Shortfall deviation risk: an alternative for risk measurement

Righi, Marcelo Brutti; Ceretta, Paulo Sergio

doi:10.21314/jor.2016.349

Cited by 38 publications

(7 citation statements)

References 71 publications

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“…Thus, SDR takes into consideration the tails of a distribution which stand for the worst values. Moreover, Righi and Ceretta (2016) conclude in SDR being a more fitting risk measure than VaR and ES when scenarios of increased riskiness are present. Finally, we recommend that an educational program could take place, as proposed by Linciano et al (2018) to keep investors in line with the new KID introduced by the regulatory bodies.…”

Section: Conclusion Limitations and Further Researchmentioning

confidence: 90%

“…Additionally, we encourage that a similar research could be applied in UL products of other countries-members of the EU that are in financial distress. Moreover, we suggest that the shortfall deviation risk (SDR) methodology of Righi and Ceretta (2016) can be applied and test whether it is a more appropriate model for market risk assessment than VaR and ES in periods of financial crisis. SDR merges two risk notions: the possibility of poor results (ES) and the volatility of a potential outcome (SD).…”

Section: Conclusion Limitations and Further Researchmentioning

confidence: 99%

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Market risk assessment

Kokoris

Archontakis

Grose

2020

JRF

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Purpose This study aims to examine whether the methodology proposed by the European Supervisory Authorities (ESAs) within Delegated Regulation (European Union) 2017/653 for the calculation of market risk of certain packaged retail and insurance-based investment products (PRIIPs) is the most appropriate. Design/methodology/approach Risk models are put into effect to validate the appropriateness of the methodology announced by ESAs. ESAs have announced that the unit-linked (UL) products, labeled as Category II PRIIPs, will be subject to the Cornish–Fisher value-at-risk (CFVaR) methodology for their market risk assessment. We test CFVaR at 97.5% confidence level on 70 UL products, and we test Cornish–Fisher expected shortfall (CFES) at the same confidence level, which acts as a counter methodology for CFVaR. Findings The paper provides empirical insights about the Cornish-Fisher (CF) expansion being a method that incorporates the possibility of financial instability. When CFVaR by ESAs is calculated, it is shown that CF is in general a more robust risk model than the simpler historical ones. However, when CFES is applied, important points are derived. First, only in half of the occasions the CF expansion can be considered as a reliable method. Second, the CFES is a more coherent risk measure than CFVaR. We conclude that the CF expansion is unable to accurately estimate the market risk of UL products when excessive fat-tailed or non-symmetrical distributions are present. Hence, we suggest that a different methodology could also be considered by the regulatory bodies which will capture the excessive values of products in financial distress. Originality/value Literature, both theoretical and applied, regarding PRIIPs, is not extended. Although business and regulators research has begun to intensify in the last two years, to our knowledge this is one of the first studies that uses the CFES methodology for market risk assessment of Category II PRIIPs. In addition, we use a unique data set from a country in the headwinds of the recent financial crisis. This research contributes both to the academic and business community by enriching the existing literature and aiding risk managers in assessing the market risk of certain Category II PRIIPs. Considering the recent efforts of the regulatory authorities at the beginning of 2020 to implement certain amendments to the PRIIPs, we indicate relative risks related with the calculation of the market risk of the aforementioned products. Our findings could contribute to regulatory authorities’ persistent efforts in wrapping up this ongoing project.

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Section: Conclusion Limitations and Further Researchmentioning

confidence: 90%

Section: Conclusion Limitations and Further Researchmentioning

confidence: 99%

Market risk assessment

Kokoris

Archontakis

Grose

2020

JRF

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“…However, the variability concept, which is one of the pillars of the concept of risk, is ignored in this definition. Righi and Ceretta (2016) propose a new measure of risk called the Shortfall Deviation Risk (SDR), which can be defined as the ES, which is penalized by the dispersion of results that represent losses greater than ES. In addition to its concrete practical definition, SDR possesses solid theoretical properties that ensure that it can be used without violating axiomatic assumptions.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, in this study we use four measures of risk, and their formulations appear below (1). These definitions have been adapted from Righi and Ceretta (2016). Let X signify the daily log-returns for each analyzed market.…”

Section: Methodsmentioning

confidence: 99%

The Relationship between Sentiment and Risk in Financial Markets

Paraboni

Righi

Vieira

et al. 2018

BAR, Braz. Adm. Rev.

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This article estimates association coefficients between measures of market sentiment and risk in the U.S., German and Chinese markets. In terms of risk, four measures were considered: standard deviation, value at risk, expected shortfall and shortfall deviation risk. For market sentiment, data was collected using the Psych Signal technology, which is based on the behavior of investors on social networks. The results indicate significant statistical associations, with the direction of association having financial meaning. Moreover, the empirical findings are valid for all risk measurements. The results are in keeping with the Prospect Theory, since in moments when the sentiment indicates low liquidity (a negative value for the difference between Bullish and Bearish Intensities) investors try to reduce the negotiation volume, which has a positive impact on risk. On the other hand, under the inverted scenario, when sentiment indicates high liquidity, there is an increase in the negotiation volume and a consequent decrease in risk. This article is important because its observations of market sentiment as measured by social media data show a consistent relationship with measures of financial risk.

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“…Furthermore, we consider risk-averse reinforcement learning, whose risk is assumed to come from these uncertainties. We regard the negative of total discounted future reward as loss and use the risk-measure method Conditional Value at Risk (CVaR) (Righi and Ceretta, 2016). It is a variant of Value at Risk (VaR) (Mansour and Abdel-Rahman, 1984) and also recently identified as suitable for measuring risk in robotics (Majumdar and Pavone, 2020).…”

Section: Preliminariesmentioning

confidence: 99%

Risk-Aware Model-Based Control

Chen

Rosendo

2021

Front. Robot. AI

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Model-Based Reinforcement Learning (MBRL) algorithms have been shown to have an advantage on data-efficiency, but often overshadowed by state-of-the-art model-free methods in performance, especially when facing high-dimensional and complex problems. In this work, a novel MBRL method is proposed, called Risk-Aware Model-Based Control (RAMCO). It combines uncertainty-aware deep dynamics models and the risk assessment technique Conditional Value at Risk (CVaR). This mechanism is appropriate for real-world application since it takes epistemic risk into consideration. In addition, we use a model-free solver to produce warm-up training data, and this setting improves the performance in low-dimensional environments and covers the shortage of MBRL’s nature in the high-dimensional scenarios. In comparison with other state-of-the-art reinforcement learning algorithms, we show that it produces superior results on a walking robot model. We also evaluate the method with an Eidos environment, which is a novel experimental method with multi-dimensional randomly initialized deep neural networks to measure the performance of any reinforcement learning algorithm, and the advantages of RAMCO are highlighted.

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Shortfall deviation risk: an alternative for risk measurement

Cited by 38 publications

References 71 publications

Market risk assessment

Market risk assessment

The Relationship between Sentiment and Risk in Financial Markets

Risk-Aware Model-Based Control

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