Value-at-risk under ambiguity aversion

Agliardi, Rossella

doi:10.1186/s40854-018-0095-z

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…For instance, the researchers aim to establish a financial stability measurement system and comparisons of different regulation mechanisms, and so on (Cao & Illing, 2010;Ashraf, Rizwan, & L'Huillier, 2016, etc). Choi, Chan, and Yue, 2017;Campbellverduyn, Goguen, and Porter, 2017;Flood, Jagadish, Kyle, Olken, and Raschid, 2011;Cui, 2015;Dong, Yang, and Tian, 2015;Smailović, Grčar, Lavrač, and Žnidaršič, 2014;Sarlin, 2016a;Sarlin, 2016b;Cerchiello, Giudici, and Nicola, 2016;Brunnermeier and Pedersen, 2009;Agliardi, 2018. Policies and laws of financial regulations.…”

Section: The Classification Of Research Objectsmentioning

confidence: 99%

Machine Learning Methods for Systemic Risk Analysis in Financial Sectors

Kou

Chao

Alsaadi

et al. 2019

Technological and Economic Development of Economy

265

122

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Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.

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Section: The Classification Of Research Objectsmentioning

confidence: 99%

Machine Learning Methods for Systemic Risk Analysis in Financial Sectors

Kou

Chao

Alsaadi

et al. 2019

Technological and Economic Development of Economy

265

122

View full text Add to dashboard Cite

show abstract

“…Some authors deal with alternative set of priors, mainly related to the value of some parameters, and they verify the sensitivity of their results with respect to the ambiguity about these parameters . We can also analyze the robustness of our results with respect to the selected values u = 1/2 and U = 3/2.…”

Section: Examplesmentioning

confidence: 65%

V@R representation theorems in ambiguous frameworks

Balbás

Charron

2019

Appl Stoch Models Bus & Ind

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The value at risk (V@R) is a very important risk measure with significant applications in finance (risk management, pricing, hedging, portfolio theory, etc), insurance (premium principles, optimal reinsurance, etc), production, marketing (newsvendor problem), etc. It also plays a critical role in regulation about risk (Basel, Solvency, etc), it is very appreciated by practitioners due to its intuitive interpretation, and it is the unique popular risk measure remaining finite for heavy tailed risks with unbounded expectation. Besides, ambiguous frameworks are becoming more and more usual in applications of risk analysis. Lack of data or committed errors may provoke discrepancies between real probabilities and estimated ones. This paper combines both V@R and ambiguous settings, and a new representation theorem for V@R is given. Consequently, inspired by previous studies dealing with coherent risk measures and their representation, we will give new methods to compute and optimize V@R under ambiguity. This seems to be a relevant finding because the analytical properties of V@R are very weak if one compares with a coherent risk measure. Indeed, V@R is neither continuous nor convex, which makes it very complicated to deal with it in mathematical approaches. Nevertheless, the results of this paper will allow us to transform computation and optimization problems involving V@R into continuous and differentiable problems. KEYWORDSambiguity, heavy tail, representation theorem, value at risk INTRODUCTIONRisk measures beyond the standard deviation are becoming more and more important in many applications of risk analysis. Indeed, most of them can be interpreted in terms of potential capital losses, while the standard deviation does not satisfy this property in many situations. Besides, the standard deviation is not consistent with the second-order stochastic dominance and the standard utility functions in the presence of asymmetries, 1 and this drawback is overcome by other risk measures.Among many other examples, the coherent risk measures 2 and the expectation bounded risk measures 3 are very popular for practitioners and researchers since they have good analytical properties such as subadditivity, convexity, and continuity, making it feasible to deal with them in complex mathematical approaches. Important examples of such measures are the conditional value at risk (CV@R) and the weighted conditional value at risk. 3 With respect to the coherent and expectation bounded risk measures above, the value at risk (V@R) presents several shortcomings. Indeed, it is neither continuous nor convex, making it complex many analytical studies. Nevertheless, V@R 414

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“…Our study incorporates price diffusion, volatility diffusion, and jump ambiguity into a single framework. In addition, Agliardi ( 2018 ) explores ambiguity in the calculation of Value-at-Risk in terms of capital requirement.…”

Section: Introductionmentioning

confidence: 99%

Dynamic portfolio choice with uncertain rare-events risk in stock and cryptocurrency markets

Pang

Xia

et al. 2023

Financ Innov

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In response to the unprecedented uncertain rare events of the last decade, we derive an optimal portfolio choice problem in a semi-closed form by integrating price diffusion ambiguity, volatility diffusion ambiguity, and jump ambiguity occurring in the traditional stock market and the cryptocurrency market into a single framework. We reach the following conclusions in both markets: first, price diffusion and jump ambiguity mainly determine detection-error probability; second, optimal choice is more significantly affected by price diffusion ambiguity than by jump ambiguity, and trivially affected by volatility diffusion ambiguity. In addition, investors tend to be more aggressive in a stable market than in a volatile one. Next, given a larger volatility jump size, investors tend to increase their portfolio during downward price jumps and decrease it during upward price jumps. Finally, the welfare loss caused by price diffusion ambiguity is more pronounced than that caused by jump ambiguity in an incomplete market. These findings enrich the extant literature on effects of ambiguity on the traditional stock market and the evolving cryptocurrency market. The results have implications for both investors and regulators.

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Value-at-risk under ambiguity aversion

Cited by 6 publications

References 17 publications

Machine Learning Methods for Systemic Risk Analysis in Financial Sectors

Machine Learning Methods for Systemic Risk Analysis in Financial Sectors

V@R representation theorems in ambiguous frameworks

Dynamic portfolio choice with uncertain rare-events risk in stock and cryptocurrency markets

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