Enhancing Value-at-Risk with Credible Expected Risk Models

Khreshna Syuhada (), Rizka Puspitasari, I Kadek Darma Arnawa, Lailatul Mufaridho, Elonasari Elonasari, Miftahul Jannah and Aniq Rohmawati
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Khreshna Syuhada: Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, Indonesia
Rizka Puspitasari: Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, Indonesia
I Kadek Darma Arnawa: Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, Indonesia
Lailatul Mufaridho: Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, Indonesia
Elonasari Elonasari: Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, Indonesia
Miftahul Jannah: Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, Indonesia
Aniq Rohmawati: Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, Indonesia

IJFS, 2024, vol. 12, issue 3, 1-15

Abstract: Accurate risk assessment is crucial for predicting potential financial losses. This paper introduces an innovative approach by employing expected risk models that utilize risk samples to capture comprehensive risk characteristics. The innovation lies in the integration of classical credibility theory with expected risk models, enhancing their stability and precision. In this study, two distinct expected risk models were developed, referred to as Model Type I and Model Type II. The Type I model involves independent and identically distributed random samples, while the Type II model incorporates time-varying stochastic processes, including heteroscedastic models like GARCH(p,q). However, these models often exhibit high variability and instability, which can undermine their effectiveness. To mitigate these issues, we applied classical credibility theory, resulting in credible expected risk models. These enhanced models aim to improve the accuracy of Value-at-Risk (VaR) forecasts, a key risk measure defined as the maximum potential loss over a specified period at a given confidence level. The credible expected risk models, referred to as CreVaR, provide more stable and precise VaR forecasts by incorporating credibility adjustments. The effectiveness of these models is evaluated through two complementary approaches: coverage probability, which assesses the accuracy of risk predictions; and scoring functions, which offer a more nuanced evaluation of prediction accuracy by comparing predicted risks with actual observed outcomes. Scoring functions are essential in further assessing the reliability of CreVaR forecasts by quantifying how closely the forecasts align with the actual data, thereby providing a more comprehensive measure of predictive performance. Our findings demonstrate that the CreVaR risk measure delivers more reliable and stable risk forecasts compared to conventional methods. This research contributes to quantitative risk management by offering a robust approach to financial risk prediction, thereby supporting better decision making for companies and financial institutions.

Keywords: risk management; Value-at-Risk; credible risk measures; forecast; GARCH; cryptocurrency (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2024
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