Volatility Index Research Articles

Global financial cycle: Impact on Ukraine

Global financial fluctuations that arise in the financial centres – the US and the EU through international channels of monetary transmission affect the financial markets of peripheral countries, including Ukraine, where the assessment and consideration of such an impact is an important element of monetary policy. Therefore, the purpose of the article was to assess the impact of cyclical fluctuations of the global financial situation on the financial sector of Ukraine’s economy. As a result of correlation analysis, a direct relationship between leverage indicators in Ukraine and the Chicago Board Options Exchange Volatility Index and Euro Stoxx Volatility Index has been revealed. At the same time, the direct relationship between these indices and the growth of domestic credit in Ukraine contradicts the concept of the global financial cycle and may indicate the presence of more important factors of domestic credit. Correlation analysis of the volatility index of the Chicago Board Options Exchange and capital flows in Ukraine showed that in the years of increased global uncertainty and volatility, the volume of capital flows decreased, especially for portfolio debt instruments. Analysis of the cyclical components of the global and domestic credit cycle in Ukraine obtained with the Godric-Prescott filter indicates a higher level of volatility in domestic lending. The obtained value of the concordance coefficient did not make it possible to draw an unequivocal conclusion about the counter- or procyclical nature of domestic credit fluctuations relative to the global volume of loans in the non-banking sector. It has been found that the global financial cycle was not a significant factor in domestic credit in Ukraine in the studied period. The practical significance of the results lies in the fact that the National Bank of Ukraine will be able to implement a monetary policy independent of the global financial fluctuations and limit the outflow of capital during the period of growing uncertainty in the international financial markets

Do implied volatilities of stock and commodity markets affect conventional & shariah indices differently? An evidence by OVX, GVZ and VIX

The current study aims to investigate how index returns of conventional and shariah indices of the USA, Europe, and Asia are affected by changes in oil prices, gold prices, VIX, gold-VIX, and oil-VIX. In our investigation, we used the S&P 500, S&P Europe 350, S&P Pan Asia, and their relevant shariah counterparts for the USA, Europe, and Asia. To examine how the explanatory factors affect the overall distribution of the explained variables, we used OLS and quantile regression. For the time frame prior to Covid-19, we discover that all volatility indices—OVX, GVZ, and VIX—influence returns of all indices simultaneously, and that all variables—aside from the spot price of oil—have a greater impact during the bear phase according to QR findings. Further, Volatility indices have a greater impact on volatility of index returns during the Covid-19 period. This is largely because the Covid-19 outbreak had a rapid impact on economies all around the world, and the only thing that affected financial markets consistently was high volatility. This is further supported by the findings of BEKK, which demonstrate that volatility extends across all markets and originates from commodities like gold, oil, gold-VIX, and VIX. Evidence for this can be seen in the fact that during the COVID-19 period, stock prices reacted more favorably to oil price volatility than to oil spot prices, which even went negative on April 20, 2020. Because of this, market stability can be promoted by reducing volatility through the prompt dissemination of crucial information, even while governments have little direct control over the prices of significant commodities like gold and crude oil.

COVID-19 Pandemic and Indices Volatility: Evidence from GARCH Models

This study examines the impact of volatility on the returns of nine National Stock Exchange (NSE) indices before, during, and after the COVID-19 pandemic. The study employed generalized autoregressive conditional heteroskedasticity (GARCH) modelling to analyse investor risk and the impact of volatility on returns. The study makes several contributions to the existing literature. First, it uses advanced volatility forecasting models, such as ARCH and GARCH, to improve volatility estimates and anticipate future volatility. Second, it enhances the analysis of index return volatility. The study found that the COVID-19 period outperformed the pre-COVID-19 and overall periods. Since the Nifty Realty Index is the most volatile, Nifty Bank, Metal, and Information Technology (IT) investors reaped greater returns during COVID-19 than before. The study provides a comprehensive review of the volatility and risk of nine NSE indices. Volatility forecasting techniques can help investors to understand index volatility and mitigate risk while navigating these dynamic indices.

Information spillovers in Hong Kong REITs and related asset markets

This study examines information spillovers among Hong Kong (HK) REITs, major HK financial markets, major Chinese Mainland financial markets, and the VIX index. Our results reveal that time-varying spillover effects peak during crises, notably during the COVID-19 pandemic. The HK REITs market consistently receives return and volatility spillover effects from the HK stock market and real estate market. Since 2014, the HK REITs market has also been a significant recipient of volatility spillovers from Chinese Mainland stock and real estate markets. Additionally, the VIX index plays a driving role in the risk contagion effect of HK REITs. These findings inform investors in adjusting strategies, optimizing portfolios, and mitigating financial risks.

The response of gold to the COVID-19 pandemic

PurposeThis study aims to investigate how the gold return and its volatility respond to the COVID-19 pandemic.Design/methodology/approachStochastic volatility (SV) models are conducted to examine the response of gold to the number of new confirmed cases and deaths.FindingsThe results indicate that an increase in the change rate of the number of COVID-19 infections or fatalities leads to heightened volatility in gold prices. Moreover, the results suggest that gold volatility is more sensitive to the impacts from high-income countries than by those from middle- and low-income countries. In addition, the asymmetric effect is detected in the gold price volatility, which is contrary to the typical asymmetric effect seen in the stock market. Furthermore, the results remain robust after accounting for the US dollar and the volatility index in relation to gold returns.Practical implicationsThis study presents whether and to what extent gold is incorporated in the information related to the number of COVID-19 cases and deaths.Originality/valueThis study augments the existing literature by exploring how the number of COVID-19 infections and fatalities influences gold prices. In addition, it examines the day-of-the-week and asymmetric effects that may contribute to the volatility of gold prices. To the best of the authors’ knowledge, the evolution of gold has not yet been investigated using SV models.

Forecasting VIX: the illusion of forecast evaluation criteria

The study uses daily realized volatility measures in order to gain forecast accuracy over stocks’ market implied volatility, as proxied by VIX Index. We evaluate forecast accuracy by incorporating a traditional statistical loss function, along with an objective-based evaluation criterion, that is the cumulative returns earned from the different HAR-type volatility models, through a simple yet effective trading exercise on VIX futures. Findings, illustrate how illusive the choice between the two metrics may be, as it ends in two contradicting results.

Leading and lagging role between financial stress and crude oil

PurposeThe crude oil market plays a key role in addressing the issue of energy economics. This paper aims to detect the causality relationship between the crude oil market and economy based on the financial system.Design/methodology/approachThis paper used the static and dynamic Hatemi-J Bootstrap Toda–Yamamoto and Diebold–Yilmaz connectedness index. The Hatemi-J Bootstrap Toda-Yamamoto approach allows researchers to use nonstationary data and that method is robust to nonnormal distribution and heteroscedasticity. The Diebold–Yilmaz connectedness index model provides researchers to detect the power of connectedness besides linkage direction. The analyzed period is the span from January 3, 2005 to October 3, 2022.FindingsThe results show bidirectional causality in the full sample but unidirectional causality before and after the 2008 financial crisis. During the 2008 financial crisis period and the COVID-19 period, there was a bidirectional and unidirectional causality, respectively. The connectedness approach indicates that the crude oil market affects financial stress through investors’ risk preferences.Research limitations/implicationsThe Diebold–Yilmaz spillover index model is based on vector autoregression methods with a stationarity precondition. However, some of the five dimensions that constitute the financial stress index (FSI) are nonstationary in level. Therefore, the authors takes the first difference of the nonstationary data.Practical implicationsThe linkage between the crude oil market and the FSI provides useful information for investors and policymakers. For instance, this paper indicates that an investor wanted to forecast future value of the crude oil (financial stress) should consider the current and past values of financial stress (crude oil). Moreover, policymaker should consider the crude oil market (FSI) to make a policy proposal for financial system (crude oil market).Originality/valueRecently, indicators of economic activity levels (economic policy uncertainty, implied volatility index) have begun to be considered to analyze the relationship between energy and the economy but very little is known in the literature about the leading and lagging roles of data in subsample periods and the linkage channel. The other originality of this research is using the new econometric approaches.

Improving prediction efficiency of Chinese stock index futures intraday price by VIX-Lasso-GRU Model

With T+0 and short selling mechanism, the stock index futures are attractive to short-term traders in China, where stocks cannot be liquidated within the day and are difficult to short. So in terms of futures, how to improve the accuracy and speed of intraday price forecasting always fascinates short-term traders and researchers. Here we propose a novel forecasting model, VIX-Lasso-GRU Model, which based on the gated recurrent unit (GRU) by adding VIX information and a method called Least absolute shrinkage and selection operator (Lasso). The volatility index (VIX) can reduce the prediction errors and the Lasso algorithm significantly improve the training speed of the model. We predict the 5-minute closing prices of three datasets of index futures by VIX-Lasso-GRU Model. Comparing to the pure GRU and LSTM, we find that this new prediction model can improve the prediction efficiency with faster speed and higher accuracy.

Asymmetric responses of equity returns to changes in exchange rates at different market volatility levels

Our study aims to explore interactions between equity market returns and exchange rates at different market risk zones proxied by the Chicago Board Options Exchange Volatility Index (VIX). We analyze comovements between daily S&P 500 returns and three different USD exchange rates: the Federal Reserve's Nominal Broad U.S. Dollar Index, the Nominal Advanced Foreign Economies U.S. Dollar Index, and the USD in euro. The comovements are examined at three VIX zones (low, intermediate, and high) that we identify by employing the self-exciting threshold autoregressive SETAR(2,p) tests on daily data from January 03, 2006 to January 23, 2023. We subsequently employ VAR and conditional least square tests for S&P 500 returns and log changes in USD exchange rates with all showing the most robust transmission of shocks between equity returns and exchange rates in the high VIX zone. We further run Markov switching tests to identify specific jump periods from low to high responsiveness of equity returns to the USD exchange rate. Our tests show that interactions between equity returns and exchange rates are asymmetric, i.e., the exchange rate elasticity of equity returns is pronounced during periods of high market volatility and indiscernible at periods of low volatility. These findings may be useful for forecasting equity returns, exchange rates, as well as for asset pricing and portfolio diversification.

Examining the hedge performance of US dollar, VIX, and gold during the coronavirus pandemic: Is US dollar a better hedge asset?

This study utilizes the hedging potential of the U.S. Dollar Index (USDX) during the COVID-19 period, specifically comparing its positive effects on optimal portfolio weights and hedging ratios with those of traditional hedging assets, such as the VIX and gold. The scalar BEKK GARCH model is employed to forecast volatility and calculate hedging indicators. The results show that USDX exhibits strong hedging abilities against S&P 500 index volatility. These findings highlight the advantageous role of the USDX as a hedging instrument, particularly during periods of heightened market uncertainty, such as during the COVID-19 crisis. Despite the increased market volatility during the COVID-19 pandemic, the value of the optimal portfolio weights is stable and the volatility of the weights is significantly reduced, demonstrating the strength of the USDX's low risk and volatility in hedging against market fluctuations. Moreover, the increase in the hedge ratio indicates that more capital is allocated to hedging, reflecting the increased correlation between the USDX and S&P 500 index. These results emphasize the beneficial role of the USDX as a hedging instrument during times of elevated market uncertainty, such as during the COVID-19 crisis. Ultimately, USDX can provide valuable insights for market participants seeking effective hedging strategies.

Comparing the Performance of Developed and Emerging Market Equities during Economic Downturns

Purpose: This study compares developed and developing market stocks during the Russia-Ukraine crisis, a time of high geopolitical tensions. Economic downturns are complicated by geopolitical, financial, and natural disasters. A relevant case study to analyze equities market divergence under geopolitical uncertainty.
 Design/Methodology/Approach: This research quantifies stock market indexes, daily returns, and volatility indicators using historical financial data. Performance indicators, volatility patterns, and risk-return characteristics are compared across established and developing market stocks. The analysis seeks to determine if geopolitical tensions during the war increased market volatility and to understand market segment reactions. The analysis uses cumulative returns, average returns, standard deviations, volatility indexes, and the Sharpe ratio.
 Findings: It is found that clear geopolitical tensions during the crisis affected market volatility differently in established and new economies. This is because industrialized economies are financially stronger than developing ones. The Sharpe ratio allowed detailed market sector comparisons of risk-adjusted returns. This comparison showed investors' possible trade-offs between rewards and dangers.
 Implications/Originality/Value: These findings affect investors seeking educated strategies, politicians creating effective actions, and market participants managing risks. This research improves decision-making and risk management amid economic volatility by evaluating geopolitical events and stock market behaviors.

The Emerging Stock Markets and Their Asymmetric Response to Infectious Disease Equity Market Volatility (ID-EMV) Index

The infectious disease equity market volatility (ID-EMV) index, projected by Baker et al. (Baker, SR, N Bloom, SJ Davis, KJ Kost, MC Sammon and T Viratyosin (2020). The unprecedented stock market impact of COVID-19. Review of Asset Pricing Studies, 10(4), 742–758), relates infectious disease to equity market variability during the COVID-19 disease. The ID-EVM index examines the asymmetric influence on the stock market returns of seven developing countries: Mexico, Turkey, Brazil, China, Mexico, India and Indonesia. The investigation applies various statistical estimations, for instance, unit root, quantile cointegration and quantile-on-quantile regression (QQR) approaches. The relation between the stock returns of seven emerging economies and infectious disease EMV index is revealed by the quantile cointegration approach. Additionally, the QQR procedure shows that amid bullish market situation, stock returns are positively influenced by the infectious disease index. While, amid the bearish market situations, stock returns are negatively influenced by infectious disease index, the findings of this research have important policy implications. A piece of valuable information on the nexus between the variability in the equity market and the infectious disease index is provided by this investigation during the COVID-19 pandemic. Policymakers and investors can benefit from this newly introduced ID-EMV index to understand the influence on emerging market countries of this infectious disease.

Volatility impacts on global banks: Insights from the GFC, COVID-19, and the Russia-Ukraine war

This study analyzes the volatility impact of the Chicago Board Options Exchange Volatility Index (VIX) on the global banking sector during the Global Financial Crisis (GFC), COVID-19, and the Russia-Ukraine War. Using a Dynamic Conditional Correlation (DCC) model with asymmetric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) volatility, we examine three geographical regions, focusing on large banks. The key findings include significant symmetric Granger causality between volatility changes and negative bank returns during the GFC, asymmetric impacts of volatility increases and decreases in the lower quartile of bank returns, with COVID-19 exhibiting the strongest asymmetry, and volatility shocks affecting the downside risk of the banking sector, where the highest value-at-risk (VaR) levels occur in the GFC and the lowest during the war period. Finally, Asian banks demonstrated greater resilience to volatility impacts than European banks, which were the most affected by COVID-19 and the war. Overall, we find that volatility has less impact on the global banking sector in the war sample than in other crises. Our findings provide valuable insights for policymakers, investors, and regulators to help effectively manage future crises and ensure the stability of the global banking sector.

Volatility predictability in crude oil futures: Evidence based on OVX, GARCH and stochastic volatility models

The paper examines the volatility predictive ability of the CBOE crude oil volatility index (OVX), GARCH and Stochastic Volatility Models in the crude oil market. Specifically, the dynamics of two major crude oil pricing benchmarks — Brent in Europe and WTI in America are compared. OVX index is able to provide the optimal forecast for the volatility of Brent’s future. The GJR-GARCH model outperforms its competition volatility models in both oil markets. It also reveals that WTI exhibits a faster and stronger response to market shocks compared to Brent oil. Most importantly, the predictability exhibit consistent and convincing results during remarkable events: the 2014 oil price decline, the 2020 coronavirus pandemic, and the 2022 Russian–Ukraine war. In practice, the paper contributes to portfolio strategy construction and investment risk management.

Can technical indicators based on underlying assets help to predict implied volatility index

AbstractGiven the widespread use of technical analysis and the tight relationship between derivatives and the underlying assets, we employ the copula approach to investigate whether the technical indicators based on underlying assets convey extra information about the future movements of implied volatility (IV) indexes. The empirical results, based on long samples of five well‐known IV indexes, suggest that although the technical indicators are not informative for forecasting the future prices of IV indexes, they can provide extra information about the size of forecasting errors of the IV indexes. The findings are also robust to the impact of COVID‐19. The technical indicators are then used to extend Threshold ARCH and Exponencial GARCH models for improving the estimation of Value at Risks (VaRs). The out‐of‐sample forecast results show that the proposed model outperforms the benchmark in estimating the VaRs. These findings have implications for pricing options of IV indexes and managing the risks of IV‐related portfolios.

Forecasting gold price using machine learning methodologies

This study investigates the potential of advanced Machine Learning (ML) methodologies to predict fluctuations in the price of gold. The study employs data from leading global stock indices, the S&P500 VIX volatility index, major commodity futures, and 10-year bond yields from the US, Germany, France, and Japan. Lagged values of these features up to 10 previous days are also used. Four machine learning models are used: Random Forest, Gradient Boosted Regression Trees (GBRT), and Extreme Gradient Boosting (XGBoost), to forecast future gold prices. The study finds that the most influential stocks indices for prediction are one-day lagged data of ASX, S&P500, TA35, IBEX, and AEX, as well as U.S. and Japan bonds yields and delayed data of gas and silver. Furthermore, the study's models identify that one-day lagged VIX score and our VIX dummy variable have a significant impact on gold price, indicating that economic uncertainty affects gold prices. The results suggest that incorporating various financial indicators and moving averages can be a powerful tool for predicting future gold prices. GBRT and XGBoost can be valuable models for making informed decisions about gold investments.

An explainable AI-enabled granular ensemble machine learning framework to demystify fertilizer price movements

This paper proposes a novel explainable artificial intelligence (AI) driven ensemble machine learning (ML) framework for predicting fertilizer price movements and assessing the contributions of the technical and macroeconomic indicators. We integrate the Boruta algorithm, Maximal Overlap Discrete Wavelet Transformation (MODWT), Random Forest (RF), and explainable AI. The predictive analytics exercise utilises the residual of the previous stage as an additional indicator for arriving at the subsequent stage forecasts. We observe a significant influence of the residual in providing forecasts for time series with higher frequencies. The explainable AI is used at the global and local levels to explain the impacts of the indicators on fertilizer price movements. We have used monthly urea and diammonium phosphate (DAP) prices for nearly the last 30 years for predictive analytics. The explainable AI identifies the more significant impacts of the technical indicators compared to macroeconomic counterparts in forecasting urea and DAP prices at the global level. Also, the price movements of urea and DAP are similar at the global level. On the contrary, macroeconomic indicators influence more at the local level. The CBOE volatility index for urea, geopolitical risk, and commodity industrial input for DAP significantly influence the price movements at the local level.

Is a VIX ETP an investment in the VIX?

eturn and risk of these products are not related to the return and risk of the VIX index. The authors note that VIX ETPs do not correlate well to the VIX index. In fact, these funds are not even designed to have a high correlation to the VIX index. Individual investors can often mistake VIX ETPs for an investment in the VIX index itself, which is incorrect and may lead to a costly mistake.

The impact of macroeconomic uncertainty on the relationship between financial volatility and real economic activity

ABSTRACT A stock market volatility index is a widely used proxy of uncertainty in the macroeconomy, where a rise in the index dampens real economic activity. By contrast, the macroeconomic uncertainty index proposed by Jurado et al. (2015) measures the predictability of a wide range of macroeconomic indicators and thus, is a comprehensive indicator of macroeconomy-wide uncertainty. This study empirically investigates the nonlinear link between financial volatility and real economic activity based on the level of the macroeconomic uncertainty index. Employing data from the United States and Japan, the empirical analysis suggests that an increase in financial volatility lowers industrial production and business-fixed investment more persistently when macroeconomic uncertainty is higher.

Effective Factors under Stock Market Regimes

The Chicago Board Options Exchange (CBOE) Volatility Index (VIX) is a measurement of volatility in the stock market, which is closely associated with the return of the risk premium. This paper categorized the monthly log returns of VIX by Gaussian Mixture Models (GMM) and investigates the driving factors of VIX among the Equity Market Volatility (EMV) trackers under different regimes using the elastic net linear regression model. As a result of categorization, two regimes in log returns of VIX are found. Regime 1 with a lower mean covers most of the months, while regime 2 with a higher mean captures the months of extreme log returns. In months of both regimes, the policy-related factor significantly and independently affects VIX. Another factor that largely affects VIX in regime 1 is the macroeconomy and other factors have little impact on VIX in regime 1. Infectious disease, policy, and government related factors are more important in affecting VIX in regime 2.