Volatility Index Research Articles

Can gold- and USD-backed stablecoins hedge against US stock market volatility? Evidence from a quantile coherency approach

ABSTRACT Emerging markets are increasingly appealing to market participants seeking alternative investments. Using the Quantile Coherency method, this study investigates the interrelatedness between stablecoins backed by two types of traditional assets (gold and US dollars) and the volatility indices of the US stock market across different frequency domains and market conditions. The potential of gold- and USD-backed stablecoins to hedge stock market volatility varies. There is a stronger connection under long-term and extreme conditions than in the short-/medium- and normal markets. The empirical results suggest that the USD-backed stablecoins should be used as hedging assets against US stock market volatility (typically showing a non-negative correlation) rather than the gold-backed stablecoins (which often exhibit a negative correlation). These findings provide valuable insights for market participants and academic research.

The determinants of volatility connectedness of South African equity super sectors

The paper aims to explore the determinants of total volatility connectedness of nine super sectors on the Johannesburg Stock Exchange (JSE) market from 3rd January 2006 to 31st December 2021. These sectors are Automobile and Parts, Chemical, Telecommunication, Technology, Energy, Health, Finance, Insurance, and General Industrials. The paper applied Diebold and Yilmaz connectedness matrix and the time-varying parameter – vector autoregressive (TVP-VAR) model to determine the sectorial total volatility connectedness index (STVCI). After that, the nonlinear autoregressive distributed lag model (NARDL) was used to determine the asymmetric effects and the drivers of STVCI. It was found that the partial sum decomposition of the South African volatility index (SAVI) and Economic Policy Uncertainty Index (EPU) are the key determinants of the STVCI both in the long and short run. However, domestic market return (DMR) shows no significant asymmetric effect on STVCI. The study concluded that SAVI and EPU are the key determinants of volatility connectedness among the JSE super-sectors. The results unveil important implications for sectorial investors and policymakers on potential regulations and stability of the significant determinants of spillover risk.

Forecasting Equity Premium in the Face of Climate Policy Uncertainty

ABSTRACTThis study examines the role of the US climate policy uncertainty (CPU) index in forecasting the equity premium, employing shrinkage methods such as LASSO and elastic net (ENet) to dynamically select predictors from a dataset spanning April 1987 to December 2022. Alongside CPU, other uncertainty predictors like economic policy uncertainty (EPU), geopolitical risk (GPR), and the volatility index (VIX) are considered to assess their complementary roles in out‐of‐sample (OOS) equity premium forecasting. The results reveal that while CPU alone cannot consistently predict the equity premium, it provides crucial complementary information when combined with other predictors, leading to a statistically significant OOS of 1.231%. The relationship between CPU and the equity premium is time varying, with a stronger influence observed during periods of economic downturn or heightened uncertainty, as demonstrated by wavelet coherence analysis. This study also identifies CPU's significant impact on industry‐specific returns, particularly in climate‐sensitive sectors, offering valuable insights for investment strategies and risk management in an era of increasing CPU.

Fear, extreme fear and U.S. stock market returns

This paper investigates the multiscale co-movement between the levels and changes in VIX and SKEW indices and the returns of the S&P 500 market indices at the aggregate and sector levels of data. By utilizing the wavelet localized multiple correlation (WLMC) method over the most recent time period (January 3, 2006 - October 26, 2023), we demonstrate that very short-term market participants, such as traders and speculators, are watchful about the combined information from the VIX and SKEW levels; while the changes in VIX and SKEW are more informative for stock returns in the medium- to long-term, which should concern the decisions of investors and portfolio managers. We also show that VIX in general exerts a more dominant influence on the S&P 500 Index returns than SKEW, but more for VIX changes than for VIX levels. The very short-run informativeness of VIX and SKEW for stock returns is markedly pronounced around the European debt crisis (2012) and the COVID-19 meltdown (2020). The stock sector analysis reveals that defensive sectors are sensitive to the SKEW index, whereas the cyclical sectors are predominantly related to the fluctuations in the VIX index. Overall, our findings provide useful new insights into the stock market sentiment and the resulting market participants’ behavior at the market microstructure level. As such, they may be of interest not only to traders and investors, but also to financial analysts and government policy-makers in their attempts to understand the multiscale relationship between monetary policy and the stock market.

Spillovers between cryptocurrencies and financial markets in a global framework

We employ the Bayesian Global Vector Autoregression (BGVAR) model to examine the transmission of adverse shocks originating in the cryptocurrency market to global financial markets. The analysis shows that these spillover effects are not limited to a specific group of countries but are instead global in nature. The results indicate that shocks originating in the cryptocurrency market adversely affect stock markets, bond indices, exchange rates, and volatility indices. These shocks, while typically moderate in magnitude and short in duration, suggest that cryptocurrencies act as mediators of short-term negative shocks. The study also underscores the heterogeneous nature of these impacts across different financial markets and countries, highlighting the varying sensitivities and responses to cryptocurrency market fluctuations. Importantly, this research represents the first application of the GVAR model in the context of the cryptocurrency market, to the best of our knowledge.

The effect of COVID-19 vaccine on the international financial markets

Existing studies on the COVID-19 pandemic and vaccine development have primarily documented their effects on stock markets, with limited attention to international financial markets. This paper constructs a news-based sentiment index on COVID-19 vaccine development and examines its effects on international financial markets during the first year of the pandemic. We find positive vaccine sentiment decreases the CBOE Volatility Index (VIX) while boosting capital inflows and currency appreciation in emerging markets. It also reduces the unit price of risk in the context of carry trade risk premiums, as positive developments lower future risk and economic uncertainty.

Forecasting the volatility of crude oil futures: New evidence from jump-induced volatility

This paper proposes an augmented heterogenous autoregressive (HAR) model with time-varying jumps to forecast the realized volatility (RV) of crude oil futures. Jump-induced volatility of crude oil futures is obtained from a GARCH-jump process, then used to augment the HAR model. The results based on both the in-sample and out-of-sample analyses suggest that jumps offer added information for forecasting the RV of crude oil futures, surpassing the incremental information contained in the crude oil implied volatility index (OVX). Various robustness tests confirm these findings. Our findings have key implications for energy market investors, risk managers, and policymakers.

Monetary Policy Instruments and Stock Market Returns Volatility in Nigeria

The study examines monetary policies instruments impact on stock market volatility in Nigeria for the period of 1993-2022. The study identified the independent variables, namely; reserve requirement (RR), cash reserve ratio (CRR), discount rate (DR) and money supply (MS) which were analyzed in relation to stock market volatility proxied with all share index volatility (ASIV). The data was analyzed with descriptive statistics, correlation matrix several diagnostics tests (VIF, validity test, ADF and Johansen tests) and the multiple regression analysis. The findings revealed that RR, CRR and MS have significant effect on ASIV while DR has insignificant effect on ASIV in Nigeria. It was concluded that reserve requirement, discount rate and money supply exert significant effect on Nigerian economy. Thus, it thereby recommended that management of CBN should put modalities in place to aggressively control ASIV in Nigeria. CBN should put measures in place to increase CRR. This will provide banks opportunity to fund economic transaction in the Nigerian economy, thereby reducing ASIV.

Connectedness Structure and Volatility Dynamics Between BRICS Markets and International Volatility Indices: An Investigation

ABSTRACTThis research aims to explore and understand the dynamic nature of volatility connectedness between BRICS stock markets and various asset price implied volatility indices through a TVP‐VAR broadened connectedness approach. Results display nontrivial dynamic connectedness in the BRICS stock markets and uncertainties in different markets during the period 31 March 2019–31 August 2023. They also report heterogeneous patterns in the connectedness between stock indices and volatility indices. The time‐varying spillover effect seems to be strong during the black‐swan events. The variations of volatility connectedness among each volatility index and stock market increasingly depend on unusual stress caused by the outbreak of unexpected events. These finding provide significant guidance for investors seeking to enhance their risk management practices. By leveraging the insights into volatility transmission mechanisms and the roles of different volatility indices, investors can make informed decisions to protect and grow their investments in an increasingly volatile global market.

Bayesian estimation of stochastic volatility jump diffusion model parameters using S&P 500 and VIX data

The Stochastic Volatility Jump Diffusion (SVJD) model is a common tool used for option pricing. Existing literature has employed many techniques in order to estimate the parameters of this model, including both Bayesian and frequentist approaches. In 2010, Duan and Yeh [Jump and volatility risk premiums implied by VIX. J Econ Dyn Control. 2010;34(11):2232–2244] used a frequentist approach with maximum likelihood estimation that included the VIX, the Chicago Board Options Exchange (CBOE) Volatility Index, which carries real time S&P 500 option price information in addition to the S&P 500 data. In this paper, we combine the previous Bayesian approach in Cape et al. [Estimating Heston's and Bates' models parameters using Markov Chain Monte Carlo simulation. J Stat Comput Simul. 2015;85(11):2295–2314] with the inclusion of VIX by Duan and Yeh. We provide the derivations of the conditional posterior distributions of the SVJD model's parameters and a guide to a Markov Chain Monte Carlo (MCMC) algorithm used to estimate the parameters. We apply the algorithm to the historical S&P 500 and VIX data and provide the subsequent parameter estimates. We offer these tools for others performing similar research with our R code available upon request.

Predicting the volatility of Chinese stock indices based on realized recurrent conditional heteroskedasticity.

The realized recurrent conditional heteroscedasticity (RealRECH) model improves volatility prediction by integrating long short-term memory (LSTM), a recurrent neural network unit, into the realized generalized autoregressive conditional heteroskedasticity (RealGARCH) model. However, at present, there is no literature on the ability of the RealRECH model to fit and predict volatility in the Chinese market. In this paper, a study is conducted to test the in-sample explainability and out-of-sample prediction ability of the RealRECH model for the SSE50, CSI300, CSI500 and CSI1000 indices in the Chinese market and to determine whether it performs better than the RealGARCH model. The results of the in-sample analysis show that the RealRECH model not only provides better in-sample interpretability for all four indices but also captures the complex dynamics of time series volatility that the RealGARCH model cannot capture, such as long-term dependence and nonlinearity. The results of out-of-sample volatility prediction show that the RealRECH model better predicts the volatility of the CSI500 and CSI1000 indices but yields worse predictions for the SSE50 and CSI300 indices. Thus, the RealRECH model can be used for CSI500 and CSI1000 prediction.

Optimal Market Completion through Financial Derivatives with Applications to Volatility Risk

This paper investigates the optimal choices of financial derivatives to complete a financial market in the framework of stochastic volatility (SV) models. We first introduce an efficient and accurate simulation-based method applicable to generalized diffusion models to approximate the optimal derivatives-based portfolio strategy. We build upon a double optimization approach, i.e., expected utility maximization and risk exposure minimization, already proposed in the literature, demonstrating that strangle options are the best choices for market completion within equity options. They lead to lower investors’ risk exposure for a wide range of strikes compared to the lesser flexibility of calls, puts, and strangles. Furthermore, we explore the benefit of using volatility index derivatives and conclude that they could be more convenient substitutes when short-term maturity equity options are not available.

Capital Resilience: Dynamics Movements of Large Cap and Small-Mid Cap Stock in Global Macroeconomic Uncertainty

This research was conducted to examine how large and small-medium cap stocks in Indonesia respond to movements in global macroeconomic variables (Federal funds rate, M2 of Dollar, S&P 500, CBOE Volatility Index) and to determine the extent of the contribution of each variable in influencing both of stock prices. The study period spans from January 2022 to July 2023, during a period of global economic instability due to excessive quantitative easing post-Covid-19 pandemic. This research is quantitative, and the Vector Error Correction Model (VECM) was chosen as the method for analysis. The findings of the study, analysed using Impulse Response Functions (IRF), indicate that small-medium cap stocks represented by DBX are more significantly impacted than large-cap stocks represented by IDX30 when there is a shock at a particular moment; it can be seen from standard deviation values in the IRF output, suggesting a higher level of volatility in small-medium cap stocks. Further analysis using Forecast Error Variance Decomposition (FEVD) reveals that the movements of both IDX30 and DBX in the short run are predominantly influenced by their past values at t-1, indicating inefficient market conditions based on the Efficient Market Hypothesi. However, these conditions are expected to shift gradually towards more efficiency in the long run. Regarding the examined macroeconomic variables, it was found that the Federal funds rate and S&P 500 have a more significant impact on large-cap stocks than small-medium-cap stocks. Conversely, the M2 of Dollar and CBOE Volatility Index is more dominant in small-medium-cap stocks than large-cap stocks. This study offers valuable insights and considerations for investors seeking to diversify their stock portfolios according to their objectives and risk profiles.

Asymmetric impact of energy prices on financial cycles based on interval time series modeling

Energy prices crucially affect financial cycles. We employ the bivariate empirical mode decomposition (BEMD) model to disaggregate the daily interval data of the volatility index (VIX) and rely on the threshold autoregressive interval model (TARI) to incorporate energy prices into the forecasting model. Furthermore, the threshold interval decomposition ensemble (TIDE) is used to forecast the VIX series with nonlinearities to improve the forecasting accuracy. Moreover, we apply the root mean square error (RMSE) and the Diebold–Mariano test (DM) to evaluate the TIDE model performance across various frequency components and the final integration results. This paper demonstrates a significant correlation between energy prices and financial cycles, along with a temporal asymmetry effect. While the impact of energy prices on improving VIX forecasting is minimal in the short term, it becomes substantial over medium and long terms. Specifically, the influence of liquefied petroleum gas (LPG) prices on the VIX is notable in both medium and long terms. Our results offer new insights and methodologies for predicting financial cycles, assisting investors in evaluating volatility-related exchange-traded products. Additionally, these findings are crucial for developing more effective policies to promote green energy development.

FORECASTING CBOE OPENING VALUES: AN EFFECTIVE ARIMA APPROACH WITH TRANSFORMATION AND MODEL DIAGNOSTICS

This study investigates the use of an ARIMA model, coupled with Monte Carlo simulation, to forecast the opening value of a Volatility Index (VIX) time series. The data obtained from the Chicago Board Options Exchange (CBOE) for the years 1992–2019 have been transformed into stationary data using a detrend method and first-order difference. The Augmented Dickey-Fuller (ADF) test is used to ensure the data are adequately transformed. The autocorrelation function (ACF) and partial ACF (PACF) are then used to identify series with serial correlation and determine whether an autoregressive (AR) model is appropriate. Significant moving average (MA) lags are also determined for model identification. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) are used to find the best-fit ARIMA model. Among the evaluated ARIMA models, which ranged from a random walk ARIMA(0, 1, 0) to ARIMA(2, 1, 2), the ARIMA(2, 1, 2) model was found to be the most optimal, exhibiting the lowest AIC and BIC values. This model was then used to forecast the opening value for the year 2014, using 2013 data as the real data. The generated ARIMA(2, 1, 2) model demonstrates reasonable alignment with the actual 2014 data, suggesting its potential for forecasting in this context. Additionally, Monte Carlo simulations are employed to assess the model’s robustness by generating a range of potential outcomes, providing a more comprehensive understanding of the uncertainty associated with the forecasts.

Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation

The Bitcoin, one of the most discussed event of recent times, is the first cryptocurrency to be released. Bitcoin has the biggest market capitalization in the cryptocurrencies, and it is a decentralized payment tool that cannot be controlled by any government institution. Cryptocurrencies operate on top of a decentralized network, recording and verifying transactions using a technology called Blockchain. Many bubble price formations occurred in financial markets throughout history. Cryptocurrency markets are prone to manipulation because they are not connected to any center. This study aims to to investigate the buble formations in Bitcoin (BTC) US Dollar price for the period of 11 December 2017 - 31 July 2023, and also to determine the main financial variables affecting Bitcoin prices. ARDL Bounds test proves cointegration between Bitcoin price and dependent variables. Error Correction Model determines positive connection between BTC and Nasdaq100 index. A daily change in Nasdaq100 index leads to a 0.58% increase in BTC price. Model captures also negative relationship between Volatility Index, USD Index and BTC price. No long-term relationship emerges in the results. In the long run, unique and different variables shape BTC price formation.

Multi-model transfer function approach tuned by PSO for predicting stock market implied volatility explained by uncertainty indexes

This paper studies the forecasting power of uncertainty emanating from the commodities market, energy market, economic policy, and geopolitical threats to the CBOE Volatility Index (VIX). In this study, the relationship between the various uncertainty metrics throughout the period 2012—2022, using a multi-model transfer function technique optimized by particle swarm optimization (PSO) is estimated. Furthermore, we utilize PSO for parameter optimization within the multi-model framework, improving model performance and convergence speed. According to empirical findings, the CBOE Volatility Index reacts nonlinearly to the uncertainty indices. Specifically, the conclusions of the performance metrics show that the OVX index (MAPE: 4.1559%; RMSE: 1.0476% and W: 96.74%) outperforms the geopolitical risk index, the Bloomberg energy index, and the economic policy uncertainty index in predicting the volatility of the US equities market. Although individual models have generated respectful performance, results from the aggregate simulation show that when all predictors are combined, they simultaneously provide better performance indicators (MAVE: 2.7511 %; RMSE: 0.7361%; R2: 98.93%) than when they are estimated separately. In addition, results provide evidence that, when considering non-linear patterns in the data, the multi-model transfer function technique calibrated using PSO demonstrates its outperformance over autoregressive baseline models, traditional econometric models, and deep learning techniques. The effectiveness and accuracy of the multi-model transfer function method tuned by PSO as a forecasting tool are confirmed by the convergence analysis of the cost function. Our methodology innovates by employing a multi-model transfer function technique, which captures the complex and nonlinear relationships between uncertainty indicators and the VIX more comprehensively than traditional single-model approaches. These results are important for traders in terms of hedging as well as portfolio diversification by investing in defensive equities and for policymakers in terms of reliability and preciseness of volatility forecasts.

Forecasting of the Korean Stock Market Volatility Index Using a Modified Multi-Input LSTM Model

Forecasting of the Korean Stock Market Volatility Index Using a Modified Multi-Input LSTM Model

Quantile connectedness between social network sentiment and sustainability index volatility: Evidence from the Moroccan financial market

The goal of this paper is to investigate the connectedness between investor sentiment and volatility on the environmental, social, and governance index (ESG) in Morocco. Therefore, on the basis of an investor sentiment index constructed from the X platform, and using quantile and frequency connectedness approaches, the findings reveal a significant connectedness between sentiment and ESG volatility, particularly during turbulent events. Although sentiment acts most of the time as a net receiver of shocks, notably during the COVID-19 pandemic and geopolitical crises, it sometimes becomes a net sender of shocks. Furthermore, the spillovers between sentiment and ESG volatility are determined mainly by the long-term component, especially during extreme events, implying the persistence of shock transmission due to high uncertainty. The results also illustrate the impact of market conditions on the spillovers between sentiment and ESG volatility. The conclusions of this study provide useful guidance for pro-ESG investors, policymakers, and companies.

Research on the volatility forecasting model of KOSPI index returns using AR(M)-GARCH(P,Q) model

In this study, we estimated the volatility of the KOSPI index returns and analyzed volatility trends. The data used in the study consisted of monthly observations from January 2005 to December 2022, and the KOSPI index raw data was transformed into log returns. The volatility estimation model used the AR(m)-GARCH(p,q) model, which combines the autoregressive error model and the GARCH model that explains the persistence of volatility at low orders. The goodness of fit of the model was confirmed using the Portmanteau Q-test and LM-test. Applying the autoregressive error model revealed significant autocorrelation in the log returns of the KOSPI index at lags 3 and 6. Residual analysis indicated that the residuals followed white noise, but the squared residuals exhibited heteroscedasticity. Therefore, after fitting the autoregressive error model, we applied the GARCH model and conducted residual analysis, finding both the residuals and squared residuals significant at a 5% significance level. The volatility forecasting results indicated a continuous increase in volatility. The findings of this study are expected to provide important implications for policymakers responsible for risk management in the Korean stock market.