Download or read book Essays on Machine Learning in Volatility Forecasting written by Eghbal Rahimikia and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Volatility Forecasting written by Dimos S. Kambouroudis and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays on Volatility Forecasting written by Xin Cheng and published by . This book was released on 2010 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Financial Volatility Forecasting written by Katina Tsakou and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays on Volatility Forecasting and Forecast Evaluation written by Onno Kleen and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Realised Volatility Forecasting for International Stock Markets written by Yi Ding and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Cojumps Hedging and Volatility Forecasting written by Lyudmyla Hvozdyk and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on the Forecasting Power of Implied Volatility written by Prithviraj S. Banerjee and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Machine Learning Approach to Volatility Forecasting written by Kim Christensen and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays in Computational Statistics with Applications to Volatility Forecasting and Forecast Combination written by Simon D. Knaus and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Der erste Aufsatz der vorliegenden Dissertation untersucht die Dynamik der realisierten Volatilität. Ein erfolgreiches Model in diesem Bereich ist das sogenannte heterogene auto-regressive Modell (HAR), das konzeptionell einfach und gut für Vorhersagen geeignet ist. Eine neue Herangehensweise basierend auf dem Operator der kleinsten absoluten Schrumpfung und Auswahl ermöglicht es, das HAR Modell von einem Modellwahl Standpunkt her zu betrachten. Es wird gezeigt, dass die Modellwahl asymptotisch das wahre Modell identifizieren könnte. Zusätzlich werden simulierte Resultate im nicht-asymptotischen Bereich präsentiert, welche die Modellwahlgüte unterstreichen. Zusammenfassend kann gesagt werden, dass das HAR Model wohl nicht als wahres Modell identifiziert wird, das gewählte Model aber nicht vom HAR Modell zu unterscheiden ist, falls Vorhersagegüte als Vergleichskriterium herangezogen wird. Der zweite Aufsatz untersucht den Einfluss von externen Faktoren auf die Dynamik von Modellen für die realisierte Volatilität. Diese externen Faktoren umfassen Volatilitätsübertragung zwischen Märkten, die Bekanntgabe von makroökonomischen Kenngrössen, den Hebeleffekt sowie innerwöchentliche Saisonalitäten. Um die Rolle der einzelnen Faktoren besser zu verstehen wird wiederum der Operator der kleinsten absoluten Schrumpfung und Auswahl herangezogen. Zusätzlich werden diese Resultate mit existierenden Modellen verglichen, um die Relevanz der genannten Faktoren auf die Modellierung der realisierten Volatilität des S & P 500 Index abzuschätzen. Es kann festgehalten werden, dass ein um diese Informationen erweitertes Modell tatsächlich bessere Vorhersagen für die realisierte Volatilität liefert. Der dritte Aufsatz untersucht die Kombination von Expertenprognosen für makroökonomische Daten. Obwohl individuelle Expertenprognosen sehr verbreitet sind ist es a priori nicht klar, wie diese zu einer aggregierten Vorhersage kombinier.

Download or read book Essays on Forecasting and Volatility Modelling written by Gustavo Fruet Dias and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on the Suitability of Machine Learning Algorithms for Financial Forecasts written by Marko Kureljusic and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays in Honor of Subal Kumbhakar written by Christopher F. Parmeter and published by Emerald Group Publishing. This book was released on 2024-04-05 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is the editor’s distinct privilege to gather this collection of papers that honors Subhal Kumbhakar’s many accomplishments, drawing further attention to the various areas of scholarship that he has touched.

Download or read book Volatility Forecasting with Machine Learning Methods written by Tim Hess and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays in Cryptocurrencies Forecasting and Trading with Technical Analysis and Advanced Machine Learning Methods written by Mingzhe Wei and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Volatility Forecasting and Density Estimation written by Shan Lu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Volatility Forecasting and Density Estimation written by Shan Lu and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter 4 compares six estimation methods for extracting risk-neutral densities (RND) from option prices. By using a pseudo-price based simulation, we find that the positive convolution approximation method provides the best performance, while mixture of two lognormals is the worst; In addition, we show that both price and volatility jumps are important components for option pricing. Our results have practical applications for policymakers as RNDs are important indicators to gauge market sentiment and expectations.