maksimum, końcowej oraz dryfu

100%
Perczak G., Fiszeder P.
Przegląd Statystyczny
|
2013
|
vol. 60
|
issue 1
39-62
PL
W opracowaniu poruszony jest problem wyznaczenia wariancji stopy zwrotu instrumentu finansowego na podstawie rynkowych notowań dziennych cen otwarcia, minimalnej, maksymalnej i zamknięcia. Wykorzystując znajomość łącznego rozkładu minimum, maksimum i wartości końcowej arytmetycznego ruchu Browna dokonano analizy porównawczej znanych estymatorów wariancji. Wyznaczono formuły wartości oczekiwanych bardzo wielu funkcji zmiennych losowych, które posłużyły do konstrukcji tych estymatorów. Ponadto, na ich podstawie zaproponowano nowy estymator wariancji. Dokonano analizy założeń, które przyjęto przy konstrukcji tego estymatora. Metodami analitycznymi porównano jego efektywność z efektywnością podstawowych znanych estymatorów zmienności dziennej.
EN
This paper examines the problem of calculating the variance of returns of a financial instrument which is based upon the historical opening, closing, high, and low prices. For this purpose, the knowledge of the joint distribution of minimum, maximum and final values of arithmetic Brownian motion was used. It gave a possibility to make a comparative analysis of the variance estimators. The formulae of expected values of many random variables, which were used for the construction of these estimators were calculated. Moreover, on the basis of those formulae, the new estimator of variance was proposed. The assumptions that were adopted for the construction of the estimator were examined. The efficiency of the proposed estimator was compared with the efficiency of the well-known estimators of daily volatility.
3
Publication available in full text mode
Content available

Forecasting volatility during the outbreak of Russian invasion of Ukraine: application to commodities, stock indices, currencies, and cryptocurrencies

100%
Fiszeder P., Małecka M.
Equilibrium. Quarterly Journal of Economics and Economic Policy
|
2022
|
vol. 17
|
issue 4
939-967
EN
Research background: The Russian invasion on Ukraine of February 24, 2022 sharply raised the volatility in commodity and financial markets. This had the adverse effect on the accuracy of volatility forecasts. The scale of negative effects of war was, however, market-specific and some markets exhibited a strong tendency to return to usual levels in a short time. Purpose of the article: We study the volatility shocks caused by the war. Our focus is on the markets highly exposed to the effects of this conflict: the stock, currency, cryptocurrency, gold, wheat and crude oil markets. We evaluate the forecasting accuracy of volatility models during the first stage of the war and compare the scale of forecast deterioration among the examined markets. Our long-term purpose is to analyze the methods that have the potential to mitigate the effect of forecast deterioration under such circumstances. We concentrate on the methods designed to deal with outliers and periods of extreme volatility, but, so far, have not been investigated empirically under the conditions of war. Methods: We use the robust methods of estimation and a modified Range-GARCH model which is based on opening, low, high and closing prices. We compare them with the standard maximum likelihood method of the classic GARCH model. Moreover, we employ the MCS (Model Confidence Set) procedure to create the set of superior models. Findings & value added: Analyzing the market specificity, we identify both some common patterns and substantial differences among the markets, which is the first comparison of this type relating to the ongoing conflict. In particular, we discover the individual nature of the cryptocurrency markets, where the reaction to the outbreak of the war was very limited and the accuracy of forecasts remained at the similar level before and after the beginning of the war. Our long-term contribution are the findings about suitability of methods that have the potential to handle the extreme volatility but have not been examined empirically under the conditions of war. We reveal that the Range-GARCH model compares favorably with the standard volatility models, even when the latter are evaluated in a robust way. It gives valuable implication for the future research connected with military conflicts, showing that in such period gains from using more market information outweigh the benefits of using robust estimators.
first rewind previous Page / 1 next fast forward last
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.