Deep learning for CVA computations of large portfolios of financial derivatives

Andersson, Kristoffer; Oosterlee, Cornelis W.

doi:10.1016/j.amc.2021.126399

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…. Deep learning, reinforcement learning, and deep reinforcement learning in portfolio optimization DL concept has been used lately to manage portfolios in diverse conditions based on neural networks (Becker et al, 2019;Andersson and Oosterlee, 2021). Thus, numerous variants of DNN may function as independent evaluators to optimize the algorithm.…”

Section: Metaheuristics For Portfolio Optimizationmentioning

confidence: 99%

Enhancing portfolio management using artificial intelligence: literature review

Sutiene,

Schwendner,

Sipos

et al. 2024

Front. Artif. Intell.

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Building an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process.

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Section: Metaheuristics For Portfolio Optimizationmentioning

confidence: 99%

Enhancing portfolio management using artificial intelligence: literature review

Sutiene,

Schwendner,

Sipos

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…For instance, Lopez de Prado (2016) presents a portfolio asset allocation scheme that exploits clustering techniques. Moreover, Kwak et al (2021), Andersson and Oosterlee (2021) are examples of papers where a deep learning framework is proposed to optimize some portfolios management aspects. Needless to say, due to the novelty of these techniques, there is room for improvement.…”

Section: Introductionmentioning

confidence: 99%

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach Based on Network Community Detection

Ferretti

2022

Comput Econ

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We present a study on portfolio investments in financial applications. We describe a general modeling and simulation framework and study the impact on the use of different metrics to measure the correlation among assets. In particular, besides the traditional Pearson’s correlation, we employ the Detrended Cross-Correlation Analysis (DCCA) and Detrended Partial Cross-Correlation Analysis (DPCCA). Moreover, a novel portfolio allocation scheme is introduced that treats assets as a complex network and uses modularity to detect communities of correlated assets. Weights of the allocation are then distributed among different communities for the sake of diversification. Simulations compare this novel scheme against Critical Line Algorithm (CLA), Inverse Variance Portfolio (IVP), the Hierarchical Risk Parity (HRP). Synthetic times series are generated using the Gaussian model, Geometric Brownian motion, GARCH, ARFIMA and modified ARFIMA models. Results show that the proposed scheme outperforms state of the art approaches in many scenarios. We also validate simulation results via backtesting, whose results confirm the viability of the proposal.

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“…For instance, [43] presents a portfolio asset allocation scheme that exploits clustering techniques. Moreover, [28,1] are examples of papers where a deep learning framework is proposed to optimize some portfolios management aspects. Needless to say, due to the novelty of these techniques, there is room for improvement.…”

Section: Introductionmentioning

confidence: 99%

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach based on Network Community Detection

Ferretti

2022

Preprint

View full text Add to dashboard Cite

We present a study on portfolio investments in financial applications. We describe a general modeling and simulation framework and study the impact on the use of different metrics to measure the correlation among assets. In particular, besides the traditional Pearson's correlation, we employ the Detrended Cross-Correlation Analysis (DCCA) and Detrended Partial Cross-Correlation Analysis (DPCCA). Moreover, a novel portfolio allocation scheme is introduced that treats assets as a complex network and uses modularity to detect communities of correlated assets. Weights of the allocation are then distributed among different communities for the sake of diversification. Simulations compare this novel scheme against Critical Line Algorithm (CLA), Inverse Variance Portfolio (IVP), the Hierarchical Risk Parity (HRP). Synthetic times series are generated using the Gaussian model, Geometric Brownian motion, GARCH, ARFIMA and modified ARFIMA models. Results show that the proposed scheme outperforms state of the art approaches in many scenarios. We also validate simulation results via backtesting, whose results confirm the viability of the proposal.

show abstract

Deep learning for CVA computations of large portfolios of financial derivatives

Cited by 5 publications

References 17 publications

Enhancing portfolio management using artificial intelligence: literature review

Enhancing portfolio management using artificial intelligence: literature review

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach Based on Network Community Detection

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach based on Network Community Detection

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