Volatility Index Research Articles

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.

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.

Market resilience in turbulent times: a proactive approach to predicting stock market responses during geopolitical tensions

PurposeThe Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.Design/methodology/approachHybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.FindingsOur study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.Originality/valueTo the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

Incorporating Different Sources of Information for Bayesian Optimal Portfolio Selection

This article introduces Bayesian inference procedures for tangency portfolios, with a primary focus on deriving a new conjugate prior for portfolio weights. This approach not only enables direct inference about the weights but also seamlessly integrates additional information into the prior specification. Specifically, it automatically incorporates high-frequency returns and a market condition metric (MCM), exemplified by the CBOE Volatility Index (VIX) and Economic Policy Uncertainty Index (EPU), significantly enhancing the decision-making process for optimal portfolio construction. While the Jeffreys’ prior is also acknowledged, emphasis is placed on the advantages and practical applications of the conjugate prior. An extensive empirical study reveals that our method, leveraging this conjugate prior, consistently outperforms existing trading strategies in the majority of examined cases.

Forecasting the CBOE VIX and SKEW Indices Using Heterogeneous Autoregressive Models

We analyze the predictability of daily data on the CBOE VIX and SKEW indices, used to capture the average level of risk-neutral risk and downside risk, respectively, as implied by S&P 500 index options. In particular, we use forecast models from the Heterogeneous Autoregressive (HAR) class to test whether and how lagged values of the VIX and of the SKEW may increase the forecasting power of HAR for the SKEW and the VIX. We find that a simple HAR is very hard to beat in out-of-sample experiments aimed at forecasting the VIX. In the case of the SKEW, the benchmarks (the random walk and an AR(1)) are clearly outperformed by HAR models at all the forecast horizons considered and there is evidence that special definitions of the SKEW index based on put options data only yield superior forecasts at all horizons.

The Real Equilibrium Exchange Rate of the Czech Koruna – the BEER Approach

The aim of this paper is to develop a model of the real equilibrium CZK/EUR exchange rate with all relevant explanatory variables. Emphasis will be placed on examining the longest possible time horizon, with a period of real convergence, but also economic stagnation. The model is based on the theoretical BEER approach using a cointegration or error correction model that distinguishes between short-term and long-term relationships. The results show that the Czech koruna strengthens when the productivity differential, terms of trade differential or gross fixed capital formation increase, while the koruna weakens when the VIX index representing global risk aversion increases. For the real interest rate differential, the hypothesis that the koruna strengthens in the short run during the improvement but weakens in the long run probably due to the country's risk premium, was confirmed. Moreover, it turned out that the start of the European Central Bank's quantitative easing led to strengthening of the Czech koruna, while the start of the CNB's foreign exchange interventions in 2013 led to weakening of the Czech koruna.

A global flash drought inventory based on soil moisture volatility

Flash droughts, characterized by rapid onset and development, present significant challenges to agriculture and climate mitigation strategies. Operational drought monitoring systems, based on precipitation, soil moisture deficits, or temperature anomalies, often fall short in timely detection of these events, underscoring the need for customized identification and monitoring indices that account for the rapidity of flash drought onset. Recognizing this need, this paper introduces a global flash drought inventory from 1990 to 2021 derived using the Soil Moisture Volatility Index (SMVI). Our work expands the application of the SMVI methodology, previously focused on the United States, to a global scale, providing a tool for understanding and predicting these rapidly developing phenomena. The dataset encompasses detailed event characteristics, including onset, duration, and severity, across diverse climate zones. By integrating atmospheric variables through their impact on soil moisture, the inventory offers a platform for analyzing the drivers and impacts of flash droughts, and serves as a large, consistent dataset for use in training and evaluating flash drought prediction models.

Are Indian markets insulated from the impact of cryptocurrencies? Unveiling the volatility linkages through multi‐index dynamic multivariate GARCH analysis

AbstractThis paper investigates the dynamic relationships between the volatility of Bitcoin and major Indian stock market indices. Employing a dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) model, we explore how volatility shocks and information flow influence the correlations between these asset classes. Our findings reveal a key characteristic: volatility spillovers tend to be short‐lived, indicated by a relatively low DCC‐GARCH parameter (dcca1). This suggests that while a surge in volatility in one market might lead to a temporary increase in correlation with the other, this heightened correlation is unlikely to persist for extended periods. However, the model also highlights a high DCC‐GARCH parameter (dccb1), signifying that the correlations themselves are responsive to new information. This implies that volatility linkages can adjust rapidly in response to market events or economic data releases. To enhance accessibility for a broad audience, we translate these findings into economic intuitions. We illustrate how the model can be interpreted through real‐world examples, such as the impact of sudden policy changes in India or global market flash crashes. By understanding the short‐lived nature of volatility spillovers and the responsiveness of correlations, investors in the Indian markets can make more informed decisions when considering the potential influence of Bitcoin's volatility while contributing to a deeper understanding of the dynamic interactions between cryptocurrency and traditional financial markets in the Indian context.

Assessing the VlX's lmpact on SPY Returns in Response to the Federal Reserve's 2022 Rate Hike

This study examines the relationship between the CBOE Volatility Index (VIX) and the returns of the SPDR S&P 500 ETF (SPY) before and after the Federal Reserve's first interest rate hike in 2022. The analysis covers an eight-month period, centered on the date of interest rate changes. The reason to use this period because it is the first hike after the pandemic. A time series approach is used, constructing multi-linear regressions to evaluate the pre-hike and post-hike periods. The pre-hike model did not find any significant predictors, suggesting that market dynamics and the role of VIX were not aligned with SPY returns. However, after the interest rate hike, the VIX rate became significant indicators, reflecting the market's increased sensitivity due to the new interest rate environment. This transformation highlights the crucial role of the VIX as a gauge of market sentiment and risk perception in response to significant monetary policy actions.

Demand Risks and Term Structure of Volatility Index Futures

In this paper, we develop an equilibrium framework to explain the characteristics of volatility index (VIX) futures prices and returns across maturities. In the framework, the investors prefer VIX futures with specific maturities, and the arbitrageurs optimize portfolios based on mean-variance preferences. The model-implied futures prices are affected by the variance and demand risk factors. Theoretical and empirical analyses show that incorporating jump risks helps to explain the higher-order moments of futures and that including the demand factor improves futures pricing performance.

Tourism and uncertainty: a machine learning approach

ABSTRACT In this paper, we attempt to create a unique forecasting model to forecast out-of-sample the tourism demand in 24 European Union countries. The initial dataset included 34 relevant variables of annual frequency that span the period from 2010 to 2020 for 40 countries. A data prefiltering process resulted in a final set of 17 relevant variables for 24 countries. Additionally, in the effort to investigate the impact of uncertainty on international tourism, apart from the traditional factors that affect tourism, we also include variables that measure various forms of uncertainty: we use the World Pandemic Uncertainty (WPU) Index, the Global CBOE Volatility Index, the Political Globalisation Index, the Economic Globalisation Index, and the Political Stability Index. In the empirical part of our research, we employ and compare in terms of their forecasting accuracy a set of six state-of-the-art machine learning algorithms, the Support Vector Regression with both a linear and an RBF kernel, the Random Forests, the Decision Trees, the KNN, and gradient-boosting trees. The results show that the Gradient-Boosting Trees algorithm outperforms the other five models providing the most accurate forecasts with a MAPE of 0.10% and 1.36% in the training and the out-of-sample tests, respectively.