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Empirical Asset Pricing via Ensemble Gaussian Process Regression
Filipović, Damir; Pasricha, Puneet - 2022
We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and...
Persistent link: https://www.econbiz.de/10014236083
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Mixed-frequency predictive regressions with parameter learning
Leippold, Markus; Yang, Hanlin - In: Journal of forecasting 42 (2023) 8, pp. 1955-1972
Persistent link: https://www.econbiz.de/10014432824
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Quantitative equity investing : techniques and strategies
Fabozzi, Frank J.; Focardi, Sergio M.; Kolm, Petter N. - 2010
Persistent link: https://www.econbiz.de/10003898661
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Multivariate fractional regression estimation of econometric share models
Mullahy, John - 2010
Persistent link: https://www.econbiz.de/10008662924
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Quantitative equity investing : techniques and strategies
Fabozzi, Frank J.; Focardi, Sergio M.; Kolm, Petter N. - 2010
management business and include the necessary background material in probability, statistics, and econometrics to make the book … QUANTITATIVE EQUITY MANAGEMENT -- LOOKING AHEAD FOR QUANTITATIVE EQUITY INVESTING -- Chapter 2: Financial Econometrics I: Linear … TREES -- SUMMARY -- Chapter 3: Financial Econometrics II: Time Series -- STOCHASTIC PROCESSES -- TIME SERIES -- STABLE …
Persistent link: https://www.econbiz.de/10012683140
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Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computations
Crépey, Stéphane - 2019
Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all the trades with each counterparty under both market and credit risk. We present a multi-Gaussian process regression approach, which is well suited for OTC derivative portfolio...
Persistent link: https://www.econbiz.de/10012893780
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Gaussian process regression for derivative portfolio modeling and application to credit valuation adjustment computations
Crépey, Stéphane; Dixon, Matthew F. - In: The journal of computational finance 24 (2020) 1, pp. 47-81
Persistent link: https://www.econbiz.de/10012421957
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Mixed-Frequency Predictive Regressions with Parameter Learning
Leippold, Markus; Yang, Hanlin - 2023
We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed-frequency models...
Persistent link: https://www.econbiz.de/10014348997
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Special issue: Symposium on computational finance
Jensen, Bjarne Astrup (contributor) - 2000
Persistent link: https://www.econbiz.de/10001653140