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The purpose of this paper is to investigate regime-switching and single-regime GARCH models for the extreme risk forecast of the developed and the emerging crude oil markets.

The regime-switching GARCH-type models and their single-regime counterparts are used in risk forecast of crude oil.

The author finds that the regime-switching GARCH-type models are suitable for the developed and the emerging crude oil markets in that they effectively measure the extreme risk of crude oil in different cases. Meanwhile, the model with switching regimes captures dynamic structures in financial markets, and these models are just only better than the corresponding single-regime in terms of long position risk forecast, instead of short position. That is, it just outperforms the single-regime on the downside risk forecast.

This study comprehensively compares risk forecast of crude oil in different situations through the competitive models. The obtained findings have strong implications to investors and policymakers for selecting a suitable model to forecast extreme risk of crude oil when they are faced with portfolio selection, asset allocation and risk management.

The purpose of this paper is threefold. First, it models and forecasts the risk of the five leading cryptocurrencies, stock market indices (developed and BRICS) and gold returns. Second, it conducts different backtesting procedures forecasts. Third, it focuses on the hedging potential of cryptocurrencies and gold.

The authors used the generalized autoregressive score (GAS) models to model and forecast the risk of cryptocurrencies, stock market indices and gold returns. They conduct different backtesting procedures of the 1% and 5%-value-at-risk (VaR) forecasts. They also use the generalized orthogonal generalized autoregressive conditional heteroskedasticity (GO-GARCH) model to explore the hedging potential of cryptocurrencies by estimating the dynamic conditional correlation between cryptocurrencies and gold, on the one hand, and stock markets on the other hand.

When conducting different backtesting procedures of VaR, our finding suggests that Bitcoin has the highest VaR among cryptocurrencies and Gold and the BRICS indices returns have lower VaR compared to the developed countries. Finally, we provide evidence that the risks among developed stock markets can be hedged by Bitcoin and Gold. Bitcoin can be considered as the new Gold for these economies. Unlike Bitcoin, Gold can be considered as a hedge for Chinese and Indian investors. However, Gold and Bitcoin can be considered as diversifier assets for the other BRICS economies while Dash and Monero are diversifier assets for developed stock markets.

The first paper's empirical contribution lies in analyzing optimal forecast models for cryptocurrencies (other than Bitcoin) returns and risk. The second contribution consists of studying the hedging potential of five leading cryptocurrencies. To the best of our knowledge, no previous studies have investigated the role of cryptocurrencies for BRICS investors.

The purpose of this paper is to evaluate the predictive capacity of market risk estimation models in times of financial crises.

For this, value-at-risk (VaR) valuation models applied to the daily returns of portfolios composed of stock indexes of developed and emerging countries were tested. The Historical Simulation VaR model, multivariate ARCH models (BEKK, VECH and constant conditional correlation), artificial neural networks and copula functions were tested. The data sample refers to the periods of two international financial crises, the Asian Crisis of 1997, and the US Sub Prime Crisis of 2008.

The results pointed out that the multivariate ARCH models (VECH and BEKK) and Copula-Clayton had similar performance, with good adjustments in 100 percent of the tests. It was not possible to perceive significant differences between the adjustments for developed and emerging countries and of the crisis and normal periods, which was different to what was expected.

Previous studies focus on the estimation of VaR by a group of models. One of the contributions of this paper is to use several forms of estimation.

In this paper, we investigate empirically the time-frequency co-movement between the recent COVID-19 pandemic, G7stock markets, gold, crude oil price (WTI) and cryptocurrency markets (bitcoin) using both the multivariate MSGARCH models.

This paper examines the relationship between the volatilities of oil, Chinese stock index and financial assets (cryptocurrency, gold, and G7 stock indexes), for the period January 17th 2020 to December 10th 2020. It tests the presence of regime changes in the GARCH volatility dynamics of bitcoin, gold, Chinese, and G7 stock indexes as well as oil prices by using Markov–Switching GARCH model. Also, the paper estimates the dynamic correlation and volatility spillover between oil, Chinese and financial assets by using the MSBEKK-GARCH and MSDCC-GARCH models.

Overall, we find that all variables display a strong volatility concentrated in the first four months of Covid-19 outbreak. The paper conducts different backtesting procedures of the 1% and 5% Value-at-Risk forecasts of risk. The results find that gold has the lowest VaR. However, the Canadian and American indices have the highest VaR, for respectively 1% and 5% confidence level. The estimation results of MSBEKK-GARCH prove the volatility spillover between Chinese index, oil and financial assets. Although, the past news about shocks in the Chinese index significantly affects the current conditional volatility of financial assets. Moreover, for the high regime, the correlation increased between Chinese and G7 stock indexes which proving the contagion effect of the COVID-19 pandemic. On the contrary, the correlation decreased between Chinese-gold and Chinese-bitcoin, which confirming that gold and bitcoin can be considered as an alternative hedge for some investors during a crisis. During the COVID-19 pandemic, the correlations for the couples oil-gold and oil-bitcoin peaked. Contrary to gold, bitcoin cannot be considered as a safe haven during the global pandemic when investing in crude oil.

In contrast, comparative analysis in terms of responses to US COVID-19 pandemic, the US Covid-19 confirmed cases have relative higher impact on the co-movement in WTI and bitcoin. This paper confirms that gold is a safe haven during the COVID19 pandemic period.

The purpose of this paper is to examine modern monetary policy as practiced and promoted by the officials of Central Banks, with the Federal Reserve Bank of the USA and the Bank of Japan in leading roles.

Modern monetary policy is assessed for its rhetoric and its philosophies steeped in Keynesian traditions. The fallacies of relying on patently incorrect economic theory with specific critique on the assumption that saving is equal to investment (S=I) is exposed in the policy failures of themes such as quantitative easing, approaching the zero bound, wealth effects, the liquidity trap, forbearance lending and an unwavering belief in the power to inflate. An alternative credit theory is presented and discussed to explain the accumulation of monetary interventions in the modern banking environment. The credit theory is further expanded to evaluate an economy in distress as a result of an accumulation of monetary stimulations against a background of the philosophies of the Austrian school of economics.

Three decisive monetary policy outcomes are identified and substantiated in the Austrian philosophy of laissez faire; the probable outcome of modern monetary policies in deflationary stasis; and the destructive outcome of extreme monetary and fiscal interventions resulting in a hyperinflationary depression and destruction of the money unit.

The conceptual framework and content of the paper are mostly original and will contribute to the study of political and monetary economics.

The study aims to understand the role of different streams of oil shocks (demand, supply and risk shocks) on the oil-importing and exporting countries' stock returns. The study also examines the impact of crude oil shocks across the economic regimes and market states. Besides, the role of the Global Financial Crisis (GFC) of 2008 in shaping the oil–stock relationship is also investigated.

The authors revisit the impact of oil shocks on emerging equity markets by using the novel shock decomposition algorithm proposed by Ready (2018). The authors consider 24 emerging equity markets for the period spanning over July 15, 2002, to June 18, 2018, and bifurcate them based on oil dependence. The authors use rolling and dynamic conditional correlation analysis to understand the time-varying co-movements between oil prices and stock returns. The regime and state-specific dependence of stock returns on the structural oil shocks are captured by the Markov regime switching and quantile regression models.

The authors find that the demand shocks are positively associated with stock markets, whereas the supply shocks are negatively related, except in some of the oil-exporting countries. The risk-based shocks also appear to have a negative association with stocks. The authors do not find evidence of strong regime dependence and the direction of relationship across the high and low regimes is somewhat stable. Further, the authors observe an intense oil–stock relationship in the bearish market conditions. Besides, the authors also report evidences of changes in oil–stock relationship onset the GFC.

This is among the first studies to use the oil shock decomposition algorithm of Ready (2018) in the context of emerging equity markets. Additionally, oil shocks' role on the stock market movements across the regimes and market states is studied comprehensively. Thus, the nature of oil shock and the extent to which the emerging markets are exposed is observed in this study.

This study examines problems that may occur when conventional Value‐at‐Risk (VaR) estimators are used to quantify market risks in an agricultural context. For example, standard VaR methods, such as the variance‐covariance method or historical simulation, can fail when the return distribution is fat tailed. This problem is aggravated when long‐term VaR forecasts are desired. Extreme Value Theory (EVT) is proposed to overcome these problems. The application of EVT is illustrated by an example from the German hog market. Multi‐period VaR forecasts derived by EVT are found to deviate considerably from standard forecasts. We conclude that EVT is a useful complement to traditional VaR methods.

The purpose of this paper is to use filtered extreme‐value theory (EVT) model to forecast one of the main emerging market stock returns and compare the predictive performance of this model with other conditional volatility models.

This paper employs eight filtered EVT models created with conditional quantile to estimate value‐at‐risk (VaR) for the Istanbul Stock Exchange. The performances of the filtered EVT models are compared to those of generalized autoregressive conditional heteroskedasticity (GARCH), GARCH with student‐t distribution, GARCH with skewed student‐t distribution, and FIGARCH by using alternative back‐testing algorithms, namely, Kupiec test, Christoffersen test, Lopez test, Diebold and Mariano test, root mean squared error (RMSE), and h‐step ahead forecasting RMSE.

The results indicate that filtered EVT performs better in terms of capturing fat‐tails in stock returns than parametric VaR models. An increase in the conditional quantile decreases h‐step ahead number of exceptions and this shows that filtered EVT with higher conditional quantile such as 40 days should be used for forward looking forecasting.

The research results show that emerging market stock return should be forecasted with filtered EVT and conditional quantile days lag length should also be estimated based on forecasting performance.

This study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models.

One-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stage GARCH-EVT models, are generated. The forecasting ability of the standard GARCH-EVT model and the asymmetric exponential GARCH (EGARCH)-EVT model is compared with that of the Realized GARCH-EVT model. Additionally, five realized volatility measures are used to test whether the choice of realized volatility measure affects the forecasting performance of the Realized GARCH-EVT model.

In terms of the out-of-sample comparisons, the Realized GARCH-EVT models generally outperform the standard GARCH-EVT and EGARCH-EVT models. However, the choice of the realized estimator does not affect the forecasting ability of the Realized GARCH-EVT model.

It is one of the earliest implementations of the two-stage Realized GARCH-EVT model for generating quantile forecasts. To the best of the authors’ knowledge, this is the first study that compares the performance of different realized estimators within Realized GARCH-EVT framework. In the context of high-frequency data-based forecasting studies, a sample period of around 11 years is reasonably large. More importantly, the data set has a cross-sectional dimension with multiple European stock indices, whereas most of the earlier studies are based on the US market.

This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models.

The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated using six forecasting models: GARCH, EGARCH, RGARCH, GARCH-EVT, EGARCH-EVT and RG-EVT. The EVT models are implemented using the two-stage conditional EVT framework of McNeil and Frey (2000). The forecasting performance is evaluated using multiple statistical tests to ensure the robustness of the results.

The authors find that regardless of the choice of the GARCH model, the two-stage conditional EVT approach provides significantly better out-of-sample performance than the standalone GARCH model. The standalone RGARCH model does not perform better than the GARCH and EGARCH models. However, using the RGARCH model in the first stage of the conditional EVT approach leads to a significant improvement in the VaR forecasting performance. Overall, among the six forecasting models, the RG-EVT model provides the best forecasts of daily VaR.

To the best of the authors’ knowledge, this is the earliest implementation of the RGARCH model within the conditional EVT framework. Additionally, the authors use a data set with a reasonably long sample period (around 11 years) in the context of high-frequency data-based forecasting studies. More significantly, the data set has a cross-sectional dimension that is rarely considered in the existing VaR forecasting literature. Therefore, the findings are likely to be widely applicable and are robust to the data snooping bias.