DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks

Fatouros, Georgios; Makridis, Georgios; Kotios, Dimitrios; Soldatos, John; Filippakis, Michael Ε.; Kyriazis, Dimosthenis

doi:10.1007/s42521-022-00050-0

Cited by 15 publications

(3 citation statements)

References 36 publications

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“…where, represents the covariance of asset i and j, is number of single assets in portfolio While the formulas to calculate the variance and standard deviation of the portfolio consisting of assets are as follows: After the covariance matrix, variance, and standard deviation are obtained, the VaR of the portfolio with Variance Covariance method can be estimated. The formula to estimate the VaR of the portfolio with Variance Covariance method is as follows [15]: √ 1.9 where, P is initial investment, = standard normal distribution quantile with confidence level , = holding period, and = the standard deviation of portfolio…”

Section: Variance Covariance Methodsmentioning

confidence: 99%

Comparison of Variance Covariance and Historical Simulation Methods to Calculate Value At Risk on Banking Stock Portfolio

Irsan

Priscilla

Siswanto

2022

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In investing, all investors must be faced with risk that must be borne. Therefore, to determine the best strategy in investing, every investor must calculate the risk. One statistical approach that can be used to measure the risk is Value at Risk (VaR). VaR is defined as a tolerable loss with a certain level of confidence. The purpose of this research is to estimate VaR using Variance Covariance and Historical Simulation methods on banking stock portfolio consisting of three stocks for the period 11 September 2020-30 September 2021. Both methods will then be evaluated using backtesting to determine the accuracy of VaR and to obtain the best method. From the research results, if the holding period is 1 day, then the VaR calculation for banking stock portfolio using both methods can be used to estimate the risk at 99% and 95% confidence levels, except for the VaR value using the Variance Covariance method for banking stock portfolio at 95% confidence level. The results show that Variance Covariance method is the best method for 99% confidence level. As for the 95% confidence level, Historical Simulation method is the best method.

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Section: Variance Covariance Methodsmentioning

confidence: 99%

Comparison of Variance Covariance and Historical Simulation Methods to Calculate Value At Risk on Banking Stock Portfolio

Irsan

Priscilla

Siswanto

2022

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“…To account for the time dependency in the data, they preprocess the data with rolling window statistics. Fatouros et al (2022) estimate the VaR of the underlying univariate time series composing the portfolio via a recurrent neural net. They combine the univariate VaR estimates via an estimate of the covariance matrix of the log-returns of the elements of the portfolio.…”

Section: Comparison To Related Workmentioning

confidence: 99%

Estimating the Value-at-Risk by Temporal VAE

Buch

Grimm

Korn

et al. 2023

Risks

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Estimation of the value-at-risk (VaR) of a large portfolio of assets is an important task for financial institutions. As the joint log-returns of asset prices can often be projected to a latent space of a much smaller dimension, the use of a variational autoencoder (VAE) for estimating the VaR is a natural suggestion. To ensure the bottleneck structure of autoencoders when learning sequential data, we use a temporal VAE (TempVAE) that avoids the use of an autoregressive structure for the observation variables. However, the low signal-to-noise ratio of financial data in combination with the auto-pruning property of a VAE typically makes use of a VAE prone to posterior collapse. Therefore, we use annealing of the regularization to mitigate this effect. As a result, the auto-pruning of the TempVAE works properly, which also leads to excellent estimation results for the VaR that beat classical GARCH-type, multivariate versions of GARCH and historical simulation approaches when applied to real data.

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“…Deciding and reducing manifestation constitute one of the considerable issues in the nancial industry that in uence analogous decisions. In this state of affairs, a probabilistic deep learning technique considering time series forecasting mechanisms with elevated prospective of risk monitoring in an e cient manner was presented in [17]. Yet another novel deep learning technique employing Wavelet Transforms (WT), Stacked Auto Encoders (SAEs) and Long Short Term Memory (LSTM) [18] were integrated for forecasting stock price.…”

Section: Organizationmentioning

confidence: 99%

Box Jenkins Neural Network Keynecian Reinforcement Learning Based Financial Big Data Analysis for Optimal Prediction

Zhang

2022

Preprint

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In the digital era, Economic and Finance information is huge and attainable. The financial industry is a key to stimulating the evolution of the national economy, and with the immense availability of huge data makes the process of financial data analysis both a time consuming and tedious decision making in the financial market. By analyzing this Economic and Finance information significantly and easily, valuable perceptions can be provided. For rational economic growth, information pertaining to economic affairs and likelihoods is critical to decision-makers like governmental bodies, financial institutions and banks. However, precise predictions have been demand owing to the intricacy and unpredictability of financial and economic systems in the midst of persistent changes in economic environments. This work provides to approaches for better economic prediction and decision-making using a novel method called, Multivariate Box Jenkins Neural Network-based Keynecian Reinforcement Learning (MBJNN-KRL). In this work, Financial Data Analysis Deep Economic Prediction model that contains a Multivariate Box Jenkins Long Short Term Memory Forecasting model, for better economic prediction and convergence speed even in case of large data is first designed. The deep reinforcement learning technique is then adapted to retrain neural networks and rebalance the aggregate expenditure periodically. We combined the multivariate Multivariate Box Jenkins Long Short Term Memory Forecasting and the Keynecian Reinforcement Learning models into a single integrated reinforcement learning model named Multivariate Box Jenkins Neural Network-based Keynecian Reinforcement Learning (MBJNN-KRL). Finally, distinct sets of experiments are carried out on real large-scale data set, and the results entirely prove the efficiency and robustness of the proposed method in arbitrary economic prediction based on financial data. Our empirical data revealed that the MBJNN-KRL method could attain combative financial performance compared to traditional methods in terms of predictive accuracy, convergence speed and prediction error.

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DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks

Cited by 15 publications

References 36 publications

Comparison of Variance Covariance and Historical Simulation Methods to Calculate Value At Risk on Banking Stock Portfolio

Comparison of Variance Covariance and Historical Simulation Methods to Calculate Value At Risk on Banking Stock Portfolio

Estimating the Value-at-Risk by Temporal VAE

Box Jenkins Neural Network Keynecian Reinforcement Learning Based Financial Big Data Analysis for Optimal Prediction

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