Forecasting VaR and Expected Shortfall using Dynamical Systems: A Risk Management Strategy

Caillault, Cyril; Guégan, Dominique

doi:10.2139/ssrn.898828

Cited by 3 publications

(6 citation statements)

References 19 publications

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“…The Moving Window method proposed by Caillault and Guegan [17] starts from a fixed interval and then moves forward until the whole data is covered. In each interval, Caillault and Guegan [17] fitted the best copula while in this paper, we test the goodness-of-fit instead of just choosing the best copula. We choose the rolling-window size as N points and move this window by K points every time in order to maintain a good power of test.…”

Section: Moving Windowmentioning

confidence: 99%

“…Caillault and Guegan [16] first established the variation of copula family in measuring risk using a moving window approach. Zhang and Guegan [33] also applied the moving window approach and detected the change both in copula family and parameter of Asian foreign exchange markets, USD/THB (Thai Baht) and USD/MYR (Malaysian Dollar).…”

Section: Introductionmentioning

confidence: 99%

“…The most popular two approaches to detect the change of copula are the Binary Segmentation [31] and the Moving Window method [17]. The main idea of Binary Segmentation is splitting the whole sample into two subsamples if the copula does not remain the same repeatedly until this is not the case.…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, Moving Window method fixes an initial window which is then moved forward until the whole sample is covered. This method can detect the change more precisely while the width of the moving window is still an open question, see Caillault and Guegan [17]. Note that an essential difference between the two approaches is that binary segmentation is backward looking while the moving window can be used as "a real time" tool.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Dependence Modeling in financial time series

Dou,

Liu,

Aivaliotis

2019

Preprint

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This paper explores the dependence modeling of financial assets in a dynamic way and its critical role in measuring risk. Two new methods, called Accelerated Moving Window method and Bottom-up method are proposed to detect the change of copula. The performance of these two methods together with Binary Segmentation [31] and Moving Window method [17] is compared based on simulated data. The best-performing method is applied to Standard & Poor 500 and Nasdaq indices. Value-at-Risk and Expected Shortfall are computed from the dynamic and the static model respectively to illustrate the effectiveness of the best method as well as the importance of dynamic dependence modeling through backtesting.

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Section: Moving Windowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Dependence Modeling in financial time series

Dou,

Liu,

Aivaliotis

2019

Preprint

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“…While copula function provides an effective solution to deal with the nonlinear dependences of multiple variables. A copula is generally used to describe the joint distribution and correlation structure of asset portfolios, and it has also been used to improve the accuracy of risk measurement for portfolio (Guegan & Cyril, 2004;Linsmeier & Pearson, 2000;Romano, 2015). In this article, we regard the intraday returns at each moment of the day as different variables, and we intend to improve the HF-based model for risk measurement by exploring the nonlinear dependences of intraday returns through copula.…”

Section: Introductionmentioning

confidence: 99%

A new model for forecasting VaR and ES using intraday returns aggregation

Song

2022

Journal of Forecasting

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This paper proposes a new risk measurement model that directly incorporate information from high‐frequency data to predict daily Value‐at‐Risk and expected shortfall. In this model, Regular‐Vine copula and Monte Carlo simulation are applied to produce the predicted intraday returns that have nonlinear dependences. And the time‐varying marginal distribution of intraday returns is estimated under the framework of generalized autoregressive score. The model is so named R‐Vine‐Copula‐GAS (abbreviated as RVCGAS). The predicted intraday returns in the same day are added up to obtain the daily return and form its empirical distribution. The risk measurements are then calculated based on this simulated distribution. An empirical analysis is conducted using data from the index in China's stock market and some of its constituents, and the effects of the proposed model and several Benchmark models are compared through some backtestings. The results show that RVCGAS has an advantage over others in predicting tail risk when the risk level is high, since it could cover more risky returns and reduce more costs.

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The Emotion Magnitude Effect: Navigating Market Dynamics Amidst Supply Chain Events

McCarthy,

Alaghband

2023

JRFM

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During the volatile market period of 2019–2021, characterized by geopolitical shifts, economic sanctions, pandemics, natural disasters, and wars, the global market presented a complex landscape for financial decision making and motivated this study. This study makes two groundbreaking and novel contributions. First, we augment Plunket’s emotional research and leverage the emotional classification algorithm in Fin-Emotion to introduce a novel quantitative metric, “emotion magnitude”, that captures the emotional undercurrents of the market. When integrated with traditional time series analysis using Temporal Convolutional Networks applied to stock market futures, this metric offers a more holistic understanding of market dynamics. In our experiments, incorporating it as a feature led to significantly better performance on both the training and validation sets (9.26%, 52.11%) compared to traditional market-based risk measures, in predicting futures market trends based on the commodities and supply chains analyzed. Second, we deploy a multidimensional data science framework that synthesizes disparate data streams and analyses. This includes stock metrics of sector-leading companies, the time horizon of significant market events identified based on company stock data, and the extraction of further knowledge concepts identified through “emotion magnitude” analysis. Our approach stitches together countries, commodities, and supply chains identified in the targeted news search and identifies the domestic companies impacted based on the time horizon of these emotional supply chain events. This methodology culminates in a unified knowledge graph that not only highlights the relationships between supply chain disruptions, affected corporations, and commodities but also quantifies the broader systemic implications of such market events that are revealed. Collectively, these innovations form a robust analytical tool for financial risk strategy, empowering stakeholders to navigate an ever-evolving financial global ecosystem with enhanced insights. This graph encapsulates multi-dimensional forces and enables stakeholders to anticipate and understand the broader causal implications of related supply chain and market events (such as economic sanctions’ impact on the energy, technology, and telecommunication sectors).

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Forecasting VaR and Expected Shortfall using Dynamical Systems: A Risk Management Strategy

Cited by 3 publications

References 19 publications

Dynamic Dependence Modeling in financial time series

Dynamic Dependence Modeling in financial time series

A new model for forecasting VaR and ES using intraday returns aggregation

The Emotion Magnitude Effect: Navigating Market Dynamics Amidst Supply Chain Events

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