Financial Risk Measurement for Turkish  Insurance Companies Using VaR Models

Yıldırım, İsmail

doi:10.4236/jfrm.2015.43013

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…The requirements for the building of a model of risk management for an insurance company's functioning with the use of a prediction approach are: Babbel (1977), Yildirim (2015) The model should include and describe the main risk characteristics of an insurance company.…”

Section: Risk Assessmentmentioning

confidence: 99%

Predictive Analytics and Risk Management of Property Insurance

Amirhosseini,

Vanani,

Taghavifard

et al. 2023

Preprint

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Claim prediction plays a leading role in risk management for insurance companies. This research proposes a risk management model by evaluating property policy's risk and classifying risk levels to reduce uncertainty for decision-makers. The approach consists of three phases. Phase I is claims prediction; It integrates three claim predictions: occurrence probability, claim severity, and time of loss; we use machine learning for each prediction. Algorithms have been implemented on about 2 million records of a large insurance company between 2011 and 2022. Results showed that the best algorithm is a deep neural network. In Phase II, risk metric results are calculated to determine the risk level score; we present the prediction results into a quadrant risk-grouped associated measure. Phase III is risk management. The risk level score is linked to the decision-makers action list to keep, reject or transfer the risk of each insurance policy. The final model is a risk management tool combining risk prediction metrics and the risk matrix. To assess the efficacy of this new model, 500 sample records were provided to the risk management system, and its output was compared with that of expert opinion. 72% correct matching indicates the accuracy of the model.

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Section: Risk Assessmentmentioning

confidence: 99%

Predictive Analytics and Risk Management of Property Insurance

Amirhosseini,

Vanani,

Taghavifard

et al. 2023

Preprint

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“…VaR is a metric used to quantify the market risk of an investment portfolio, measuring the potential loss in value in the event of adverse changes in asset prices (Culp et al, 1998;Yildirim, 2015;Pearson, 2002). Unlike other standard measures such as standard deviation or beta (Racicot & Théoret, 2006: p. 470),…”

Section: Value At Risk and Expected Shortfall: Risk Budgeting Toolsmentioning

confidence: 99%

“…In the realm of finance, the concept of risk gained importance with the emergence of Markowitz's theory in the 1950s, which was followed by other measures such as Beta, the Capital Asset Pricing Model (CAPM), and multifactor models including the Arbitrage Pricing Theory (APT) and the three-factor profitability model (Pierandrei, 2019). Financial institutions have also adopted techniques like risk budgeting (Chow et al, 2001;Yildirim, 2015), which helps define the acceptable level of risk beforehand and facilitates better risk management at all levels of strategic, tactical, and operational decision-making.…”

Section: Introductionmentioning

confidence: 99%

Risk Budgeting: A Tactical Asset Allocation Approach for Retirement Reserve Funds in Morocco

Kabiri¹,

Msiyah²,

Nouisser³

2023

JFRM

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This paper aims to suggest a risk budgeting approach that can enhance the asset allocation process of retirement reserve funds. The approach takes into consideration the importance of incorporating risk into tactical decisions to improve fund performance. The significance of this study lies in the need to adopt a strong approach that enables asset managers to outperform strategic asset allocation while avoiding excessive risk. The article proposes a methodology for measuring and allocating market risk in investment decisions, through the computation and decomposition of Value at Risk and Expected Shortfall. Additionally, it introduces a novel scientific method of using Expected Shortfall and its decomposition into marginal units to guide shortterm tactical decision-making.

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“…-studies that address the deficiencies of VaR legislation (Vasileiou (2016)) and the procyclicality caused by VaR legislation (Adrian and Shin (2014), Vasileiou and Pantos (2020)), and -studies that try to apply advanced econometric models for more accurate VaR estimations: extreme value theory (GARCH family models (Engle (2004), Assaf (2009), Diamandis et al (2011)), Markov Switching Regime (Billio and Pelizzon (2000)), data filtering models (Vasileiou (2017(Vasileiou ( , 2019, Extreme Learning Machine (Zhang et al (2017)) etc.. The importance of the TRY risk has been documented in several studies: Günay (2017), Yildirim (2015), Gün (2020) etc.. In this study, we examine conventional VaR models that are usually applied in the financial industry, such as the Historical, Variance Covariance, Exponential Weighted Moving Average1, and the widely applied GARCH model.…”

Section: Introductionmentioning

confidence: 99%

Do econometric advances lead to more accurate VaR estimations when the legislation requirements are applied? The case of Turkish Lira FX Market.

Vasileiou

Rizopoulos

2023

ICBE-HOU

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This paper empirically tests the benefits of econometric advances in the field of Value at Risk (VaR) within the existing legal framework that regulates the Banking (Basel III) and Asset Management industry (CESR (2010)). We test several VaR models, from the simplest and the most easily applied, i.e. the Historical VaR (HVaR), Variance Covariance VaR (VCVaR), and the Exponential Weighted Moving Average VaR (EWMAVaR), to advanced models such as GARCH(1,1). We test these models by examining the extremely volatile FX market of the Turkish Lira (TRY) and we evaluate the models according to the criteria set by the legal framework on VaR. The empirical findings suggest that the HVaR model is a very reliable model if the goal of a risk manager is to satisfy the legislation requirements. However, we should mention that models that make use advances in econometrics lead to more accurate and more representative VaR estimations than the HVaR. The law should give incentives to adopt more representative models in the financial industry in order to take advantage of advances in financial modeling. JEL Classification: C53; G15 Keywords: Value-at-risk; GARCH estimation; Historical VaR; Variance Covariance; Exponential Weighted Moving Average VaR; Back-testing; Legislation; FX Market

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Financial Risk Measurement for Turkish Insurance Companies Using VaR Models

Cited by 4 publications

References 10 publications

Predictive Analytics and Risk Management of Property Insurance

Predictive Analytics and Risk Management of Property Insurance

Risk Budgeting: A Tactical Asset Allocation Approach for Retirement Reserve Funds in Morocco

Do econometric advances lead to more accurate VaR estimations when the legislation requirements are applied? The case of Turkish Lira FX Market.

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