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Comparing large-sample maximum Sharpe ratios and incremental variable testing
Hanke, Michael; Penev, Spiridon - In: European journal of operational research : EJOR 265 (2018) 2, pp. 571-579
Persistent link: https://www.econbiz.de/10011811449
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Handbook of financial econometrics, mathematics, statistics, and machine learning
Lee, Cheng F. (ed.); Lee, John C. (ed.) - 2021
, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance …, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook … finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook. Led by …
Persistent link: https://www.econbiz.de/10012061412
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Likelihood-based statistical estimation from quantized data
Vardeman, Stephen B.; Lee, Chiang-Sheng - 2003
Most standard statistical methods treat numerical data as if they were real (infinitenumber- of-decimal-places) observations. The issue of quantization or digital resolution is recognized by engineers and metrologists, but is largely ignored by statisticians and can render standard statistical...
Persistent link: https://www.econbiz.de/10010511727
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Likelihood-based statistical estimation from quantized data
Vardeman, Stephen B.; Lee, Chiang-Sheng - 2003
Persistent link: https://www.econbiz.de/10001901600
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ANOVA with Summary Statistics : A Stata Macro
Butt, Nadeem Shafique - 2011
some packages can perform one-way ANOVA after generating surrogate data from summary statistics. In this short note we have …
Persistent link: https://www.econbiz.de/10013125051
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Estimating Value-at-Risk and Expected Shortfall using the intraday low and range data
Meng, Xiaochun; Taylor, James W. - In: European journal of operational research : EJOR 280 (2020) 1, pp. 191-202
Persistent link: https://www.econbiz.de/10012132379
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Quasi-maximum likelihood estimation for long memory stock transaction data - under conditional heteroskedasticity framework
Quoreshi, A. M. M. Shahiduzzaman; Uddin, Reaz; Khan, … - In: Journal of risk and financial management : JRFM 12 (2019) 2/74, pp. 1-13
This paper introduces Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data of unknown underlying distribution. The moments with conditional heteroscedasticity have been discussed. In a Monte Carlo experiment, it was found that the QML estimator performs as well as CLS and...
Persistent link: https://www.econbiz.de/10012022130
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Asymmetries in conditional mean and variance : modelling stock returns by asMA-asQGARCH
Brännäs, Kurt; Gooijer, Jan G. de - 2000
The asymmetric moving average model (asMA) is extended to allow forasymmetric quadratic conditional heteroskedasticity (asQGARCH). Theasymmetric parametrization of the conditional variance encompassesthe quadratic GARCH model of Sentana (1995). We introduce a framework fortesting asymmetries in...
Persistent link: https://www.econbiz.de/10011303289
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Does risk aversion affect bank output loss? : the case of the Eurozone
Tsionas, Efthymios G.; Mamatzakis, Emmanuel C.; Ongena, … - In: European journal of operational research : EJOR 282 (2020) 3, pp. 1127-1145
Persistent link: https://www.econbiz.de/10012161877
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An improved least squares Monte Carlo valuation method based on heteroscedasticity
Fabozzi, Frank J.; Paletta, Tommaso; Tunaru, Radu - In: European journal of operational research : EJOR 263 (2017) 2, pp. 698-706
Persistent link: https://www.econbiz.de/10011794017