Estimating the Value-at-Risk by Temporal VAE

Buch, Robert; Grimm, Stefanie; Korn, Ralf; Ivo, Richert,

doi:10.3390/risks11050079

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Holton (2019) study financing risks and costs, as well as Rachev et al (2011). Buch et al (2023) addresses the problem of value at risk (VaR) in the financial field, a field also studied by Artzner et al (1999).…”

Section: Literature Reviewmentioning

confidence: 99%

Taguchi Risk and Process Capability

Isaic-Maniu,

Dragan,

Grigore

et al. 2023

Risks

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Process control methods, in general, and quality, in particular, most often refer to the measures taken especially to the finished product, as well as to the technological process, in order to maintain its performance within certain statistical parameters. Genichi Taguchi is the first who developed a quality control approach, used first in Japan and later in industrialized economies, a procedure widespread in quality under the name Taguchi method. Within the Taguchi method, he imposed a key term average loss attached to a process /characteristic in case it deviates, compared to a target value/objective, considered optimal. The Taguchi methodology is especially oriented towards the design phase, different from the classic approach oriented towards the final control phase upon delivery, or towards the supervision of the processes. This new approach aims to design processes and products so that they are as insensitive as possible (robust) to the influence of external, disruptive factors of the processes. In our paper, the capability indicators of the processes and their connection with the Taguchi risk are also presented. A link is also made between the statistical measurement of uncertainty and the Taguchi risk with an example in a process from the mechanical industry.

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Section: Literature Reviewmentioning

confidence: 99%

Taguchi Risk and Process Capability

Isaic-Maniu,

Dragan,

Grigore

et al. 2023

Risks

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“…Moreover, when they are equalities, they provide new ways to estimate different risk measures in practical applications, with special focus on the value at risk. It is known that topics related to the practical estimation of risks are very important in real-world applications (Buch et al 2023;Dacorogna 2023, etc. ).…”

Section: Introductionmentioning

confidence: 99%

Bidual Representation of Expectiles

Balbás,

Balbás

et al. 2023

Risks

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Downside risk measures play a very interesting role in risk management problems. In particular, the value at risk (VaR) and the conditional value at risk (CVaR) have become very important instruments to address problems such as risk optimization, capital requirements, portfolio selection, pricing and hedging issues, risk transference, risk sharing, etc. In contrast, expectile risk measures are not as widely used, even though they are both coherent and elicitable. This paper addresses the bidual representation of expectiles in order to prove further important properties of these risk measures. Indeed, the bidual representation of expectiles enables us to estimate and optimize them by linear programming methods, deal with optimization problems involving expectile-linked constraints, relate expectiles with VaR and CVaR by means of both equalities and inequalities, give VaR and CVaR hyperbolic upper bounds beyond the level of confidence, and analyze whether co-monotonic additivity holds for expectiles. Illustrative applications are presented.

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“…In the financial domain, VAE has been employed to learn latent market structures and generate synthetic data with similar features. However, VAE may oversimplify the handling of extreme market fluctuations and nonlinear relationships, leading to inadequate generalization in some complex market situations (Buch et al, 2023).…”

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confidence: 99%

Enhancing Financial Investment Decision-Making With Deep Learning Model

Wang,

2024

Journal of Organizational and End User Computing

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This paper introduces the ISSA-BiLSTM-TPA model to improve financial investment decision-making. Traditional deep learning models face limitations in handling the complexity and uncertainty of financial markets. Our approach incorporates attention mechanisms, Bidirectional Long Short-Term Memory (BiLSTM), and Temporal Pattern Attention (TPA) to enhance accuracy in modeling and forecasting financial time series. The attention mechanism focuses on crucial information, BiLSTM captures bidirectional dependencies, and TPA identifies optimal solutions. Experimental results show higher prediction accuracy compared to traditional models, offering more reliable decision support for financial practitioners. Continuous optimization aims to provide innovative decision-making tools for the finance industry, advancing deep learning technology in finance.

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Estimating the Value-at-Risk by Temporal VAE

Cited by 3 publications

References 18 publications

Taguchi Risk and Process Capability

Taguchi Risk and Process Capability

Bidual Representation of Expectiles

Enhancing Financial Investment Decision-Making With Deep Learning Model

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