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Risk Management in Insurance and Fin...

Yang, Yunshen,

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Risk Management in Insurance and Finance in Response to Model Uncertainty /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Risk Management in Insurance and Finance in Response to Model Uncertainty // Yunshen Yang.
Author:	Yang, Yunshen,
Description:	1 electronic resource (276 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-08, Section: A.
Contained By:	Dissertations Abstracts International86-08A.
Subject:	Probability. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31864638
ISBN:	9798304907521

Risk Management in Insurance and Finance in Response to Model Uncertainty /
Yang, Yunshen,

Risk Management in Insurance and Finance in Response to Model Uncertainty /Yunshen Yang. - 1 electronic resource (276 pages)

Source: Dissertations Abstracts International, Volume: 86-08, Section: A.

The world we live in is rapidly evolving and full of complexity, compounded by emerging threats due to climate change and shocks such as COVID-19. These factors impose existential challenges on the insurance and financial industries, calling for refined risk management tools. This thesis focuses on one specific challenge, model uncertainty, a notorious issue that is pervasive in virtually all applied fields and is exacerbated in this changing environment. I endeavour to develop a series of approaches to address the issue of model uncertainty in risk assessment, portfolio management, and financial decision-making.First, consider a general problem of estimating the expectation of a certain loss function, where the underlying probability distribution is unknown. The objective is subject to an ambiguity issue, for which the worst-case treatment has been developed in the literature but the resulting estimates are often too conservative. We propose a scenario analysis and develop a new worst-case treatment. As an illustration, we derive worst-case moment estimates analytically. Our numerical results show that our approach indeed greatly alleviates the over-conservativeness issue.Next, we examine large investment portfolios that are crucially important for social and economic security. Consider a portfolio exposed to multilevel risks, including idiosyncratic risk, systematic risk, and common shock. In this changing environment, we argue that common shock and systematic risk, which are usually assumed to be independent in the literature, may interplay with each other, forming a causal loop and leading to a joint extreme scenario. An asymptotic study of the portfolio loss due to defaults is carried out under a joint regular variation structure of common shock and systematic risk. Our main finding is that the tail dependence between these two risk factors, besides their marginal tails, is another driving force in the portfolio loss.Lastly, we investigate how humans make financial forecasts under Knightian uncertainty. Our hypothesis posits that a little information nudge can empower humans to surmount Knightian uncertainty independently, thereby greatly improving their performance. To test this, we design an innovative experiment in which participants forecast structural shifts in the market, and then compare the results from three experimental treatments with varying degrees of information nudges. The data collected so far strongly support our hypothesis.

English

ISBN: 9798304907521Subjects--Topical Terms:

518898
Probability.
Subjects--Index Terms:

COVID-19

Risk Management in Insurance and Finance in Response to Model Uncertainty /
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245 1 0 $a Risk Management in Insurance and Finance in Response to Model Uncertainty / $c Yunshen Yang.
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500 $a Source: Dissertations Abstracts International, Volume: 86-08, Section: A.
500 $a Advisors: Tang, Qihe; Wong, Bernard; Payzan-LeNestour, Elise; Avanzi, Benjamin.
502 $b Ph.D. $c University of New South Wales (Australia) $d 2024.
520 $a The world we live in is rapidly evolving and full of complexity, compounded by emerging threats due to climate change and shocks such as COVID-19. These factors impose existential challenges on the insurance and financial industries, calling for refined risk management tools. This thesis focuses on one specific challenge, model uncertainty, a notorious issue that is pervasive in virtually all applied fields and is exacerbated in this changing environment. I endeavour to develop a series of approaches to address the issue of model uncertainty in risk assessment, portfolio management, and financial decision-making.First, consider a general problem of estimating the expectation of a certain loss function, where the underlying probability distribution is unknown. The objective is subject to an ambiguity issue, for which the worst-case treatment has been developed in the literature but the resulting estimates are often too conservative. We propose a scenario analysis and develop a new worst-case treatment. As an illustration, we derive worst-case moment estimates analytically. Our numerical results show that our approach indeed greatly alleviates the over-conservativeness issue.Next, we examine large investment portfolios that are crucially important for social and economic security. Consider a portfolio exposed to multilevel risks, including idiosyncratic risk, systematic risk, and common shock. In this changing environment, we argue that common shock and systematic risk, which are usually assumed to be independent in the literature, may interplay with each other, forming a causal loop and leading to a joint extreme scenario. An asymptotic study of the portfolio loss due to defaults is carried out under a joint regular variation structure of common shock and systematic risk. Our main finding is that the tail dependence between these two risk factors, besides their marginal tails, is another driving force in the portfolio loss.Lastly, we investigate how humans make financial forecasts under Knightian uncertainty. Our hypothesis posits that a little information nudge can empower humans to surmount Knightian uncertainty independently, thereby greatly improving their performance. To test this, we design an innovative experiment in which participants forecast structural shifts in the market, and then compare the results from three experimental treatments with varying degrees of information nudges. The data collected so far strongly support our hypothesis.
546 $a English
590 $a School code: 0423
650 4 $a Probability. $3 518898
650 4 $a Probability distribution. $3 3562293
650 4 $a Climate change. $2 bicssc $3 2079509
650 4 $a Pandemics. $3 3464080
650 4 $a COVID-19. $3 3554449
650 4 $a Finance. $3 542899
653 $a COVID-19
653 $a Risk management tools
690 $a 0404
690 $a 0508
710 2 $a University of New South Wales (Australia). $e degree granting institution. $3 3815488
720 1 $a Tang, Qihe $e degree supervisor.
720 1 $a Wong, Bernard $e degree supervisor.
720 1 $a Payzan-LeNestour, Elise $e degree supervisor.
720 1 $a Avanzi, Benjamin $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-08A.
790 $a 0423
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31864638