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Using Portfolio Returns to Estimate the Probability of Large Jumps
Cacho-Diaz, Julio - 2021
Sudden jumps in the stock market have a significant impact on consumers’ wealth. A market crash, in particular, can devastate lives and destabilize the entire economy. Therefore, it would be desirable if consumers, policy makers, and financial intermediaries could better anticipate such...
Persistent link: https://www.econbiz.de/10013239109
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Predicting the global minimum variance portfolio
Reh, Laura; Krüger, Fabian; Liesenfeld, Roman - 2020
We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP). The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss...
Persistent link: https://www.econbiz.de/10012243462
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Predicting the Global Minimum Variance Portfolio
Reh, Laura - 2020
We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP). The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss...
Persistent link: https://www.econbiz.de/10012847269
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Portfolio Construction When Regimes are Ambiguous
Kritzman, Mark; Kulasekaran, Cel; Turkington, David - 2023
Investors sometimes have strong convictions that a distinctive economic regime will prevail in the period ahead and therefore would like to form a portfolio that reflects the expected returns, standard deviations, and correlations of assets during such a regime. To do so, they typically isolate...
Persistent link: https://www.econbiz.de/10014348956
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Package CovRegpy : Regularised Covariance Regression and Forecasting in Python
van Jaarsveldt, Cole; Peters, Gareth; Ames, Matthew; … - 2023
This paper will outline the functionality available in the CovRegpy package for actuarial practitioners, wealth managers, fund managers, and portfolio analysts written in Python 3.7. The major contributions of CovRegpy can be found in the CovRegpy_DCC.py, CovRegpy_IFF.py, CovRegpy_RCR.py,...
Persistent link: https://www.econbiz.de/10014253907
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Maximum entropy evaluation of asymptotic hedging error under a generalised jump-diffusion model
Fard, Farzad Alavi; Doko Tchatoka, Firmin; … - In: Journal of risk and financial management : JRFM 14 (2021) 3/97, pp. 1-19
In this paper we propose a maximum entropy estimator for the asymptotic distribution of the hedging error for options …. Perfect replication of financial derivatives is not possible, due to market incompleteness and discrete-time hedging. We … derive the asymptotic hedging error for options under a generalised jump-diffusion model with kernel bias, which nests a …
Persistent link: https://www.econbiz.de/10012484861
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Asymmetric Jump Beta Estimation with Implications for Portfolio Risk Management
Alexeev, Vitali - 2019
We evaluate the impact of extreme market shifts on equity portfolios and study the difference in negative and positive reactions to market jumps with implications for portfolio risk management. Employing high-frequency data for the constituents of the S&P500 index over the period 2 January 2003...
Persistent link: https://www.econbiz.de/10012865575
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Shrinkage Estimation in Risk Parity Portfolios
Alkafri, Nabil; Frey, Christoph - 2022
We investigate the impact of shrinkage estimation techniques for the moments of asset returns on risk-parity portfolios. In contrast to mean-variance portfolios, the risk contributions of individual assets in risk-parity portfolios are fixed a priori. This additional restriction stabilizes...
Persistent link: https://www.econbiz.de/10013313921
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Estimation methods for non-homogeneous regression models : minimum continuous ranked probability score vs. maximum likelihood
Gebetsberger, Manuel; Messner, Jakob W.; Mayr, Georg. J.; … - 2017
Non-homogeneous regression models are widely used to statistically post-process numerical ensemble weather prediction models. Such regression models are capable of forecasting full probability distributions and correct for ensemble errors in the mean and variance. To estimate the corresponding...
Persistent link: https://www.econbiz.de/10011762435
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Mixing Probabilities, Priors and Kernels via Entropy Pooling
Meucci, Attilio - 2012
We show how to mix machine learning signals such as kernel smoothing and fuzzy memberships via the Entropy Pooling approach by Meucci (2008). We illustrate a case study, where we overlay an exponentially time-decayed prior to a pseudo-Gaussian kernel that emphasizes market scenarios where...
Persistent link: https://www.econbiz.de/10013113859