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Support vector regression based GARCH model with application to forecasting volatility of financial returns

Chen, Shiyi (contributor); Jeong, Kiho (contributor); … - 2008

financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange … examined to the free parameters. Keywords: recurrent support vector regression ; GARCH model ; volatility forecasting …

Persistent link: https://www.econbiz.de/10003636113

Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns

Chen, Shiyi - 2017

Persistent link: https://www.econbiz.de/10012966267

Intraday Stock Predictability Everywhere

Liu, Fred; Stentoft, Lars - 2023

With approximately 900 million observations we conduct, to our knowledge, the largest study ever of intraday stock return predictability using machine learning techniques finding consistent out-of-sample predictability across market, sector, and individual stock returns at various time horizons....

Persistent link: https://www.econbiz.de/10014349804

PREDICTING STOCK RETURN AND VOLATILITY WITH MACHINE LEARNING AND ECONOMETRIC MODELS : A COMPARATIVE CASE STUDY OF THE BALTIC STOCK MARKET

Nõu, Anders; Lapitskaya, Darya; Eratalay, Mustafa Hakan; … - 2022

For stock market predictions, the essence of the problem is usually predicting the magnitude and direction of the stock price movement as accurately as possible. There are different approaches (e.g., econometrics and machine learning) for predicting stock returns. However, it is non-trivial to...

Persistent link: https://www.econbiz.de/10013305881

Predictability hidden by anomalous observations

Camponovo, Lorenzo; Scaillet, Olivier; Trojani, Fabio - 2013 - This version: March 2013

Persistent link: https://www.econbiz.de/10010221576

Predictability hidden by anomalous observations

Camponovo, Lorenzo; Scaillet, Olivier; Trojani, Fabio - 2013

- and out-of-sample, using predictive variables such as the dividend yield or the volatility risk premium …

Persistent link: https://www.econbiz.de/10009721331

Measuring High-Frequency Causality between Returns, Realized Volatility and Implied Volatility

Dufour, Jean-Marie - 2011

theory assumes that return shocks can be caused by changes in conditional volatility through a time-varying risk premium. On … leverage effect and the volatility feedback effect. We stress the importance of distinguishing between realized volatility and … implied volatility, and find that implied volatilities are essential for assessing the volatility feedback effect. The …

Persistent link: https://www.econbiz.de/10013128856

Evaluating density forecasts with an application to stock market returns

Raaij, Gabriela de; Raunig, Burkhard - 2002

Density forecasts have become quite important in economics and finance. For example, such forecasts play a central role in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density forecast evaluation framework as a simple alternative to other...

Persistent link: https://www.econbiz.de/10011431370

Volatility modeling for forecasting stock index with fixed parameter distributional assumption

Rahman, Md. Mostafizur; Rahman, Md. Azizur; Hossain, … - In: Journal of applied finance & banking 3 (2013) 1, pp. 109-122

The aim of this paper is to empirically investigate the in sample and out of sample forecasting performance of several GARCH-type models such as GARCH, EGARCH and APARCH model with Gaussian, student-t, Generalized error distribution (GED), student-t with fixed DOF 10 and GED with fixed parameter...

Persistent link: https://www.econbiz.de/10009741216

Evaluating Density Forecasts with an Application to Stock Market Returns

Raunig, Burkhard - 2016

Persistent link: https://www.econbiz.de/10012991280

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