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

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402110057
Author(s):  
Fahim Afzal ◽  
Pan Haiying ◽  
Farman Afzal ◽  
Asif Mahmood ◽  
Amir Ikram
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Stock Markets ◽  
Value At Risk ◽  
Stock Exchange ◽  
Integrated Approach ◽  
Actual Behavior ◽  
Time Varying ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation

To assess the time-varying dynamics in value-at-risk (VaR) estimation, this study has employed an integrated approach of dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models on daily stock return of the emerging markets. A daily log-returns of three leading indices such as KSE100, KSE30, and KSE-ALL from Pakistan Stock Exchange and SSE180, SSE50 and SSE-Composite from Shanghai Stock Exchange during the period of 2009–2019 are used in DCC-GARCH modeling. Joint DCC parametric results of stock indices show that even in the highly volatile stock markets, the bivariate time-varying DCC model provides better performance than traditional VaR models. Thus, the parametric results in the DCC-GRACH model indicate the effectiveness of the model in the dynamic stock markets. This study is helpful to the stockbrokers and investors to understand the actual behavior of stocks in dynamic markets. Subsequently, the results can also provide better insights into forecasting VaR while considering the combined correlational effect of all stocks.

ARE STOCK MARKETS AND CRYPTOCURRENCIES CONNECTED?

2020 ◽  
pp. 1-16
Author(s):  
MUHAMMAD UMAR ◽  
NGO THAI HUNG ◽  
SHIHUA CHEN ◽  
AMJAD IQBAL ◽  
KHALIL JEBRAN
Keyword(s):  
Operating System ◽  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Composite Index ◽  
Wavelet Coherence ◽  
Time Varying ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Nasdaq Composite Index

This study explores the connectedness between cryptocurrencies (Bitcoin, Ethereum, Ripple, Bitcoin cash and Ethereum Operating System) and major stock markets (NYSE composite index, NASDAQ composite index, Shanghai Stock Exchange, Nikkei 225 and Euronext NV). Using the asymmetric dynamic conditional correlation (ADCC) and wavelet coherence approaches, we document a significant time-varying conditional correlation between the majority of the cryptocurrencies and stock market indices and that the negative shocks play a more prominent role than the positive shocks of the same magnitude. Overall, our findings explore potential avenues for diversification for investors across cryptocurrencies and major stock markets.

scholarly journals Optimisation of Time-Varying Asset Pricing Models with Penetration of Value at Risk and Expected Shortfall

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 394
Author(s):  
Adeel Nasir ◽  
Kanwal Iqbal Khan ◽  
Mário Nuno Mata ◽  
Pedro Neves Mata ◽  
Jéssica Nunes Martins
Keyword(s):  
At Risk ◽  
Asset Pricing ◽  
Stock Returns ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Time Varying ◽  
Pricing Models ◽  
Asset Pricing Model ◽  
Asset Pricing Models ◽  
Market Beta

This study aims to apply value at risk (VaR) and expected shortfall (ES) as time-varying systematic and idiosyncratic risk factors to address the downside risk anomaly of various asset pricing models currently existing in the Pakistan stock exchange. The study analyses the significance of high minus low VaR and ES portfolios as a systematic risk factor in one factor, three-factor, and five-factor asset pricing model. Furthermore, the study introduced the six-factor model, deploying VaR and ES as the idiosyncratic risk factor. The theoretical and empirical alteration of traditional asset pricing models is the study’s contributions. This study reported a strong positive relationship of traditional market beta, value at risk, and expected shortfall. Market beta pertains its superiority in estimating the time-varying stock returns. Furthermore, value at risk and expected shortfall strengthen the effects of traditional beta impact on stock returns, signifying the proposed six-factor asset pricing model. Investment and profitability factors are redundant in conventional asset pricing models.

scholarly journals Dynamic value at risk estimation for BELEX15

2011 ◽  
Vol 8 (1) ◽  
Author(s):  
Emilija Nikolić-Đorić ◽  
Dragan Đorić
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Long Memory ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Value Theory ◽  
Garch Models ◽  
Measurement Models ◽  
Standardized Residuals

This paper uses RiskMetrics, GARCH and IGARCH models to calculate daily VaR for Belgrade Stock Exchange index BELEX15 returns based on the normal and Student t innovation distribution. In the case of GARCH and IGARCH models VaR values are obtained applying Extreme Value Theory on the standardized residuals. The Kupiec's LR statistics was used to test the accuracy of risk measurement models. The main conclusions are: (1) when modelling value-at-risk it is very important to have a good model for volatility of stock returns; (2) both stationary and integrated GARCH models outperform RiskMetrics in estimating VaR; (3) although long memory volatility is present in the BELEX15 index, IGARCH models cannot outperform GARCH type models in VaR evaluations for this index.

The contagion effect between the oil market, and the Islamic and conventional stock markets of the GCC country

2018 ◽  
Vol 11 (2) ◽  
pp. 157-181 ◽  
Author(s):  
Taicir Mezghani ◽  
Mouna Boujelbène
Keyword(s):  
Stock Returns ◽  
Stock Markets ◽  
Strong Increase ◽  
Conditional Correlation ◽  
Content Type ◽  
Dynamic Conditional Correlation ◽  
Dynamic Correlation ◽  
Oil Market ◽  
Oil Crisis ◽  
Forecasting Errors

PurposeThis study aims to investigate the transmission of shock between the oil market and the Islamic and conventional stock markets of the Gulf Cooperation Council (GCC) countries during the oil shocks of 2008 and 2014.Design/methodology/approachThis study uses two models. First, the dynamic conditional correlation–generalized autoregressive conditionally heteroskedastic model has been used to capture the fundamental contagion effects between the oil market and the Islamic and conventional stock markets during the tranquil and turmoil-crisis periods of 2008-2014. Second, the filter of Kalman has been used to capture the effects of pure contagion between the oil market and the GCC Islamic and conventional stock markets. The authors analyze the dynamic correlation between forecasting errors of oil returns and stock returns of GCC Islamic and GCC conventional indices.FindingsThe main findings of this investigation are: first, the estimation of the dynamic conditional correlation– generalized autoregressive conditionally heteroskedastic model for oil market and the Islamic and conventional stock markets proves that the Islamic and conventional stock markets and oil market displayed a significant increase in the dynamic correlation during the turmoil period, from mid-2008 and mid-2014. This proves the existence of contagion between the markets studied. Second, the authors analyze the dynamic correlation between forecasting errors of oil returns and stock returns of GCC Islamic and GCC conventional indices. They show a strong increase in the correlation coefficients between the oil market and the conventional GCC stock markets, and between the conventional and Islamic GCC stock markets during the oil crisis of 2014. However, there is no change in regime in the figure of the correlation coefficient between the oil market and the GCC Islamic stock markets during the 2008 financial crisis. This pure contagion is mainly attributed to the herding bias in 2014 oil crisis.Originality/valueThis study contributes to identifying the contribution of herding bias on the volatility transmission between the oil markets, and the GCC Islamic and conventional stock market, especially during two controversial shocks: the 2008 oil-price increase and the 2014 oil drop.

scholarly journals Time-Varying Conditional Co-movement between Inter-Sector Stock Returns: Evidence from Ghana Stock Market

2020 ◽  
Vol 5 (4) ◽  
Author(s):  
Thomas Appiah ◽  
Abednego Forson
Keyword(s):  
Stock Returns ◽  
Stock Return ◽  
Time Varying ◽  
Conditional Correlation ◽  
Domestic Markets ◽  
Dynamic Conditional Correlation ◽  
Fund Managers ◽  
Diversification Strategies ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Sector Returns

Investors generally exhibit home bias with regards to their investment destinations. To diversify their portfolio, such investors invest in different sectors within the domestic economy. However, such behaviour could be counter-productive in periods of increased co-movement of assets returns.  In this paper, we examine the inter-sector stock return co-movement among the major sectors of the Ghanaian economy with the view to shedding some light on the nature of assets return correlations and its implications for portfolio diversification.  A sample of 332 weekly observations of stock returns of five major sectors within the Ghanaian economy is used to undertake the analysis. Dynamic Conditional Correlation - Generalized Autoregressive Conditional Heteroscedasticity (DCC-GARCH) techniques are applied to the weekly stock return series from January 2010 to June 2017. The DCC-GARCH model was estimated with correlation targeting and asymmetric DCC. We find dynamic conditional correlation among stock returns of all the sectors, implying that the correlation between the sector returns is time-varying. This result challenges the assumption of constant correlation among stock returns of different sectors in the domestic markets. We also find that the conditional correlation between returns of the various sectors ranges from 0.234 to 0.998, which indicates medium to very high interdependence among the stock returns. Based on the result of this study, we propose that fund managers and investors should not limit their diversification strategies to inter-sector investments since in periods of uncertainty, the ability of the investor to enjoy diversification benefits is seriously undermined.

scholarly journals Commodity Price Correlation And Time Varying Hedge Ratios

2014 ◽  
Vol 30 (4) ◽  
pp. 1053
Author(s):  
Amine Lahiani ◽  
Khaled Guesmi
Keyword(s):  
Stock Market ◽  
Value At Risk ◽  
Price Volatility ◽  
Commodity Price ◽  
Conditional Value At Risk ◽  
Time Varying ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Hedging Strategies ◽  
Hedge Ratios

<p>This paper examines the price volatility and hedging behavior of commodity futures indices and stock market indices. We investigate the weekly hedging strategies generated by return-based and range-based asymmetric dynamic conditional correlation (DCC) processes. The hedging performances of short and long hedgers are estimated with a semi-variance, low partial moment and conditional value-at-risk. The empirical results show that range-based DCC model outperforms return-based DCC model for most cases.</p>

scholarly journals The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Halit Cinarka ◽  
Mehmet Atilla Uysal ◽  
Atilla Cifter ◽  
Elif Yelda Niksarlioglu ◽  
Aslı Çarkoğlu
Keyword(s):  
Case Reports ◽  
Time Varying ◽  
Conditional Correlation ◽  
Web Based ◽  
Specific Symptom ◽  
Sliding Windows ◽  
Dynamic Conditional Correlation ◽  
The United Kingdom ◽  
Correlation Models ◽  
Google Search

AbstractThis study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r ≥ 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.

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