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This study explores the effect of video assistant referee (VAR) sponsorship on spectator response and compares it with advertising and conventional sponsorship.

An experiment with 809 subjects is conducted by analyzing 20 one-minute video clip stimuli from a Premier League soccer game divided into four formats: two formats of VAR sponsorship, advertising, and conventional sponsorship.

The results show that the indicators of recall, credibility, and perceived congruence improve when the VAR sponsorship format is used.

This is the first manuscript to examine the effectiveness of a new type of sponsorship: VAR sponsorship. This manuscript provides metrics that will guide practitioners on whether to use this type of sponsorship.

This paper tests the accuracies of the models that predict the Value-at-Risk (VaR) for the Market Integrated Latin America (MILA) and Association of Southeast Asian Nations (ASEAN) emerging stock markets during crisis periods.

Many VaR estimation models have been presented in the literature. In this paper, the VaR is estimated using the Generalized Autoregressive Conditional Heteroskedasticity, EGARCH and GJR-GARCH models under normal, skewed-normal, Student-t and skewed-Student-t distributional assumptions and compared with the predictive performance of the Conditional Autoregressive Value-at-Risk (CaViaR) considering the four alternative specifications proposed by Engle and Manganelli (2004).

The results support the robustness of the CaViaR model in out-sample VaR forecasting for the MILA and ASEAN-5 emerging stock markets in crisis periods. This evidence is based on the results of the backtesting approach that analyzed the predictive performance of the models according to their accuracy.

An important issue in market risk is the inaccurate estimation of risk since different VaR models lead to different risk measures, which means that there is not yet an accepted method for all situations and markets. In particular, quantifying and forecasting the risk for the MILA and ASEAN-5 stock markets is crucial for evaluating global market risk since the MILA is the biggest stock exchange in Latin America and the ASEAN region accounted for 11% of the total global foreign direct investment inflows in 2014. Furthermore, according to the Asian Development Bank, this region is projected to average 7% annual growth by 2025.

The paper compares multi-period forecasting performances by direct and iterated method using Bayesian vector autoregressive (VAR) models.

The paper adopts Bayesian VAR models with three different priors – independent Normal-Wishart prior, the Minnesota prior and the stochastic search variable selection (SSVS). Monte Carlo simulations are conducted to compare forecasting performances. An empirical study using US macroeconomic data are shown as an illustration.

In theory direct forecasts are more efficient asymptotically and more robust to model misspecification than iterated forecasts, and iterated forecasts tend to bias but more efficient if the one-period ahead model is correctly specified. From the results of the Monte Carlo simulations, iterated forecasts tend to outperform direct forecasts, particularly with longer lag model and with longer forecast horizons. Implementing SSVS prior generally improves forecasting performance over unrestricted VAR model for either nonstationary or stationary data.

The paper finds that iterated forecasts using model with the SSVS prior generally best outperform, suggesting that the SSVS restrictions on insignificant parameters alleviates over-parameterized problem of VAR in one-step ahead forecast and thus offers an appreciable improvement in forecast performance of iterated forecasts.

This research aims to evaluate the accuracy of several Value-at-Risk (VaR) approaches for determining the Minimum Capital Requirement (MCR) for Islamic stock markets during the pandemic health crisis.

This research evaluates the performance of numerous VaR models for computing the MCR for market risk in compliance with the Basel II and Basel II.5 guidelines for ten Islamic indices. Five models were applied—namely the RiskMetrics, Generalized Autoregressive Conditional Heteroskedasticity, denoted (GARCH), fractional integrated GARCH, denoted (FIGARCH), and SPLINE-GARCH approaches—under three innovations (normal (N), Student (St) and skewed-Student (Sk-t) and the extreme value theory (EVT).

The main findings of this empirical study reveal that (1) extreme value theory performs better for most indices during the market crisis and (2) VaR models under a normal distribution provide quite poor performance than models with fat-tailed innovations in terms of risk estimation.

Since the world is now undergoing the third wave of the COVID-19 pandemic, this study will not be able to assess performance of VaR models during the fourth wave of COVID-19.

The results suggest that the Islamic Financial Services Board (IFSB) should enhance market discipline mechanisms, while central banks and national authorities should harmonize their regulatory frameworks in line with Basel/IFSB reform agenda.

Previous studies focused on evaluating market risk models using non-Islamic indexes. However, this research uses the Islamic indexes to analyze the VaR forecasting models. Besides, they tested the accuracy of VaR models based on traditional GARCH models, whereas the authors introduce the Spline GARCH developed by Engle and Rangel (2008). Finally, most studies have focus on the period of 2007–2008 financial crisis, while the authors investigate the issue of market risk quantification for several Islamic market equity during the sanitary crisis of COVID-19.

I perform the backtesting of 10-day VaR's using daily returns of KOSPI 200 from January 1994 to December 1993 (2,692 days). The seven volatility measures are calculated with the last 300-day data; those are the historical standard deviations, the exponentially weighted moving average (EWMA) volatilities, the standard deviations from GARCH (1, 1) and three measures to consider autocorrelations in daily returns. The seven types of ten-day VaR’s at 1 % and 5% significance levels are estimated from these six volatility measures and 1 or 5 percentile of the last 300-day historical distributions I use the likelihood ratio (LR) test statistics to test the expected frequency and/or independence of the occurrence of extreme losses, that is, the losses which exceed the VaR values. The LR statistics for the expected frequence show that the VaR measure based on the historical standard deviations is the best one, but the LR statistics for independence reject the usefulness of ali the VaR measures.

The aim of this paper is to compare the performance of VaR (value-at-risk) using Realized Volatility Models (which use intraday returns) with VaR the performance of GARCH-type Models (which use daily returns) with three different distribution innovations (normal distribution, t-distribution, skewed t-distribution). In this paper, we empirically examine VaR forecast of korean stock market using KOSPI and KOSDAQ. Empirical results indicate that the Realized Volatility models is superior to the GARCH-type models in forecasting VaR. We also find Var forecast by skewed t-distribution model are more accurate than those using the normal and t-distribution models. Thus, VaR using Realized Volatility models and skewed t-distribution enhances the performance of risk management in Korean financial markets.

This study empirically aims to analyze the transmission of monetary policy in consideration of asymmetry based on the Bank of the Lao PDR (BOL)'s monetary policy tools and real and financial variables in the domestic market.

This study adopts two approaches, conventional vector autoregression (VAR) and asymmetric VAR, to investigate the impact of monetary policy on macroeconomic variables including inflation and real GDP growth in the Lao PDR.

Under a highly dollarized monetary regime, the policy rate change plays a weaker role compared with M0, which exerts significantly positive effects on real GDP growth and inflation. The results of the asymmetric VAR model further substantiate that the real economy responds to a positive M0 shock (easing monetary policy) rather than a negative shock (tightening monetary policy).

Overall estimation results suggest that the effectiveness of monetary policy is limited in Laos, which would take priority over efforts to strengthen the development of the short-term financial market and de-dollarization.

This study can fill the gap in the literature in which the discussions on the transmission mechanism of monetary policy in the BOL's monetary policy are still little known.

This paper raises an issue of calculating a value at risk (VaR) of a stock price in the presence of daily price limits, suggests an appropriate methodology for it, and discusses its practical implications. One finding is that the VaR with price limits is never bigger than without. It turns out that the discrepancy between the two VaRs increases as the confidence level rises, the holding period lengthens, the volatility goes up, or the price limits get tighter.

Might the impact of the global economic policy uncertainty (GEPU) and the long-term bond yields on oil prices be asymmetric? This paper aims to consider the effects of the GEPU and the US long-term government bond yields on oil prices using quantile-based analysis and nonlinear vector autoregression (VAR) model. The author hypothesized whether the negative and positive changes in the GEPU and the long-term bond yields of the USA have different effects on oil prices.

To address this question, the author uses quantile cointegration model and the impulse response functions (IRFs) of the censored variable approach of Kilian and Vigfusson (2011).

The quantile cointegration test showed the existence of non-linear cointegration relationship, whereas Granger-causality analysis revealed that positive/negative variations in GEPU will have opposite effects on oil prices. This result was supported by the quantile regression model’s coefficients and nonlinear VAR model’s IRFs; more specifically, it was stressed that increasing/decreasing GEPU will deaccelerate/accelerate global economic activity and thus lead to a fall/rise in oil prices. On the other hand, the empirical models indicated that the impact of US 10-year government bond yields on oil prices is asymmetrical, while it was found that deterioration in the borrowing conditions in the USA may have an impact on oil prices by slowing down the global economic activity.

As a robustness check of the quantile-based analysis results, the slope-based Mork test is used.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.