Improved two-component tests in Beta-Skew-t-EGARCH models

This work proposes a likelihood ratio test to assist in the selection of the Beta-Skew-t-EGARCH model with one or two volatility components. To improve the performance of the proposed test in small samples, the bootstrap-based likelihood ratio test and the bootstrap Bartlett correction are considered. The finite sample performance of the tests are assessed using Monte Carlo simulations. The numerical evidence favors the bootstrap-based test. The tests are applied to the DAX log-returns. The results demonstrate the practical usefulness of the proposed two-component tests

Bootstrapping the Likelihood Ratio Cointegration Test in Error Correction Models with Unknown Lag Order

We investigate the small-sample size and power properties of bootstrapped likelihood ratio systems cointegration tests via Monte Carlo simulations when the true lag order of the data generating process is unknown. A recursive bootstrap scheme is employed. We estimate the order by minimizing different information criteria. In comparison to the standard asymptotic likelihood ratio test based on an estimated lag order we found that the recursive bootstrap procedure can lead to improvements in small samples even when the true lag order is unknown while the power loss is moderate.publishedVersio

Bootstrapping the likelihood ratio cointegration test in error correction models with unknown lag order

The finite-sample size and power properties of bootstrapped likelihood ratio system cointegration tests are investigated via Monte Carlo simulations when the true lag order of the data generating process is unknown. Recursive bootstrap schemes are employed which differ in the way in which the lag order is chosen. The order is estimated by minimizing different information criteria and by combining the corresponding order estimates. It is found that, in comparison to the standard asymptotic likelihood ratio test based on an estimated lag order, bootstrapping can lead to improvements in small samples even when the true lag order is unknown, while the power loss is moderate

Bootstrapping the Likelihood Ratio Cointegration Test in Error Correction Models with Unknown Lag Order

We investigate the small-sample size and power properties of bootstrapped likelihood ratio systems cointegration tests via Monte Carlo simulations when the true lag order of the data generating process is unknown. A recursive bootstrap scheme is employed. We estimate the order by minimizing different information criteria. In comparison to the standard asymptotic likelihood ratio test based on an estimated lag order we found that the recursive bootstrap procedure can lead to improvements in small samples even when the true lag order is unknown while the power loss is moderate

Bootstrapping the likelihood ratio cointegration test in error correction models with unknown lag order

The finite-sample size and power properties of bootstrapped likelihood ratio system cointegration tests are investigated via Monte Carlo simulations when the true lag order of the data generating process is unknown. Recursive bootstrap schemes are employed which differ in the way in which the lag order is chosen. The order is estimated by minimizing different information criteria and by combining the corresponding order estimates. It is found that, in comparison to the standard asymptotic likelihood ratio test based on an estimated lag order, bootstrapping can lead to improvements in small samples even when the true lag order is unknown, while the power loss is moderate.Cointegration tests Bootstrapping Information criteria

Bootstrapping the likelihood ratio cointegration test in error correction models with unknown lag order

We investigate the small-sample size and power properties of bootstrapped likelihood ratio systems cointegration tests via Monte Carlo simulations when the true lag order of the data generating process is unknown. A recursive bootstrap scheme is employed. We estimate the order by minimizing different information criteria. In comparison to the standard asymptotic likelihood ratio test based on an estimated lag order we found that the recursive bootstrap procedure can lead to improvements in small samples even when the true lag order is unknown while the power loss is moderate.Cointegration tests, Bootstrapping, Information criteria

Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models

This work is a comparative study of different univariate and multivariate time series predictive models as applied to Bitcoin, other cryptocurrencies, and other related financial time series data. ARIMA models, long regarded as the gold standard of univariate financial time series prediction due to both its flexibility and simplicity, are used a baseline for prediction. Given the highly correlative nature amongst different cryptocurrencies, this work aims to show the benefit of forecasting with multivariate time series models—primarily focusing on a novel parameter optimization of VARIMA models outlined in this paper. These models are trained on 3 years of historical data, aggregated from different cryptocurrency exchanges by Coinmarketcap.com, which includes: daily average prices and trading volume. Historical time series data of traditional market data, including the stock Nvidia, the de facto leading manufacture of gaming GPU’s, is also analyzed in conjunction with cryptocurrency prices, as gaming GPU’s have played a significant role in solving the profitable SHA256 hashing problems associated with cryptocurrency mining and have seen equivalently correlated investor attention as a result. Models are trained on this historical data using moving window subsets, with window lengths of 100, 200, and 300 days and forecasting 1 day into the future. Validation of this prediction against the actually price from that day are done with following metrics: Directional Forecasting (DF), Mean Absolute Error (MAE), and Mean Squared Error (MSE)

Forschungsbericht Universität Mannheim 2010 / 2011

Der Forschungsbericht bietet Ihnen eine Übersicht über die Forschungsschwerpunkte der Fakultäten, Abteilungen und Forschungseinrichtungen der Universität Mannheim. Dazu enthält der vorliegende Forschungsbericht Informationen über Einzelprojekte in den jeweiligen Fachdisziplinen sowie über zumeist fächerübergreifende Verbundprojekte wie Sonderforschungsbereiche, Forschergruppen, Wissenschaftscampi, Graduiertenschulen und Promotionskollegs. Die aus den Forschungsaktivitäten hervorgegangenen Publikationen, die Sie in diesem Bericht aufgelistet finden, leisten wichtige Beiträge zum wissenschaftlichen Fortschritt innerhalb der Disziplinen. Die ebenfalls aufgeführten Transferleistungen stellen Beiträge der Grundlagenwissenschaft zur Lösung gesellschaftlicher und wirtschaftlicher Herausforderungen dar. Nicht zuletzt enthält der Forschungsbericht Angaben zu wissenschaftlichen Preisen und Auszeichnungen, zu Veranstaltungen und Tagungen sowie zu akademischen Qualifikationen im Sinne von Promotionen und Habilitationen. Diese Angaben reflektieren die Reputation der Wissenschaftlerinnen und Wissenschaftler und ergänzen die sonstigen forschungsbezogenen Leistungen an der Universität Mannheim

Forschungsbericht Universität Mannheim 2008 / 2009

Die Universität Mannheim hat seit ihrer Entstehung ein spezifisches Forschungsprofil, welches sich in ihrer Entwicklung und derz eitigen Struktur deutlich widerspiegelt. Es ist geprägt von national und international sehr anerkannten Wirtschafts- und Sozialwissenschaften und deren Vernetzung mit leistungsstarken Geisteswissenschaften, Rechtswissenschaft sowie Mathematik und Informatik. Die Universität Mannheim wird auch in Zukunft einerseits die Forschungsschwerpunkte in den Wirtschafts- und Sozialwissenschaften fördern und andererseits eine interdisziplinäre Kultur im Zusammenspiel aller Fächer der Universität anstreben