The Accuracy of Risk Measurement Models on Bitcoin Market during COVID-19 Pandemic

Likitratcharoen, Danai; Kronprasert, Nopadon; Wiwattanalamphong, Karawan; Pinmanee, Chakrin

doi:10.3390/risks9120222

Cited by 7 publications

(5 citation statements)

References 40 publications

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“…Through self-learning simulation systems, in the case of incomplete risk factor information, an objective and fair measurement model can be obtained. However, the artificial neural network method has high calculation accuracy and weak practical operability [11,12]. As is evident, the main characteristics of green innovation risk measurement in the manufacturing industry are: limited research data, single measurement methods, and multiple risk influencing factors.…”

Section: Advantages and Disadvantages Of Evaluation Measurement Methodsmentioning

confidence: 99%

Exploring the Application of Green Innovation Risk Measurement Model in China's Manufacturing Industry

Sun

2024

Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17–19, 2023

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With the increasingly serious global environmental problems and the demand for sustainable development in society, green innovation has become an important direction for the development of China's manufacturing industry, but it faces numerous risks and challenges. Therefore, establishing a scientifically effective green innovation risk measurement model is of great significance for assessing and managing risks and improving investment returns. This article elaborates on the concept and principles of the current green innovation risk measurement model, and analyzes its applicability, advantages and disadvantages. On this basis, the specific application of the model in China's manufacturing industry is explored. Finally, the effectiveness of the model application is elaborated, and future research directions and suggestions are proposed. Through the research in this article, it is expected to enhance China's manufacturing industry's understanding and management capabilities of green innovation risks, promote the transformation of enterprises towards sustainable development, and make positive contributions to achieving a win-win situation of economic and environmental benefits.

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Section: Advantages and Disadvantages Of Evaluation Measurement Methodsmentioning

confidence: 99%

Exploring the Application of Green Innovation Risk Measurement Model in China's Manufacturing Industry

Sun

2024

Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17–19, 2023

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“…The applicability of VaR transcends various asset classes. In the realm of high-risk assets like cryptocurrencies, research indicates a superior performance of non-parametric approaches over those predicated on normality assumptions (Likitratcharoen et al 2018(Likitratcharoen et al , 2021(Likitratcharoen et al , 2023. In emerging markets, the deployment of Extreme Value Theory (EVT) for VaR forecasting has garnered attention.…”

Section: Value-at-risk Concepts and Limitationsmentioning

confidence: 99%

“…However, it is not devoid of assumptions. The HS VaR model presupposes that future investment returns will mirror the historical returns dataset, a premise that underlies its quantile estimation (Likitratcharoen et al 2021(Likitratcharoen et al , 2023Pritsker 2006). The model's equation is formulated as:…”

Section: Hs Var: a Non-parametric Approach To Quantile Estimationmentioning

confidence: 99%

Assessing Financial Stability in Turbulent Times: A Study of Generalized Autoregressive Conditional Heteroskedasticity-Type Value-at-Risk Model Performance in Thailand’s Transportation Sector during COVID-19

Likitratcharoen,

Suwannamalik

2024

Risks

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The Value-at-Risk (VaR) metric serves as a pivotal tool for quantifying market risk, offering an estimation of potential investment losses. Predominantly employed within financial sectors, it aids in adhering to regulatory mandates and in devising capital reserve strategies. Nonetheless, the predictive precision of VaR models frequently faces scrutiny, particularly during crises and heightened uncertainty phases. Phenomena like volatility clustering impinge on the accuracy of these models. To mitigate such constraints, conditional volatility models are integrated to augment the robustness and adaptability of VaR approaches. This study critically evaluates the efficacy of GARCH-type VaR models within the transportation sector amidst the Thai stock market’s volatility during the COVID-19 pandemic. The dataset encompasses daily price fluctuations in the Transportation Sector index (TRANS), the Service Industry index (SERVICE), and 17 pertinent stocks within the Stock Exchange of Thailand, spanning from 28 December 2018 to 28 December 2023, thereby encapsulating the pandemic era. The employed GARCH-type VaR models include GARCH (1,1) VaR, ARMA (1,1)—GARCH (1,1) VaR, GARCH (1,1)—M VaR, IGARCH (1,1) VaR, EWMA VaR, and csGARCH (1,1) VaR. These are juxtaposed with more traditional, less computationally intensive models like the Historical Simulation VaR and Delta Normal VaR. The backtesting methodologies encompass Kupiec’s POF test, the Independence Test, and Christoffersen’s Interval Forecast test. Intriguingly, the findings reveal that the Historical Simulation VaR model surpasses GARCH-type VaR models in failure rate accuracy. Within the GARCH-type category, the EWMA VaR model exhibited superior failure rate accuracy. The csGARCH (1,1) VaR and EWMA VaR models emerged as notably robust. These findings bear significant implications for managerial decision-making in financial risk management.

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“…In the interfirm network, the loss caused by the fire sale of financial assets will not only affect the formation of carbon quota counterparty risk among firms but also the size of the credit risk. Suppose that the loss of assets of firms is used to measure the credit risk of carbon quota counterparties among firms [41].…”

Section: Analysis Of the Mechanism Of Carbon Quota Counterparty Risksmentioning

confidence: 99%

“…The baseline value of the parameters is determined according to the literature [21,[28][29][30][31]36,37,40,42,43]. Suppose that the scale of firm asset losses is used to measure the credit risk of carbon quota counterparties among firms [41]. The specific benchmark values of each parameter are shown in Table 2.…”

Section: Simulation Analysismentioning

confidence: 99%

Counterparty Risk Contagion Model of Carbon Quota Based on Asset Price Reduction

et al. 2023

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Driven by the “double carbon” goal, the sale of financial assets at reduced prices by firms due to carbon emission constraints is bound to aggravate the uncertainty and volatility of carbon trading among firms, and potentially create counterparty risk contagion. In view of this, this paper considers the sensitivity of the transaction of corporate financial assets, the transaction price of carbon quotas, and corporate carbon performance; constructs a network model for the risk contagion of carbon quota counterparties; theoretically discusses the risk formation and infection mechanism of carbon quota counterparties; and calculates and simulates the evolutionary characteristics of the risk contagion of carbon quota counterparties. The main research conclusions are as follows. (1) In the interfirm debt network, the sensitivity to the price of selling the financial asset, the probability of credit risk contagion of carbon quotas among firms, the cumulative proportion of assets sold, and the proportion of rational investors in the financial market exert a decreasing phenomenon on the risk of carbon quota counterparties. In addition, the corporate carbon performance shows a marginal increasing phenomenon. (2) When multiple factors intersect, the proportion of rational investors in the financial market has the greatest influence on the formation of the carbon quota counterparty risk, whereas the effect of corporate carbon performance has the least. Corporate carbon risk awareness has the greatest effect on the risk contagion of carbon quota counterparties, whereas the trading price of the carbon quota has the least influence. In addition, the total score of the interfirm assessment has a great impact on the trend and range of the risk contagion of carbon quota counterparties. (3) Corporate carbon risk awareness and the carbon quota trading price have a marginally decreasing effect on the risk contagion of carbon quota counterparties, and corporate carbon performance and the total score of interfirm assessment have a marginally increasing effect. This study has important theoretical and practical significance for preventing interfirm counterparty risk contagion under the double carbon target.

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The Accuracy of Risk Measurement Models on Bitcoin Market during COVID-19 Pandemic

Cited by 7 publications

References 40 publications

Exploring the Application of Green Innovation Risk Measurement Model in China's Manufacturing Industry

Exploring the Application of Green Innovation Risk Measurement Model in China's Manufacturing Industry

Assessing Financial Stability in Turbulent Times: A Study of Generalized Autoregressive Conditional Heteroskedasticity-Type Value-at-Risk Model Performance in Thailand’s Transportation Sector during COVID-19

Counterparty Risk Contagion Model of Carbon Quota Based on Asset Price Reduction

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