Value-at-risk (VAR) estimation and backtesting during COVID-19: Empirical analysis based on BRICS and US stock markets

Shaik, Muneer; Padmakumari, Lakshmi

doi:10.21511/imfi.19(1).2022.04

Cited by 12 publications

(6 citation statements)

References 23 publications

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“…Linsmeier and Pearson (1996) also defined VaR as a measure of the downside risk and the chance to lose more than the amounts indicated by the measure is very low, as it depends on the confidence level used to calculate the VaR over a certain period (Berkelaar, 2002). Nevertheless, Shaik and Padmakumari (2022) conclude that using VaR in investment bank firms and significant banking corporations to mitigate risks has shown poor performance when used in periods of crisis. Bali et al (2004) conclude that VaR can seize significant time-series variation in stock returns.…”

Section: Methodsmentioning

confidence: 99%

Cryptocurrency volatility and Egyptian stock market indexes: A note

Eldomiaty,

Khaled

2024

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This paper examines the effect of the riskiness of the top four cryptocurrencies on the riskiness of stock market indexes in Egypt, being recognized as a developing country. The analysis uses daily data on cryptocurrencies and the three stock market indexes covering January 2020 to January 2023. The risk is measured using the holding period Value at Risk (VaR). The GMM results show that (a) cryptocurrency volatility is negatively associated with the volatility of stock market indexes. That is, the higher the investors’ interest in trading cryptocurrencies, the lower the volatility of stock market indexes as investors trade stocks less frequently, (b) cryptocurrencies can provide hedge and diversification benefits, and (c) the relationship between volatilities of cryptocurrencies and stock market indexes varies across indexes, therefore, contingent.

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Section: Methodsmentioning

confidence: 99%

Cryptocurrency volatility and Egyptian stock market indexes: A note

Eldomiaty,

Khaled

2024

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“…COVID-19 pandemic has created turbulence in the global financial markets (Salisu et al ., 2020; Singh et al ., 2021; Shaik et al ., 2022). Atri et al .…”

Section: Review Of Literature and Theoretical Frameworkmentioning

confidence: 99%

“…COVID-19 pandemic has created turbulence in the global financial markets (Salisu et al, 2020;Singh et al, 2021;Shaik et al, 2022). Atri et al (2021) found, using the ARDL approach, that the Covid-19 deaths and panic had an adverse effect on crude oil prices but an optimistic effect on gold prices, suggesting gold as a safe haven during pandemic.…”

Section: Review Of Literaturementioning

confidence: 99%

Revisiting the impact of geopolitical risk on Sukuk, stocks, oil and gold markets during the crises period: fresh evidence from wavelet-based approach

Raza Rabbani,

Hassan,

Jamil

et al. 2023

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Purpose In this study, the authors analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.Design/methodology/approach The study used a mix of wavelet-based approaches, including continuous wavelet transformation and discrete wavelet transformation. The analysis used data from the Geopolitical Risk index (GP{R), Dow Jones Sukuk index (SUKUK), Dow Jones Islamic index (DJII), Dow Jones composite index (DJCI), one of the top crude oil benchmarks which is based on the Europe (BRENT) (oil fields in the North Sea between the Shetland Island and Norway), and Global Gold Price Index (gold) from May 31, 2012, to June 13, 2022.Findings The results of the study indicate that during the COVID-19 and Russia–Ukraine conflict period geopolitical risk (GPR) was in the leading position, where BRENT confirmed the lagging relationship. On the other hand, during the COVID-19 pandemic period, SUKUK, DJII and DJCI are in the leading position, where GPR confirms the lagging position.Originality/value The present study is unique in three respects. First, the authors revisit the influence of GPR on global asset markets such as Islamic stocks, Islamic bonds, conventional stocks, oil and gold. Second, the authors use the wavelet power spectrum and coherence analysis to determine the level of reliance based on time and frequency features. Third, the authors conduct an empirical study that includes recent endogenous shocks generated by health crises such as the COVID-19 epidemic, as well as shocks caused by the geopolitical danger of a war between Russia and Ukraine.Highlights We analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.The results of the wavelet-based approach show that Dow Jones composite and Islamic indexes have observed the highest mean return during the study period.GPR and BRENT are estimated to have the highest amount of risk throughout the observation period.Dow Jones Sukuk, Islamic and composite stock show similar trend of volatility during the COVID-19 pandemic period and comparatively gold observes lower variance during the COVID-19 pandemic and Russia–Ukraine conflict.

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“…It builds upon Christoffersen's ideas [50] and incorporates them into a more powerful metric. TBFI is a likelihood ratio test defined in [51] as:…”

Section: Used Metricsmentioning

confidence: 99%

Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation

Bagheri,

Reforgiato Recupero,

Sirnes

2023

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Value at risk is a statistic used to anticipate the largest possible losses over a specific time frame and within some level of confidence, usually 95% or 99%. For risk management and regulators, it offers a solution for trustworthy quantitative risk management tools. VaR has become the most widely used and accepted indicator of downside risk. Today, commercial banks and financial institutions utilize it as a tool to estimate the size and probability of upcoming losses in portfolios and, as a result, to estimate and manage the degree of risk exposure. The goal is to obtain the average number of VaR “failures” or “breaches” (losses that are more than the VaR) as near to the target rate as possible. It is also desired that the losses be evenly distributed as possible. VaR can be modeled in a variety of ways. The simplest method is to estimate volatility based on prior returns according to the assumption that volatility is constant. Otherwise, the volatility process can be modeled using the GARCH model. Machine learning techniques have been used in recent years to carry out stock market forecasts based on historical time series. A machine learning system is often trained on an in-sample dataset, where it can adjust and improve specific hyperparameters in accordance with the underlying metric. The trained model is tested on an out-of-sample dataset. We compared the baselines for the VaR estimation of a day (d) according to different metrics (i) to their respective variants that included stock return forecast information of d and stock return data of the days before d and (ii) to a GARCH model that included return prediction information of d and stock return data of the days before d. Various strategies such as ARIMA and a proposed ensemble of regressors have been employed to predict stock returns. We observed that the versions of the univariate techniques and GARCH integrated with return predictions outperformed the baselines in four different marketplaces.

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Value-at-risk (VAR) estimation and backtesting during COVID-19: Empirical analysis based on BRICS and US stock markets

Cited by 12 publications

References 23 publications

Cryptocurrency volatility and Egyptian stock market indexes: A note

Cryptocurrency volatility and Egyptian stock market indexes: A note

Revisiting the impact of geopolitical risk on Sukuk, stocks, oil and gold markets during the crises period: fresh evidence from wavelet-based approach

Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation

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