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Stock Market Volatility and Mathematical Expectations
James, Robert G. - 2019
It is generally believed that excessive stock market volatility reflects non-mathematical market expectations that are …
Persistent link: https://www.econbiz.de/10012862894
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Modelling UK Stock Returns : A Multiple-Regime Framework
Khadaroo, Ahmad Jameel - 2012
Through the use of regime-switching models, recent empirical research has essentially demonstrated that the dynamics of stock returns depend on the state of one stock market. The present paper extends this analytical framework by allowing the dynamics of returns to depend on the joint-states of...
Persistent link: https://www.econbiz.de/10013101775
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Detecting serial dependence in tail events
Diks, Cees G. H. - 2002
A test for serial independence is proposed which is related to the BDS test but focuses on tail event probabilities rather than probabilities near the center of the distribution. The motivation behind this approach is to obtain a test more suitable for detecting structure in the tails, such as...
Persistent link: https://www.econbiz.de/10011327543
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Modeling Stock Price Movements : Multifractality or Multifractionality?
Bianchi, Sergio - 2011
The scaling properties of two alternative fractal models recently proposed to characterize the dynamics of stock market prices are compared. The former is the Multifractal Model of Asset Return (MMAR) introduced in 1997 by Mandelbrot, Calvet and Fisher in three companion papers. The latter is...
Persistent link: https://www.econbiz.de/10013122371
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Superstatistical Fluctuations in Time Series of Leverage Returns
Katz, Yuri A. - 2013
Fat tails of q-Gaussian distributions of daily log-leverage-returns of 520 North American industrial firms reported by Katz and Tian (2013) imply a significantly higher credit risk at short time-horizons and/or large initial distances to the default barrier than forecasted by traditional...
Persistent link: https://www.econbiz.de/10013072548
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A bibliometric analysis of machine learning econometrics in asset pricing
Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of risk and financial management : JRFM 15 (2022) 11, pp. 1-17
Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...
Persistent link: https://www.econbiz.de/10013475217
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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
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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
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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
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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