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Asymmetric connectedness of stocks : how does bad and good volatility spill over the US stock market?

Barunik, Jozef; Kočenda, Evžen; Vácha, Lukáš - 2014

Asymmetries in volatility spillovers are highly relevant to risk valuation and portfolio diversification strategies in … volatility may spill over at different magnitudes. This paper fills this gap with two contributions. One, we suggest how to … quantify asymmetries in volatility spillovers due to bad and good volatility. Two, using high frequency data covering most …

Persistent link: https://www.econbiz.de/10010407529

Asymmetric connectedness on the US stock market : bad and good volatility spillovers

Baruník, Jozef; Kočenda, Evžen; Vácha, Lukáš - 2015

This paper suggests how to quantify asymmetries in volatility spillovers that emerge due to bad and good volatility … stocks at the disaggregate level. Moreover, the spillovers of bad and good volatility are transmitted at different magnitudes …

Persistent link: https://www.econbiz.de/10010509638

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

High-dimensional statistical learning techniques for time-varying limit order book networks

Chen, Shi; Härdle, Wolfgang; Schienle, Melanie - 2021

This paper provides statistical learning techniques for determining the full own-price market impact and the relevance and effect of cross-price and cross-asset spillover channels from intraday transactions data. The novel tools allow extracting comprehensive information contained in the limit...

Persistent link: https://www.econbiz.de/10012614016

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

Chen, Shiyi - 2017

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

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

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

Maximum likelihood estimation of stock volatility using jump-diffusion models

Chekenya, Nixon S. - In: Cogent economics & finance 7 (2019) 1, pp. 1-17

return volatility. Using stock market data from June 2002 to September 2016, we hypothesize that stocks with high positive … properties of volatility on listed stocks. We also conjecture that it is valid to use maximum likelihood procedures in estimating …

Persistent link: https://www.econbiz.de/10012023360

Securities Litigation Event Studies in the COVID Volatility Regime

Feinstein, Steven P.; Villanueva, Miguel - 2022

, a substantial increase in market-wide volatility, such as what happened in 2020 with the COVID-19 pandemic, can render …

Persistent link: https://www.econbiz.de/10013296845

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