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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/10012657494
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Comparison of the Korean and US stock markets using continuous-time stochastic volatility models
Choi, Seungmoon - In: Han gug gae bal yeon gu 40 (2018) 4, pp. 1-22
Persistent link: https://www.econbiz.de/10011954453
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Modelling Asset Price Volatility Over Time Using LSTM
Datta, Suman - 2022
modelling realized volatility of asset price over time. Initially, a few modelling assumptions of classical quantitative finance …-stressed periods deserves attention. Standard time series models are used for price and volatility forecasting over time and prediction …-term memory (LSTM) model is trained to make multi-step look-ahead return forecast to calculate realized volatility. LSTM approach …
Persistent link: https://www.econbiz.de/10014238231
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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
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Fractional cointegration rank estimation
Lasak, Katarzyna; Velasco, Carlos - 2014
We consider cointegration rank estimation for a p-dimensional Fractional Vector Error Correction Model. We propose a new two-step procedure which allows testing for further long-run equilibrium relations with possibly different persistence levels. The first step consists in estimating the...
Persistent link: https://www.econbiz.de/10010244531
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Properties of the maximum likelihood estimator in spatial autoregressive models
Hillier, Grant H.; Martellosio, Federico - 2013
The (quasi-) maximum likelihood estimator (MLE) for the autoregressive parameter in a spatial autoregressive model cannot in general be written explicitly in terms of the data. The only known properties of the estimator have hitherto been its first-order asymptotic properties (Lee, 2004,...
Persistent link: https://www.econbiz.de/10010126876
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Maximum likelihood estimation of stochastic volatility models
Sandmann, Gleb; Koopman, Siem Jan - 1996
Persistent link: https://www.econbiz.de/10000953379
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Quasi-maximum likelihood estimation of volatility with high frequency data
Xiu, Dacheng - In: Journal of econometrics 159 (2010) 1, pp. 235-250
Persistent link: https://www.econbiz.de/10008839925
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How frequently does the stock price jump? : An analysis of high-frequency data with microstructure noises
Duan, Jin-Chuan (contributor); Fulop, Andras (contributor) - 2007
Persistent link: https://www.econbiz.de/10003494018
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A note on "continuous invertibility and stable QML estimation of the EGARCH(1,1) model"
Blasques, Francisco; Gorgi, Paolo; Koopman, Siem Jan; … - 2015
We revisit Wintenberger (2013) on the continuous invertibility of the EGARCH(1,1) model. We note that the definition of continuous invertibility adopted in Wintenberger (2013) may not always be sufficient to deliver strong consistency of the QMLE. We also take the opportunity to provide other...
Persistent link: https://www.econbiz.de/10011401308