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Predicting the Claims-Development-Result in the Chain-Ladder Method for Correlated Run-Off Portfolios
Heberle, Jochen - 2012
In the present paper we analyse how the estimators from Merz u. Wüthrich (2007) could be generalised to the case of N correlated run-off triangles. The simultaneous view on N correlated subportfolios is motivated by the fact, that in practice a run-off portfolio often has to be divided in...
Persistent link: https://www.econbiz.de/10013106624
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Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies
Basturk, Nalan - 2018
A dynamic asset-allocation model is specified in probabilistic terms as a combination of return distributions resulting from multiple pairs of dynamic models and portfolio strategies based on momentum patterns in US industry returns. The nonlinear state space representation of the model allows...
Persistent link: https://www.econbiz.de/10012909578
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Predicting and Decomposing the Risk of Data-driven Portfolios
Bouamara, Nabil - 2020
Sophisticated algorithmic techniques are complementing human judgement across the fund industry. Whatever the type of rebalancing that occurs in the course of a longer horizon, it probably violates the buy-and-hold assumption. In this article, we develop the methodology to predict, dissect and...
Persistent link: https://www.econbiz.de/10012851460
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Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios : A General Dynamic Factor Model Approach
Hallin, Marc; Trucíos, Carlos - 2021
Beyond their importance from the regulatory policy point of view, Value-at-Risk (VaR) and Expected Shortfall (ES) play an important role in risk management, portfolio allocation, capital level requirements, trading systems, and hedging strategies. Unfortunately, due to the curse of...
Persistent link: https://www.econbiz.de/10013242339
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Equity Machine Factor Models
Noguer i Alonso, Miquel; Zoonekynd, Vincent - 2022
We examine in this paper the training and test set performance of several equity factor models with a dataset of 20 years of data, 1,200 stocks and 100 factors. First, we examine several models to forecast expected returns, which can be used as baselines for more complex models: linear...
Persistent link: https://www.econbiz.de/10014255242
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The limits of expanding breadth : the trade-offs between diversification and forecasting ability
Goldsticker, Ralph - In: The journal of investing 22 (2013) 3, pp. 28-38
Persistent link: https://www.econbiz.de/10010194891
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Factor investing : alpha concentration versus diversification
Heinrich, Lars; Shivarova, Antoniya; Zurek, Martin - In: The journal of asset management : a major new, … 22 (2021) 6, pp. 464-487
Persistent link: https://www.econbiz.de/10012659821
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Behavioral Finance - Asset Prices Predictability, Equity Premium Puzzle, Volatility Puzzle : The Rational Finance Approach
Rachev, Svetlozar T. - 2020
In this paper we address three main objections of behavioral finance to the theory of rational finance, considered as … “anomalies” the theory of rational finance cannot explain: (i) Predictability of asset returns; (ii) The Equity Premium; (iii … are the only possible explanations of the “anomalies”, but offer statistical models within the rational theory of finance …
Persistent link: https://www.econbiz.de/10012842392
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Machine Learning in Risk Measurement : Gaussian Process Regression for Value-at-Risk and Expected Shortfall
Wilkens, Sascha - 2019
in a risk measurement context is less developed. This paper uses a scheme from probability theory and statistics …
Persistent link: https://www.econbiz.de/10012898200
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Forecast density combinations of dynamic models and data driven portfolio strategies
Basturk, Nalan; Borowska, Agnieszka; Grassi, Stefano; … - 2018
A dynamic asset-allocation model is specified in probabilistic terms as a combination of return distributions resulting from multiple pairs of dynamic models and portfolio strategies based on momentum patterns in US industry returns. The nonlinear state space representation of the model allows...
Persistent link: https://www.econbiz.de/10011916443