Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model

Zhang, Heng-Guo; Su, Chi‐Wei; Yan, Shuicheng; Qiu, Shuqi; Xiao, Ran; Su, Fei

doi:10.1016/j.econmod.2017.02.014

Cited by 15 publications

(7 citation statements)

References 52 publications

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“…By using NN models, the performance of the volatility estimation method can be improved (Monfared and Enke 2014). Zhang et al (2017) propose a model that is based on the Generalized Autoregressive Conditional Heteroskedastic (GARCH) model and Extreme Machine Learning (ELM) algorithm to estimate volatility. The model predicts the volatility of target time series using GELM-RBF and extrapolating the predicted volatilities allows for the calculation of VaR with improved performance in terms of accuracy and efficiency.…”

Section: Market Riskmentioning

confidence: 99%

Machine Learning in Banking Risk Management: A Literature Review

Leo¹,

Sharma²,

Maddulety³

2019

Risks

276

129

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There is an increasing influence of machine learning in business applications, with many solutions already implemented and many more being explored. Since the global financial crisis, risk management in banks has gained more prominence, and there has been a constant focus around how risks are being detected, measured, reported and managed. Considerable research in academia and industry has focused on the developments in banking and risk management and the current and emerging challenges. This paper, through a review of the available literature seeks to analyse and evaluate machine-learning techniques that have been researched in the context of banking risk management, and to identify areas or problems in risk management that have been inadequately explored and are potential areas for further research. The review has shown that the application of machine learning in the management of banking risks such as credit risk, market risk, operational risk and liquidity risk has been explored; however, it doesn’t appear commensurate with the current industry level of focus on both risk management and machine learning. A large number of areas remain in bank risk management that could significantly benefit from the study of how machine learning can be applied to address specific problems.

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Section: Market Riskmentioning

confidence: 99%

Machine Learning in Banking Risk Management: A Literature Review

Leo¹,

Sharma²,

Maddulety³

2019

Risks

276

129

View full text Add to dashboard Cite

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“…The need for financial risk information and an increased interest by policy makers on financial risk issues and VaR, in combination with advances in financial econometrics and computer science over the last decades, have led to significant advances in VaR modeling: Monte Carlo (Berkowitz et al 2011 ), the popular GARCH family models (Angelidis et al 2004 ; Degiannakis et al 2012 ; Engle, 2004 ), and the Markov Switching Regime models (Billio & Pelizzon, 2000 ). However, some contemporary suggestions, such as Fuzzy VaR, Expected Shortfall models with elliptical distributions (Moussa et al 2014 , and Extreme Learning Machine (Zhang et al 2017 ), are too complex to be implemented (explained) in the financial industry by (to) non-mathematicians and computer scientists.…”

Section: Introductionmentioning

confidence: 99%

Inaccurate Value at Risk Estimations: Bad Modeling or Inappropriate Data?

Vasileiou

2021

Comput Econ

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Forecasting accurate Value-at-Risk (VaR) estimations is a crucial task in applied financial risk management. Even though there have been significant advances in the field of financial econometrics, many crises have been documented throughout the world in the last decades. An explanation for this discrepancy is that many contemporary models are too complex and cannot be easily understood and implemented in the financial industry (Fama in Financ Anal J 51:75–80, 1995; Ross in AIMR conference proceedings, vol. 1993, no. 6, pp. 11–15, Association for Investment Management and Research, 1993). In order to bridge this theory–practice gap, we present a computational method based on the leverage effect. This method allows us to focus on financial theory and remove complexity. Examining the US stock market (2000–2020), we provide empirical evidence that our newly suggested approach, which uses only the most appropriate observation period, significantly increases the accuracy of the Conventional Delta Normal VaR model and generates VaR estimations which are as accurate as those of advanced econometric models, such as GARCH(1,1).

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“…GELM is a non-linear random mapping model proposed by the authors of [14] that is a combination of the GARCH model and the extreme learning machine (ELM) used to compute the VaR. Its performance and precision have proven to be better those of other traditional models, like GARCH, SVM, and ELM.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation

Bagheri,

Reforgiato Recupero,

Sirnes

2023

Data

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Value at risk is a statistic used to anticipate the largest possible losses over a specific time frame and within some level of confidence, usually 95% or 99%. For risk management and regulators, it offers a solution for trustworthy quantitative risk management tools. VaR has become the most widely used and accepted indicator of downside risk. Today, commercial banks and financial institutions utilize it as a tool to estimate the size and probability of upcoming losses in portfolios and, as a result, to estimate and manage the degree of risk exposure. The goal is to obtain the average number of VaR “failures” or “breaches” (losses that are more than the VaR) as near to the target rate as possible. It is also desired that the losses be evenly distributed as possible. VaR can be modeled in a variety of ways. The simplest method is to estimate volatility based on prior returns according to the assumption that volatility is constant. Otherwise, the volatility process can be modeled using the GARCH model. Machine learning techniques have been used in recent years to carry out stock market forecasts based on historical time series. A machine learning system is often trained on an in-sample dataset, where it can adjust and improve specific hyperparameters in accordance with the underlying metric. The trained model is tested on an out-of-sample dataset. We compared the baselines for the VaR estimation of a day (d) according to different metrics (i) to their respective variants that included stock return forecast information of d and stock return data of the days before d and (ii) to a GARCH model that included return prediction information of d and stock return data of the days before d. Various strategies such as ARIMA and a proposed ensemble of regressors have been employed to predict stock returns. We observed that the versions of the univariate techniques and GARCH integrated with return predictions outperformed the baselines in four different marketplaces.

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Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model

Cited by 15 publications

References 52 publications

Machine Learning in Banking Risk Management: A Literature Review

Machine Learning in Banking Risk Management: A Literature Review

Inaccurate Value at Risk Estimations: Bad Modeling or Inappropriate Data?

Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation

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