Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model

Afzal, Fahim; Haiying, Pan; Afzal, Farman; Mahmood, Asif; Ikram, Amir

doi:10.1177/21582440211005758

Cited by 15 publications

(12 citation statements)

References 34 publications

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“…Therefore, due to the consistent changes in markets, nothing is identical neither the stocks nor the future foretells (Afzal et al , 2019b). In such swapping conditions of dynamic markets where the returns have too much variations, traditional methods like averages and means do not provide a true picture of market (Afzal et al , 2021). So to overcome such confusing situations, dynamic models like DCC and GARCH are considered as feasible mechanisms to achieve one of the objectives of the present study that how cost of capital of PPP-based projects can be calculated effectively (Kinateder et al , 2021).…”

Section: Methods and Resultsmentioning

confidence: 99%

“…Keeping the dynamic conditions of Pakistan stock market under consideration DCC-GARCH model was used on the daily stock prices (Choudhury et al , 2021). The joint β calculated through DCC-GARCH model is considered more significant as compared to the traditional average calculations done through excel (Afzal et al , 2021). Similarly, the year wise calculations were done to further clarify the estimation process of cost of capital in PPP-based projects.…”

Section: Discussionmentioning

confidence: 99%

“…The major reason behind the application of DCC-GARCH model is the joint nature of PPP in dynamic markets where the prediction of risk and returns is a great challenge for the investors. Embracing a hybrid tool of DCC and GARCH may provide a better penetration to risk estimation in dynamic conditions (Afzal et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

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Risk perception and cost of capital in emerging market projects using dynamic conditional correlation model

Afzal

Shehzad

Rehman

et al. 2022

IMEFM

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Purpose Cost estimation is a major concern while planning projects on public–private partnership (PPP) terms in developing countries. To bridge the gap of the right approximation of cost of capital, this study aims to sermon a significant role of investor’s risk perception as unsystematic risk in PPP-based energy projects. Design/methodology/approach To investigate the effective mechanism of determining cost of capital and valuing the capital budgeting, a pure-play method has been acquired to measure systematic risk. In addition, dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models have been applied to calculate weighted average cost of capital. Findings Initially, a joint cost of capital of energy-related projects has been calculated using DCC-GARCH and pure-play method. Investors risk perception has been discussed through market point of view using country risk premium modeling. Latter yearly betas have been calculated using DCC signifying the final outcomes that applying a dynamic model can provide a better cost estimation in emerging economies. Practical implications The findings are implicating that due to the involvement of international investors, domestic risk is linked with country risk. In such situations, market-related information is a key factor to find out the market performance, helping in the estimation of cost of capital through capital asset pricing model (CAPM). High dynamic nature of emerging economies causes an impediment in the estimation of cost of capital. Consequently, to calculate risk in dynamic markets, this study has acquired DCC model that can predict the value of beta factor. Originality/value Study contributes to the body of knowledge by addressing an important issue of investor’s risk perception and effective implication of CAPM using pure-play and DCC-GARCH when data is not promptly available in dynamic situations.

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Section: Methods and Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Risk perception and cost of capital in emerging market projects using dynamic conditional correlation model

Afzal

Shehzad

Rehman

et al. 2022

IMEFM

Self Cite

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“…After that, it is essential to determine the more or less volatility clustering of the data. To determine the existence of volatility clustering in each asset class, the Ljung-Box test ( Q k ) was performed on each index’s squared log returns (Afzal et al , 2021; Badshah, 2018). In addition, the Lagrange-multiplier test Q k r also verified the arch effect in each asset class series of log returns, as shown in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Volatility spillover effect between Pakistan and Shanghai Stock Exchanges using copula and dynamic conditional correlation model

Afzal

Choudhury

Kamran

2022

IMEFM

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Purpose Because of the growing financial market integration, China’s stock market’s volatility spillover effect has gradually increased. Traditional strategies do not capture stock volatility in dependence and dynamic conditions. Therefore, this study aims to find an effective stochastic model to predict the volatility spillover effect in the dynamic stock markets. Design/methodology/approach To assess the time-varying dynamics and volatility spillover, this study has used an integrated approach of dynamic conditional correlation model, copula and extreme-value theory. A daily log-returns of three leading indices of Pakistan Stock Exchange (PSX) and Shanghai Stock Exchange (SSE) from the period of 2009 to 2019 is used in the modeling of value-at-risk (VaR) for volatility estimation. The Student’s t copula has been selected based on maximum likelihood estimation and Akaike’s information criteria values of all the copulas using the goodness-of-fit test. Findings The model results show stronger dependency between all major portfolios of PSX and SSE, with the parametric value of 0.98. Subsequently, the results of dependence structure positively estimate the spillover effect of SSE over PSX. Furthermore, the back-testing results show that the VaR model performs well at 99% and 95% levels of confidence and gives more accurate estimates upon the maximum level of confidence. Practical implications This study is helpful for the investment managers to manage the risk associated to portfolios under dependence conditions. Moreover, this study is also helpful for the researchers in the field of financial risk management who are trying to improve the returns by addressing the issues of volatility estimations. Originality/value This study contributes to the body of knowledge by providing a practical model to manage the volatility spillover effect in dependence conditions between as well as across the financial markets.

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“…It is not only an important symbol of social development and human progress but also an important embodiment of a country's comprehensive national strength and national soft power [1]. At present, sports must be strong and prosperous, and the development of the sports industry is the main way [2].…”

Section: Introductionmentioning

confidence: 99%

Research on Parameter Estimation and Prediction of Sports Financial Market Volatility Model

Song

Fei-yue

2022

Mathematical Problems in Engineering

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At present, the utility function of the financial system in the sports industry has not been brought into full play. Volatility reflects the uncertainty of asset price changes and plays an important role in asset pricing, portfolio, risk management, and asset allocation. Volatility has the characteristics of thick tail, leverage, market linkage, long memory, and aggregation. There are differences between different markets and countries. Therefore, according to the complex characteristics of volatility, this paper uses the feature-oriented research method to study several volatility prediction models, namely, random volatility model, realized volatility model, and multivariate volatility model. In order to promote capital formation and alleviate the financing constraints of sports finance, it provides reference value for reducing transaction costs, improving the quality and efficiency of sports industry, and realizing sustainable and rapid development.

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Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model

Cited by 15 publications

References 34 publications

Risk perception and cost of capital in emerging market projects using dynamic conditional correlation model

Risk perception and cost of capital in emerging market projects using dynamic conditional correlation model

Volatility spillover effect between Pakistan and Shanghai Stock Exchanges using copula and dynamic conditional correlation model

Research on Parameter Estimation and Prediction of Sports Financial Market Volatility Model

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