Stress Tests, Maximum Loss, and Value at Risk

Breuer, Thomas; Krenn, Gerald; Pistovčák, Filip

doi:10.1007/978-3-642-57492-4_21

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…The lack of sub-additivity can give rise to regulatory arbitrage in the sense if capital requirements are based on VaR, a firm could create artificial subsidiaries in order to save on regulatory capital. However, to guarantee sub-additivity and hence to restore the risk coherence of VaR, Breuer et al (2002) of parametric VaR, the portfolio value must be a linear function of risk factors whose changes are elliptically distributed. Classical examples of elliptical distributions are multivariate normal and Student-t distributions.…”

Section: Implications For Value-at-risk Estimationmentioning

confidence: 99%

Evidence of non-stationary bias in scaling by square root of time: Implications for Value-at-Risk

Saadi

Rahman

2008

Journal of International Financial Markets, Institutions and Mo

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Section: Implications For Value-at-risk Estimationmentioning

confidence: 99%

Evidence of non-stationary bias in scaling by square root of time: Implications for Value-at-Risk

Saadi

Rahman

2008

Journal of International Financial Markets, Institutions and Mo

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“…MacMinn (2002) expanded the results of Stulz (1996) and Adam (2002) and mentioned the that the responsibilities of management are to identify and manage pertinent financial risks to enhance the shareholder wealth of investors. Breuer et al (2002) pointed out the lapses of management to overlook the inherent off-balance-sheet on financial risks of investments resulting from complications from the financial markets. Cash equivalents lost during financial crises within an economy has negative trickle-down effects on returns of long-term investments such as PPP projects.…”

Section: Theoretical Framework and Hypothesesmentioning

confidence: 99%

Managing financial risks to improve financial success of public—private partnership projects: a theoretical framework

Akomea-Frimpong

Jin

Osei‐Kyei

2021

JFM

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Purpose Successful execution of public–private partnership (PPP) projects is the most desirable outcome to all stakeholders. Previous studies show that one of the topmost obstacles to fulfil this desire on the project is financial risks. Nonetheless, inadequate holistic studies exist on linking the management of this challenge to the financial returns of the project. This study aims to develop a theoretical framework interrelating financial risks, financial controls and financial performance of PPP projects. Design/methodology/approach The theoretical framework is informed and supported by existing theories and previous empirical studies from construction management, finance and economics. The underlying theories captured in the framework were chosen for their relevance and applicability to PPP projects. The propositions developed from the analysis of the theories and the empirical literature are summarised in three main hypotheses and 26 operationalised sub-hypotheses. Findings The major elements of the framework include the financial risks and 12 sub-themes which are commonly experienced on PPP projects. Financial policies and procedures on controlling financial losses of the projects are also included in the framework. Lastly, this study creates financial criteria on the projects which are intrinsically embedded in the framework to serve as benchmark to support the measurement of financial success. Research limitations/implications This study is a theoretical review of classical theories and empirical studies, and therefore, not all researches and managerial controls have not been included in this framework due to restricted time and limited studies on the topic. Practical implications This paper would serve as a multidimensional guide to project managers to mitigate financial risks and hopefully enhance the financial success of PPPs. Theoretically, this paper outlines the dimensions of managing financial risks of PPPs that require valid and reliable measurement to test the interrelationships of the constructs by further studies in the construction research community. Originality/value This theoretical framework makes ambitious efforts to embrace multifaceted theories from different disciplines to shed light on holistic mechanisms to mitigate financial risks to improve financial returns of PPP projects.

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“…For non‐linear portfolios mean‐VaR, mean‐ES and mean‐MaxLoss optimisation are not equivalent. For a non‐linear option portfolio Breuer et al (2002) show the efficiency of risk‐return management based on MaxLoss.…”

Section: Intuitive Risk‐return Management With Worst Case Analysismentioning

confidence: 99%

Providing against the worst

Breuer

2006

Managerial Finance

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PurposeMaximum Loss was one of the risk measures proposed as alternatives to Value at Risk following criticism that Value at Risk is not coherent. Although the power of Maximum Loss is recognised for non‐linear portfolios, there are arguments that for linear portfolios Maximum Loss does not convey more information than Value at Risk. This paper argues for the usefulness of Maximum Loss for both linear and non‐linear portfolios.Design/methodology/approachThis is a synthesis of existing theorems. Results are established by means of counterexamples. The worst case based risk‐return management strategy is presented as a case study.FindingsFor linear portfolios under elliptic distributions Maximum Loss is proportional to Value at Risk, and to Expected Shortfall, with the proportionality constant not depending on the portfolio composition. The paper gives a new example of Value at Risk violating subadditivity, using a portfolio of simple European options. For non‐linear portfolios, Maximum Loss need not even approximately be explained by the sum of Maximum Loss contributions of the individual risk factors. Finally, is proposed a strategy of risk‐return management with Maximum Loss.Research limitations/implicationsThe paper is restricted to elliptically distributed risk factors. Although Maximum Loss can be defined for more general continuous and even discrete distributions of risk factor changes, the paper does not address this matter.Practical implicationsThe paper proposes an intuitive, computationally easy way how to improve average returns of linear portfolios while reducing worst case losses.Originality/valueOne is a synthesis of existing theorems. The counterexample establishing result is the first example of a portfolio of plain vanilla options violating Value at Risk subadditivity, an effect hitherto only known for portfolios of exotic options. Furthermore, the strategy of risk‐return management with Maximum Loss is original.

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Stress Tests, Maximum Loss, and Value at Risk

Cited by 7 publications

References 3 publications

Evidence of non-stationary bias in scaling by square root of time: Implications for Value-at-Risk

Evidence of non-stationary bias in scaling by square root of time: Implications for Value-at-Risk

Managing financial risks to improve financial success of public—private partnership projects: a theoretical framework

Providing against the worst

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