Volatility Index Research Articles

THE DURATION OF THE IMPACT OF THE “INVESTOR FEAR INDEX” ON THE RUSSIAN STOCK MARKET

In the process of investing during the period of expectation of a new global economic crisis, it is important to sell existing shares on time. In this regard, the topic of research on the impact of the “investor fear index” on the Russian stock market is relevant. The purpose of the research is to establish the duration of the impact of the CBOE Volatility Index (VIX) on the American and Russian stock markets. To achieve this goal, the following tasks have been solved: theoretical issues of the VIX relationship with stock markets, global economic crises, risk-free assets, monetary incentives have been considered; an appropriate research methodology has been selected; economic and mathematical models have been built reflecting the relationship of the stock markets of the United States and Russia with the “investor fear index”. The results of the study work showed that after the impact of high values of the “investor fear index”, signaling the onset of a new global economic crisis, the Russian stock market will recover and grow within twelve years, the price of shares of Sberbank PJSC — within seven years, the price of shares of Gazprom PJSC — within four years. The results of tainted have practical importance for long-term investors.

Impact of Crypto Assets as Risk Diversifiers: A VAR-based Analysis of Portfolio Risk Reduction

This research aims to empirically investigate the portfolio risk associated with crypto assets. In other words, we want to investigate whether the inclusion of crypto assets in a portfolio can minimize the portfolio risk or not, because it is argued that there is a lower degree of correlation between crypto assets and traditional assets. In order to achieve our research objectives, we employ the Vector Autoregressive Model (VAR) by using five different asset classes. The first two variables are taken from the crypto assets, Bitcoin and Ethereum, and the remaining three variables for Gold, Crude Oil and VIX (Chicago Board Options Exchange's (CBOE) volatility index). Our research strategy will be based on an analysis for unit root, optimal lag selection, coefficient matrix, checking VAR stability, the Granger causality test, and impulse response function (IRF). Our findings suggest that none of the indicators of traditional assets drive and explain Bitcoin. We also found that only Bitcoin is significantly related to Ethereum. while none of the other variables are statistically useful to explain the variation in the Ethereum. Based on these findings it can be recommended that the inclusion of crypto assets into a portfolio reduces risk because none of the indicators of crypto assets are significantly related to the indicators of traditional assets.

Can ETFs affect U.S. financial stability? A quantile cointegration analysis

This study evaluates whether exchange traded funds (ETFs) threaten financial market stability by testing two hypotheses relating the growing importance of ETFs to increased market volatility and rising equity valuations. We estimate quantile cointegration models using Standard & Poor's 500 Index (S&P 500) and Chicago Board Options Exchange volatility Index (VIX) data for 1994–2020. We found that an increase in ETFs is positively and significantly related to the long-term valuation of the S&P 500 for quantile values above the median. By contrast, ETFs have only a negative and significant effect on the VIX for quantiles around the median. Ultimately, two novel results were obtained. First, the distortion in the value of the S&P 500 relative to its fundamentals is driven by investor flow into ETFs during a bull market. Second, the impact of equity ETFs on the VIX is only affected when fundamental factors are in play, decreasing it. Therefore, ETFs contribute to forming equity bubbles and support valuation market dynamics. Both regulators and policymakers should consider these conclusions.

The rough Hawkes Heston stochastic volatility model

AbstractWe study an extension of the Heston stochastic volatility model that incorporates rough volatility and jump clustering phenomena. In our model, named the rough Hawkes Heston stochastic volatility model, the spot variance is a rough Hawkes‐type process proportional to the intensity process of the jump component appearing in the dynamics of the spot variance itself and the log returns. The model belongs to the class of affine Volterra models. In particular, the Fourier‐Laplace transform of the log returns and the square of the volatility index can be computed explicitly in terms of solutions of deterministic Riccati‐Volterra equations, which can be efficiently approximated using a multi‐factor approximation technique. We calibrate a parsimonious specification of our model characterized by a power kernel and an exponential law for the jumps. We show that our parsimonious setup is able to simultaneously capture, with a high precision, the behavior of the implied volatility smile for both S&P 500 and VIX options. In particular, we observe that in our setting the usual shift in the implied volatility of VIX options is explained by a very low value of the power in the kernel. Our findings demonstrate the relevance, under an affine framework, of rough volatility and self‐exciting jumps in order to capture the joint evolution of the S&P 500 and VIX.

THE IMPACT OF THE COVID-19 PANDEMIC AND THE RUSSIAN INVASION OF UKRAINE ON GOLD MARKETS

Purpose – The study examines global Gold market performance and correlations between COVID-19, the Russian invasion, inflation, investors’ fear, asymmetric shocks, and the VIX (volatility index) impact on volatility. Research Methodology – This research uses an econometric approach to analyse the impact of COVID-19 and the Russian invasion on Gold market performance – specifically the ARCH (Autoregressive Conditional Heteroskedasticity) – GARCH (Generalized Autoregressive Conditional Heteroskedasticity) Model and the Threshold-Asymmetric ARCH Model. Findings – The study reveals that the COVID-19 pandemic and the Russian invasion of Ukraine have significantly impacted Gold market dynamics. Inflation significantly impacts volatility, emphasising the need for monitoring inflation rates. Investor fear increases demand during uncertainty periods, while negative shocks have a higher asymmetric effect. The VIX positively affects Gold market volatility, suggesting a perceived safe-haven status during increased volatility. Research Limitations – Gaps in the data related to COVID-19 cases, geopolitical events, and Gold market performance could potentially introduce inaccuracies, biases or limitations to the analysis. Practical Implications – The study’s findings have practical implications for investors, analysts, and policymakers involved in the financial investment of Gold markets. Originality/Value – This study provides new insights into the relationships between inflation, investor fear, asymmetric shocks, and the impact of the VIX on Gold market dynamics during economic and geopolitical uncertainty.

RATIONALE OF FINANCIAL STABILITY IN SOUTH AFRICA: CONSTRUCTING A FINANCIAL STRESS INDEX

The need for financial stability became apparent after the devastating events of the 2008 financial crisis. It was around this time that central banks around the world started to construct and implement scientific methods to assess financial stability. This research gives a comprehensive understanding of financial stability and makes use of scientific methodology by constructing the financial stress index for South Africa from periods 2006 through 2015 to predict and attain financial stability. After the 2008 global financial crisis, South African policymakers have put in place some policies and tools to enhance financial stability. To complement these policies, a new financial framework was developed, that is, the twin peaks model to continuously gather data and asses financial stability on a continuous basis. To put this framework into practice the financial stress indicator or index acted as a single comprehensive tool to measure and monitor stability. Included in this index are indicators that capture the different dimensions of the banking sector. The results gathered from this index contribute to the understanding of financial stability and how it is measured. It was found that the volatility or patterns of the financial stress index could be used as predictions and early warning tools for policymakers or the Central Bank to step in and contain potential risks. Moreover, from the empirical data collected the financial stability policies in South Africa are effective, as the financial stability index after the period of crises remained stable.

Effects of Inflation, Ten-Year Bond Yield Rate, and VIX Index on the Stock Prices of Banks Across All Three Market Capitalizations in India

This study investigates the impact of critical economic factors—namely, inflation, the 10-year bond yield rate, and the VIX index—on the stock prices of banks operating across different market capitalization segments in India. Through a comprehensive regression analysis framework, this research quantifies the relationships between these economic factors and bank stock prices while accounting for potential variances across large-cap, mid-cap, and small-cap banks. Utilizing data from the past five years, this analysis not only provides a nuanced understanding of how these macroeconomic indicators influence bank stock prices but also explores the specific effects on banks of varying market capitalizations. The findings reveal that small-cap companies are predominantly influenced by internal management decisions and capital allocation, whereas the consumer price index significantly predicts and reflects stock price behavior. Conversely, the bond yield rate and VIX index show minimal impact on stock prices. This study offers valuable insights for investors, policymakers, and financial institutions, aiding in the development of informed investment strategies and risk management practices.

Terrorism and its impact on the stock market: broad results from Tunisia

PurposeThe purpose of this study is to investigate the impact of terrorist attacks on the volatility and returns of the stock market in Tunisia.Design/methodology/approachThe employed sample comprises 1250 trading day from the Tunisian stock index (Tunindex) and stock closing prices of 64 firms listed on the Tunisian stock market (TSM) from January 2011 to October 2015. The research opts for the general autoregressive conditional heteroscedasticity (GARCH) and exponential generalized conditional heteroscedasticity (EGARCH) models framework in addition to the event study method to further assess the effect of terrorism on the Tunisian equity market.FindingsThe baseline results document a substantive impact of terrorism on the returns and volatility of the TSM index. In more details, the findings of the event study method show negative significant effects on mean abnormal returns with different magnitudes over the events dates. The outcomes propose that terrorism profoundly altered the behavior of the stock market and must receive sufficient attention in order to protect the financial market in Tunisia.Originality/valueVery few evidence is found on the financial effects of terrorism over transition to democracy cases. This paper determines the salient reaction of the stock market to terrorism during democratic transition. The findings of this study shall have relevant implications for stock market participants and policymakers.

Forecasting volatility of stock indices: Improved GARCH-type models through combined weighted volatility measure and weighted volatility indicators

This paper proposes an unbiased combined weighted (CW) volatility measure and weighted volatility indicators (WVI) that integrates the return- and range-based volatility measures to model the dynamics volatility of stock returns. The main feature of the CW measure is that it is formulated based on the weighted inter- and intra-price information to quantify the volatility directly, while the WVI effectively identifies signals on the shift of volatility. Empirical analysis using five stock indices demonstrates that the CW measure, utilising squared returns in combination with range-based Garman-Klass volatility measure, exhibits the lowest losses based on root mean squared error and quasi-likelihood when compared to 5-minute realised volatility as a proxy for true volatility. Furthermore, we investigate the feasibility of incorporating the CW measure and WVI as the exogenous variable(s) in the generalised autoregressive conditional heteroscedasticity (GARCH)-type models to enhance the forecasting performance. The findings indicate that the GARCH-CW-WVI and EGARCH-CW-WVI models exhibit superior in-sample model fit based on the Akaike information criterion than the existing GARCH and EGARCH models. Moreover, our proposed models also offer the best out-of-sample forecasts evaluated using various loss functions and further tested using Hansen’s model confidence set based on the mean squared error loss. Different risk levels of value-at-risk (VaR) and expected shortfall (ES) forecasts based on GARCH-CW-WVI and EGARCH-CW-WVI models are computed and examined with various backtests to confirm the accuracies of VaR and ES forecasts.

The Impact of Investor Sentiment on Value and Growth Stocks Return

The aim of this research is to investigate the impact of investor sentiment on value and stocks return. Two investor sentiment indicators, volatility index VIX and sentiment index are used as independent variables. Technical proxies (relative strength index, psychological line index, trading volumes and adjusted turnover rate) are used to construct sentiment index using principal component analysis. The dependent variable is value and growth stocks return formed based on the book to market ratio. Three Fama-French factors are used as control variables to isolate the impact of investor sentiment from systematic risk and fundamental factors. We use companies listed on Kompas 100 index over the sample period February 2017-January 2023. The sampling technique was carried out by purposive sampling and obtained 45 companies as the research samples. The empirical analysis employs multiple regression analysis using 1.385 daily time series computed by Microsoft Excel and Stata 13. The regression results indicate that volatility index VIX has a negative significant effect on value and growth stocks return. Sentiment index has a positive and significant effect on value stocks return but does not have a significant effect on growth stocks return. Investor sentiment has a greater effect on value stocks than growth stocks.

A multi-dimensional assessment of the accuracy of analyst target prices

This paper provides the first assessment for the quality of analyst target forecasts within an emerging market. We find that analysts' target forecasts tend to commit systematically upward bias (9.4%), large absolute pricing error (24.8%), over-prediction of the actual price changes (21%), and a low proportion (54%) of correct directional forecasts. The forecasting quality decays over time as the information in the target prices becomes obsolete, long before the one-year expiry period indicated in the analyst reports. Nevertheless, analysts’ target forecasts consistently outperformed alternative forecasts generated solely based on historical data, which confirms the price discovery function of analyst forecasting. A number of factors are found to be influential to the target performance. Target quality is adversely associated with stock idiosyncratic risk, prior index volatility, the aggressiveness of the target forecasts, and the optimism of consensus targets. In addition, more accurate target prices are delivered by brokerages that have prior experience with the industry and the companies under review, but not by brokerages that possess monopoly power on the stock-specific information.

EFFECT OF MACROECONOMIC DYNAMICS ON STOCK MARKET RETURN VOLATILITY IN NIGERIA: ARDL APPROACH

The study looked at how macroeconomic factors influenced stock market return volatility in Nigeria. To achieve this goal, macroeconomic dynamics were proxied with Money Supply (MS), Interest Rate (INTR), Exchange Rate (EXCHR), Inflation Rate (INFLR), and Real Gross Domestic Product Growth Rate (RGDPGR), while stock market (SMKT) return volatility was proxied by All Share Index Volatility (ASIV). The information was gathered from the CBN Statistical Bulletin from 1985 to 2021. It was then analysed using Descriptive Statistics, Unit Root Test, and Autoregressive Distributive Lag (ARDL) analysis. The data demonstrated that MS has a positive inconsequential influence on ASIV in the short run but a positive consequential impact on ASIV in the long run, as shown by p-values (0.0588 and 0.0534) in the short and long runs, respectively; INTR has a negative impact on ASIV in both the short and long runs. It has p-values of 0.4590 and 0.0473, indicating no statistical significance in the short run but a substantial effect in the long run; EXCHR has a negative, insignificant impact on ASIV in both the short and long term. Its p-values (0.3071 and 0.3046) are also more than the 5% level of significance; INFLR has a detrimental impact on ASIV both short and long term. It has p-values of 0.1258 and 0.1374, indicating that there is no statistical significance in the short and long runs, and that RGDPGR has a positive impact on ASIV in both the short and long runs. It exhibits p-values of 0.5314 and 0.5293, indicating no statistical significance in the short and long term. As a result, it was determined that macroeconomic dynamics have no significant impact on SMKT return volatility in Nigeria. This means that politicians and regulators must ensure that the money supply and exchange rates remains steady overall. They should also endeavour to curb inflationary trends and keep interest rates constant in the economy so that the SMKT can perform better and achieve the desired economic growth and national development.
 Keywords: Macro, Economic, Dynamics, Stock, Market, Return and Volatility.

The War between Russia and Ukraine and its Effects on the Indian Stock Market

The world economies are still not over the impact of COVID 19 pandemic when Russia started war on Ukraine on February 24, 2022. The global economies sided for and against Russia, while few countries took a neutral stand like India. This study analyses the impact on the war on Indian stock market. We analyse BSE sectoral indices which includes Metals, Oil and Gas, Raw materials, Auto, Energy, FMCG, Industrial, Infrastructure Manufacturing for a short window period from the start to the partial end of the war which includes all sanctions imposed on Russia by the global economies. Using technical stock market indicators like MA, SMA and EMACD, this study finds that Indian stock market was not affected by Russia Ukraine war. The sectoral indices depicted an uptrend and the volatility index showed a downtrend. To further confirm our findings, the study analyses the abnormal returns (AR) and Cumulative Abnormal Returns (CAAR) of the sectoral indices through Event Study Analysis for 21 days around the date of declaration of war. The fin dings show that there was no value destruction in any of the indices during the observed period which confirm with the previous findings. The findings of this study are interesting and has implications from a practical standpoint considering that all the sectors in Indian stock market are doing well irrespective of the rising oil prices and materials shortage because of the war

Crude oil volatility index forecasting: New evidence based on positive and negative jumps from Chinese stock market

This article investigates the crude oil volatility index (OVX) forecasting from the perspective of cross-market asymmetric effects of Chinese stock market jumps. We calculate six kinds of positive and negative jumps based on the high-frequency data of stock returns which are used to represent the asymmetric shocks of stock markets. Principal component analysis (PCA) and momentum of predictability (MoP) strategy are employed separately to synthesize the information of asymmetric jumps. Our empirical results find that considering the positive and negative jumps in Chinese stock market helps to improve the forecasting ability of OVX, especially under the MoP strategy. The out-of-sample model confidence set (MCS) tests and Diebold-Mariano (DM) tests, the evaluation of economic significance and the robustness tests further verify our results.

Diverse linkages between green bonds and equity indices of developed and developing economies

This paper investigates the dynamic conditional correlations and volatility spillovers between global stock markets and international green bonds. Envisaging the linkages between the emerging green bond markets and global equities given the unforeseen global pandemic. We use R software to run bivariate VAR-BEKK GARCH and BEKK GARCH models to investigate the time-varying conditional volatility between global stock indices and green bond indices. Daily prices of all variables from October 2014 to April 2023 are sourced from the Bloomberg database. The short-term influence of past events, as well as the long-term persistence of green bonds, on the current conditional volatility of global equity indices of developed and emerging economies is observed. Whereas from equity markets to green bond indices, the short-term as well as long-term impact is confined to only 2-3 indices. The results provide future direction for policymakers, researchers, and global investors in hedging and creating an optimal portfolio.

Volatility and returns of ESG indices: evidence from Japan

Volatility and returns of ESG indices: evidence from Japan

Assessing the Risk of Bitcoin Futures Market: New Evidence

AbstractThe main objective of this paper is to forecast the realized volatility (RV) of Bitcoin futures (BTCF) market. To serve our purpose, we propose an augmented heterogenous autoregressive (HAR) model to consider the information on time-varying jumps observed in BTCF returns. Specifically, we estimate the jump-induced volatility using the GARCH-jump process and then consider this information in the HAR model. Both the in-sample and out-of-sample analyses show that jumps offer added information which is not provided by the existing HAR models. In addition, a novel finding is that the jump-induced volatility offers incremental information relative to the Bitcoin implied volatility index. In sum, our results indicate that the HAR-RV process comprising the leverage effects and jump volatility would predict the RV more precisely compared to the standard HAR-type models. These findings have important implications to cryptocurrency investors.

Asymmetric and high-order risk transmission across VIX and Chinese futures markets

Due to the increasing attention to the linkages of financial markets in the context of ongoing economic globalization and financial integration, we delve into the transmission of risk across the Chicago Board Options Exchange Volatility Index (VIX) and volatility estimators of Chinese futures markets, where the VIX serves as an essential indicator of global financial market instability. By conducting tests on structural breaks in different order moments and risk contagions in different order co-moments via the regime-switching skew-normal model, we discover the existence of asymmetric volatility contagions through the correlation channel during the mid-2015 Chinese stock market crash and through the co-skewness channel during the first quarter of 2016. Interestingly, the risk contagion among the VIX and returns of the four futures primarily occurs via the co-skewness channel, rather than the commonly studied correlation channel. The joint structural break tests further reveal significant regime shifts in both first- and third-order moments for the VIX and negative realized semivariances of the four futures.

Analysis of Crude Oil Market Volatility and Macroeconomic Conditions: Empirical Evidence from Nigeria

This study aims to investigate the relationship between the volatility of the crude oil market and the macroeconomic conditions in Nigeria. The author used the methods of the auto-regressive distributed lag (ARDL) model in conjunction with the generalized autoregressive conditional heteroscedasticity (GARCH) to determine the extent of volatility using a monthly dataset from January 2012 to December 2022. The author regressed the crude oil price volatility index on Organization of the Petroleum Exporting Countries (OPEC) production quotas, conflicts, GDP growth rate, exchange rate and inflation. The results indicate that oil price volatility relates negatively to GDP, implying that the volatility of crude oil prices dampens growth in Nigeria. The paper concludes that rising oil prices heighten inflation, depreciate the exchange rate and depress growth in Nigeria. To hedge against oil price volatility, the paper recommends that the Nigerian government adopt policy measures that would increase energy efficiency and reduce the country’s dependency on oil exports through diversification in other related productive sectors such as agriculture and manufacturing.

Global Financial Cycle, Household Credit, and Macroprudential Policies

We show that macroprudential policies dampen the impact of global financial conditions on local bank credit cycles. For identification, we exploit variation in the U.S. volatility index (VIX) and household and business credit registers in an emerging market economy in which banks depend on foreign funding and macroprudential measures vary over the full cycle. Our results suggest that when the VIX is low, tighter macroprudential policies reduce household lending, notably for riskier (foreign currency (FX) and high debt-service-to-income) loans and by banks dependent on foreign funding. Moreover, they increase (less regulated) local currency lending to real estate firms. Such periods are associated with less subsequent total lending to households and firms and with a lower share of FX loans at the local level. Consistently, when the VIX is low, tighter macroprudential policies dampen house prices and economic activity. This paper was accepted by Victoria Ivashina, finance. Funding: M. Epure acknowledges support from the Serra Húnter program. This project received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program [Grant 648398], from the Agencia Estatal de Investigacion [Grants PID2020-115660GB-I00 and PGC2018-102133-B-I00], and the Severo Ochoa Program for Centers of Excellence in Research and Development [Grant CEX2019-000915-S]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.4981 .