A New Heuristic Measure of Fragility and Tail Risks: Application to Stress Testing

Taleb, Nassim Nicholas; Canetti, Elie; Kinda, Tidiane; Loukoianova, Elena; Schmieder, Christian

doi:10.2139/ssrn.2156095

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…In financial markets, the price of an option gets more affected with less likely to happen disruptions that have a higher impact, in other words, "Black Swan" type of events, compared to the cumulative effect of small disruptions occurring with high frequency [15]. Assuming that the frequency of those disruptive events can be modeled with a distribution, the tail parts matter the most rather than the mean part for analyzing the sensitivity of a system.…”

Section: Fragility and Antifragility Of A Networkmentioning

confidence: 99%

“…First, different network attacking strategies are considered to see which elements of the network are the main contributors to determining the fragile point of the built AAM traffic. Then, the simple heuristic measure that is defined in [14], [15] for tail risks is used to understand if our AAM network has a tendency to be fragile or antifragile.…”

Section: Fragility / Antifragility Analysismentioning

confidence: 99%

“…Last but not least, fragility and antifragility concepts are defined and detailed in [13], [14], which will be the main focus of this paper. In [15], the fragility concept and tail risks are quantified and applied to bank stress testing and public debt cases in finance. The mathematical breakdown for the considered ideas is provided in [16].…”

Section: Introductionmentioning

confidence: 99%

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Analyzing Fragility of the Advanced Air Mobility System and Exploring Antifragile Networks

Altun,

Xu,

Inalhan

et al. 2023

2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC)

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Future Advanced Air Mobility (AAM) is a concept that envisions to transform the current air transportation system into a more agile, flexible, and accessible system. Yet, the considered transformation and integrated system is not easy to achieve since it involves providing a high level of safety as well as efficiency. For that purpose, in this paper, we explored the fragility and antifragility concepts to analyze the AAM traffic network and provide an understanding of a system where it can benefit even under adverse conditions such as contingency events. For the analysis, first, a complex AAM traffic network is built via various AAM vehicles and possible vertiport locations that are analyzed for the Northern California area. After that, the AAM network is modeled via queue theory to simulate the considered flight plans, obtain the actual departure and arrival times under different conditions, and observe the delay propagation. Then, metrics from network theory based on targeted node and edge removals are studied to analyze the fragility of the AAM network and used for antifragility analysis. The methodology is used to analyze different disruptive cases over an AAM network such that disruptions at vertiports and over origin-destination pairs. Finally, an analysis of making the considered traffic antifragile through flight cancellations and its trade-off based on flight cancellation costs is provided.

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Section: Fragility and Antifragility Of A Networkmentioning

confidence: 99%

Section: Fragility / Antifragility Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analyzing Fragility of the Advanced Air Mobility System and Exploring Antifragile Networks

Altun,

Xu,

Inalhan

et al. 2023

2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC)

View full text Add to dashboard Cite

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“…Taleb's ideas have been further explored in different contexts not only in risk analysis and financial systems [2][3][4], but also as a means for strategic design and planning [5][6][7]. The original formal definition [8] (see also [9,10]), however, is difficult to use in practice and with real data. Therefore, we use in this paper an alternative definition of antifragility that can be implemented easily for a quantitative analysis and is general enough to be applied in a broad range of domains.…”

Section: Introductionmentioning

confidence: 99%

Stocks and cryptocurrencies: Antifragile or robust? A novel antifragility measure of the stock and cryptocurrency markets

2023

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In contrast with robust systems that resist noise or fragile systems that break with noise, antifragility is defined as a property of complex systems that benefit from noise or disorder. Here we define and test a simple measure of antifragility for complex dynamical systems. In this work we use our antifragility measure to analyze real data from return prices in the stock and cryptocurrency markets. Our definition of antifragility is the product of the return price and a perturbation. We explore different types of perturbations that typically arise from within the system. Our results suggest that for both the stock market and the cryptocurrency market, the tendency among the ‘top performers’ is to be robust rather than antifragile. It would be important to explore other possible definitions of antifragility to understand its role in financial markets and in complex dynamical systems in general.

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“…This approach was used in stress testing by the International Monetary Fund (IMF), where the degree of concavity in the tail was used as an indicator of the severity of tail exposure, see Taleb, Canetti et al [35]. Such a method can transfer to medicine as the convexity of the dose response can be estimated via titration applied to Equation (1).…”

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confidence: 99%

Working with Convex Responses: Antifragility from Finance to Oncology

Taleb

West

2023

Entropy

Self Cite

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We extend techniques and learnings about the stochastic properties of nonlinear responses from finance to medicine, particularly oncology, where it can inform dosing and intervention. We define antifragility. We propose uses of risk analysis for medical problems, through the properties of nonlinear responses (convex or concave). We (1) link the convexity/concavity of the dose-response function to the statistical properties of the results; (2) define “antifragility” as a mathematical property for local beneficial convex responses and the generalization of “fragility” as its opposite, locally concave in the tails of the statistical distribution; (3) propose mathematically tractable relations between dosage, severity of conditions, and iatrogenics. In short, we propose a framework to integrate the necessary consequences of nonlinearities in evidence-based oncology and more general clinical risk management.

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A New Heuristic Measure of Fragility and Tail Risks: Application to Stress Testing

Cited by 9 publications

References 5 publications

Analyzing Fragility of the Advanced Air Mobility System and Exploring Antifragile Networks

Analyzing Fragility of the Advanced Air Mobility System and Exploring Antifragile Networks

Stocks and cryptocurrencies: Antifragile or robust? A novel antifragility measure of the stock and cryptocurrency markets

Working with Convex Responses: Antifragility from Finance to Oncology

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