Volatility Index Research Articles

ESG‐Firm Performance Nexus: Evidence From an Emerging Economy

ABSTRACTFirms worldwide are strengthening environmental, social and governance (ESG) factors, indicating a bidirectional association between financial and non‐financial performance. Understanding the ambiguity around the predictors of the firm's ESG performance, we attempt to reappraise the ESG‐ firm performance (FP) linkage using the firms indexed in the NIFTY 100 ESG index spanning 2014 to 2023. Our findings highlight that market forces significantly influence firms' ESG performance; however, in emerging economies, the direct relationship between ESG and FP appears to be insignificant. Surprisingly, corporate governance positively moderates the ESG–FP linkage, strengthening this association. Among the four proxies for market forces, the Volatility Index (VIX) shows a significant impact, where higher market volatility is associated with improved ESG performance, and lower volatility corresponds to weaker ESG performance. Conversely, the World Uncertainty Index (WUI) exhibits a negative significance, suggesting that higher WUI adversely affects the ESG performance of firms in emerging economies, while lower WUI has a favourable effect. Green bonds have a positive and significant effect on overall ESG performance and on social and governance dimensions; however, their influence on environmental performance is not significant, raising concerns about potential greenwashing trends. Strikingly, our findings indicate that emerging economy firms do not consider systematic risk, suggesting that investors' risk perceptions remain unchanged regardless of whether firms over‐ or under‐invest in ESG initiatives. Our research calls for greater attention to policymakers by providing accurate reasonings for the ESG performance of emerging economy firms.

Skewness and option prices under stochastic volatility models: the role of shot-noise jumps

In this paper, we propose a novel option pricing model that incorporates both shot-noise effects in stock return jumps and stochastic volatility. The model aims to reflect the decaying effects on stock returns after the shock and attempts to provide a more reasonable structure for return jumps. We emphasize the theoretical nature of this paper and manage to derive closed-form expressions for the VIX index and the SKEW index under the new model. For the VIX index, we prove that it can be decomposed into a linear combination of the volatility part and the jump part. For the SKEW index, we obtain its analytical representation beyond traditional affine models and check its sensitivity with respect to model parameters. We also perform a calibration exercise using options data to compare our model with a classical stochastic volatility model with jumps. Several empirical observations are made.

A Comparative Analysis of Global Stock Index Volatility Risks Before and During the Covid-19 Pandemic

This research is motivated by the phenomenon of the Covid-19 pandemic. In recent years, the Covid-19 pandemic has caused a shock to the stock market. As a result, there is a significant difference in the stock index before or during the Covid-19 pandemic. Stock indices are important indicators to measure stock market fluctuations so that they can be taken into consideration for investor decision making. This study uses global stock indices, namely the United States stock index (S&P 500 and Nasdaq), Japan stock index (Nikkei 225), China stock index (SSE), Hong Kong stock index (Hang Seng), Germany stock index (Dax 30), and European stock index (Euronext). That is because the seven stock indices are the indices with the largest market capitalization in the world. This research is a quantitative research. This research uses time series data, which is data accumulated over time on an object in order to describe the development of the object. In this study, the type of data used is secondary data in the form of historical stock index data. Then the researchers conducted the Garch Test to determine the volatility risk of the entire stock index in the period before and during Covid-19. Abstrak Penelitian ini dilatarbelakangi oleh fenomena pandemi Covid-19. Dalam beberapa tahun terakhir, pandemi Covid-19 menyebabkan guncangan pada pasar saham. Imbasnya, terjadi perbedaan signifikan pada indeks saham sebelum atau saat pandemi Covid-19. Indeks saham adalah indikator penting untuk mengukur fluktuasi pasar saham sehingga dapat menjadi bahan pertimbangan guna pengambilan keputusan investor. Penelitian ini menggunakan indeks saham global yaitu indeks saham Amerika Serikat (S&P 500 dan Nasdaq), indeks saham Jepang (Nikkei 225), indeks saham China (SSE), indeks saham Hong Kong (Hang Seng), indeks saham Jerman (Dax 30), dan indeks saham Eropa (Euronext). Itu karena ketujuh indeks saham tersebut merupakan indeks dengan kapitalisasi pasar terbesar di dunia. Penelitian ini merupakan penelitian kuantitatif. Penelitian ini menggunakan data time series, yakni data yang diakumulasikan dari waktu ke waktu pada suatu objek guna mendeskripsikan perkembangan objek. Dalam penelitian ini jenis data yang digunakan merupakan data sekunder berupa data historis indeks saham. Kemudian peneliti melakukan Uji Garch guna mengetahui risiko volatilitas dari seluruh indeks.

The Impact of the VIX Volatility Index on the ISX Index of the Iraq Stock Exchange under Portfolio Investment for the Period 2016-2023

The research aimed to identify the volatility index (VIX) and its trend path during 2016-2023, as well as clarify the relationship between the (VIX) and ISX indices using the ARDL model, and test the stability of both indices. The research concluded that the ISX index for the Iraqi market is selected in a way that allows the index to reflect the state of the capital market. It is calculated through price changes, which does not reflect the reality of the market. The results of the ARDL model also showed the insignificance of the relationship in the short and long term, as the probability P (0.5280) appeared, which is higher than 0.05, which means that the VIX index did not have any effect on the ISX, whether the VIX rose or fell, and this indicates the isolation of the Iraqi Stock Exchange from international markets and its progress in modern trading tools and methods. Among the recommendations for the purpose of developing the ISX index, statistical methods and formulas should be adopted that reflect the actual market reality and simulate international markets and indices. Adopting advanced and modern methods in trading and integrating with international financial institutions and markets through gaining experience, field visits and keeping up with developments.

Market Fear and Emerging Market Returns: Analyzing the Impacts of VIX and Dollar Index

This paper examines how changes in two measures of market fear in the US — the VIX (volatility) index and the dollar index — affect equity returns in emerging markets from March 11, 2005, to May 5, 2023. We analyze daily data from two exchange-traded funds (ETFs) representing major emerging countries from different continents, which have not been studied before. We observe that during times of global uncertainty, investors often turn to the US dollar as a safe haven. This shift leads to capital leaving emerging markets, resulting in lower stock returns and increased market volatility. Our findings show that a higher VIX index significantly and asymmetrically reduces average returns in emerging markets, while a lower VIX index has a comparatively smaller positive impact. In contrast, the influence of changes in the US dollar index on emerging markets is consistent and symmetric, regardless of which ETF is considered. Moreover, our results indicate that considering both indices together provides a more accurate prediction of equity returns in emerging markets.

Cryptocurrency returns and cryptocurrency uncertainty: a time–frequency analysis

This study investigates how the uncertainty surrounding cryptocurrency affects cryptocurrency returns (CR) by employing various wavelet techniques. We concentrate on the recently published cryptocurrency uncertainty index (UCRY) and the top eight cryptocurrencies by market capitalization from December 30, 2013, to June 30, 2023. Our results showed that the UCRY index strongly predicted CR. In particular, the UCRY index has a leading position at all frequencies for all cryptocurrencies in our sample. Additionally, when the impacts of economic policy uncertainty and the volatility index are eliminated, the significant comovement of UCRY-CR remains unchanged for the short-, medium-, and long-term investment horizons. Therefore, we conclude that the UCRY-CR relationship is both persistent and pervasive. Our study contributes toward the literature on the relationships between cryptocurrencies and market uncertainties, as well as toward investors who use uncertainty indices to design investment strategies for their portfolios.

Evidence of Financial Market Synergies. A Combined Statistical and Machine Learning Research Approach

The research investigates how geopolitical conflicts influence global commodities and financial markets, aiming to identify effects on oil companies' economic and financial indicators within the context of financial market synergies. An integrated approach is implemented in the study, combining traditional statistical methods with advanced machine learning techniques. The results of the investigation were analyzed at two distinct levels: (1) that of the evolution of global commodity prices during the conflicts in Ukraine and the Gaza Strip, and (2) that of the financial performance of 250 companies in the oil industry, summarized in seven key indicators. Eloquent findings indicate that both conflicts have triggered significant changes in commodity prices, with strong investor reactions to safe haven assets such as gold and silver. In addition, there are clear manifestations of behavior reflecting supply-side concerns, as energy prices have risen considerably. Correlation analysis has shown a significant link between upward oil price movements and changes in some financial indicators (such as EBITDA) as well as asset increases. By integrating traditional statistical methods and advanced machine learning techniques, the ability of GARCH-ML hybrid models to accurately predict stock index volatility in the context of geopolitical tensions is highlighted. The research results highlight the benefits of the holistic approach in analyzing the complex interconnections between commodity markets, financial markets and company performance.

Cryptocurrency returns and volatility spillover during an era of uncertainty: COVID-19 and VIX

This study investigates the volatility of cryptocurrencies during the uncertainty created by COVID-19 and the Chicago Board Options Exchange Volatility Index (VIX). Hence, the relationship between cryptocurrencies in the pre- and post-COVID-19 era was introduced into a mean equation that applied the VAR (1)-BEKK GARCH-M (1,1) method to the eight largest market volume cryptocurrencies. Descriptive statistics show the Dogecoin has the most volatility, BNB has the highest return, and BTC has the lowest volatility. Due to the lag in its return during the pre-COVID-19 time frame, BTC harms other coins? returns. Other coins? returns, including their own, are increased by the Dogecoin?s one-period lagged return. Notably, only BNB, XLM, and XRP were statistically significantly impacted by VIX spikes. This study highlights the fact that cryptocurrencies experience first-instance (average) volatility. Bitcoin?s long-term uncertainties statistically significantly and favorably enhance other cryptocurrencies? long-term uncertainties.

Forecasting the volatility of stock price index using binary-real coded genetic algorithm: application of econophysics approach

Forecasting the volatility of stock price index using binary-real coded genetic algorithm: application of econophysics approach

Causal Behavior of Dynamic Dividend Yield of Property Stock in Information Asymmetric Market: Evidence from South African Listed Property Stock Market

The paper investigates the dynamic behavior of the dividend yield of South African (SA) property stocks under an information asymmetric market. The vector error correction model (VECM) is used to analyze daily data on dividend yield and asymmetric market indicators including bid-ask spread, turnover, volatility index, weighted average price, and market size for the period of 2007-2017. We find that dividend payouts from the SA property stock market declined during the study period and reacted positively and sharply to shocks in the market spread. The VECM shows a statistically significant causal effect between the dynamic behavior of property stock and market spread. The market spread predicts the dynamic behavior of the dividend yield of SA property stocks in both the short and long run.

The Impact of Sentiment on Realized Higher-Order Moments in the S&P 500: Evidence from the Fear and Greed Index

This study empirically investigates the relationship between realized higher-order moments and the Fear and Greed Index as a measure of sentiments. We estimate daily realized moments using 5 min return data of the S&P 500 index from 3 January 2011 to 18 September 2020. We find that the Fear and Greed Index significantly impacts realized volatility during periods of extreme fear. Additionally, various sentiment indicators influence realized skewness and realized kurtosis. The VIX index significantly reduces realized skewness across all sentiment levels. Bearish and bullish sentiments have a significant negative relationship with negative realized skewness during periods of extreme fear and extreme greed. However, the Fear and Greed Index and bearish and bullish sentiments have a significant positive relationship with positive realized skewness. During extreme fear, the Fear and Greed Index and bearish and bullish sentiments have a significant negative relationship with realized kurtosis. These results remain consistent when considering the non-linear characteristics of the Fear and Greed Index during periods of extreme fear and extreme greed. These findings highlight the relevance of understanding sentiment in financial risk management and its significant relationship with the asymmetric and extremity characteristics of asset returns.

Assessing the impact of interest rate fluctuations on sector index volatility in Bourse Istanbul: A GARCH approach

This study analyses the impact of interest rate changes and past volatility on sector index returns in Borsa Istanbul using a GARCH(1,1) model. The results show that interest rate changes negatively affect sector returns, including banking, food, holdings, tourism, services, transportation, financial, industrial, and technology. Furthermore, the GARCH(1,1) model indicates persistence in volatility in sectors like banking, holdings, transportation, financial, industrial, and technology, where past volatility strongly influences future volatility. Conversely, sectors such as food, tourism, and services exhibit less volatility persistence, suggesting more stable returns during interest rate fluctuations. The GARCH (1,1) specification outperforms the ARCH model by capturing the persistence of variance, making it a more reliable measure for sectoral volatility. The results align with previous research, mainly on the sensitivity of financial sectors to interest rate changes and market volatility. This study's unique contribution lies in its focus on BIST 100 sectors, offering valuable insights for investors to optimise asset allocation. By understanding sector-specific sensitivities to interest rate changes and volatility, investors can make informed decisions to enhance returns.

Ekonomik politika belirsizlikleri ile pay senedi piyasası oynaklığı arasında asimetrik ilişkiler: Borsa İstanbul sektörlerine ilişkin uygulama

The study aims to determine the asymmetric relationships between Borsa Istanbul sector index volatilities and economic policy uncertainties of different regions and countries. For this purpose, the relationships between the European Region, Germany, France, England, Italy and Global Economic Policy Uncertainty Indices and Borsa Istanbul 30, Services, Financial, Industrial and Technology sector indices volatilities were analyzed using the Hatemi-J Asymmetric Causality Test. The most important finding obtained from the study is that there is a causal relationship between the increases experienced in all economic policy uncertainty indices and the increases experienced in sector index volatilities. However, no causal relationship was determined between the decreases experienced in economic policy uncertainty indices and the decreases experienced in sector index volatilities. Another finding from the study is that there are asymmetric relationships between all economic policy uncertainty indices and sector index volatilities, while the industrial sector index volatility has an asymmetric relationship with all economic policy uncertainty indices.

Forecasting the Volatility of US Oil and Gas Firms With Machine Learning

ABSTRACTForecasting the realized volatility of oil and gas firms is of interest to investors and practitioners trading on the energy spot and derivative markets. In this paper, we assess whether several machine learning (ML) techniques can offer superior forecasts compared to HAR models for predicting realized volatility at the firm level. Moreover, we investigate whether economically motivated variables and technical indicators contain valuable information for forecasting firm volatility beyond those contained in various volatility factors previously identified in the literature. Our results demonstrate that certain ML techniques provide superior forecasting accuracy compared to the benchmark model. Additionally, we identify variables such as the 1‐month treasury bill and the aggregate VIX index as significant drivers of realized firm volatility in the oil and gas industry.

Capital Flows to Emerging Markets: The Role of Information Transparency

Using annual capital flow data from 1990 to 2010 for 21 emerging markets, this study empirically investigates the impact of macroeconomic information transparency on capital flows, taking into account financial market uncertainty. The level of in- formation transparency is measured using the country's compliance with the IMF's SDDS. The findings reveal that SDDS compliance mitigates the negative influence of the VIX index on both net and total gross inflows. However, the positive effect of SDDS compliance on capital inflows is observed only when the VIX index reaches a sufficiently high level. This relationship is primarily driven by other in- vestment inflows, as it is the only type of capital flow significantly affected by the SDDS compliance in conjunction with VIX. The results suggest that although enhanced information transparency in emerging markets may not attract more foreign capital during normal periods, it provides stability by reducing the decline in other investment inflows during times of financial turmoil, ultimately contributing to the overall stability of total capital inflows.

Analyzing The Effect of Monetary Policies and Financial Stability Measures Across Global Markets amid Covid-19 Pandemics

This study investigates causal relationships among financial stability measures and monetary policies implemented in US, EU and Turkey. Selected variables from stock markets, money markets, and bond markets are tested. The set of macrofinancial process covers S&P500 stock market, BIST-100 stock market; Volatility index; German, US and Turkey 10-year treasury bond interest data, European Union 27 average CPI, US CPI, Turkey CPI, ECB policy rate, FED policy rate, CBRT policy rate data. The Granger causality analysis is applied for first; each dataset of US, EU and the Turkish economy, separately. Second, mutual impact of stock markets, money markets and bond markets are examined. Finally CBOE Volatility index (VIX) is taken into account. It is aimed to observe spillover effects from developed economies to emerging markets amid pandemics. The dataset covers the monthly period of 2019M1 to 2023M6.

Macroeconomic Determinants of BIST100: A Study Using Fourier and Asymmetric Causality Methods

This study examines the relationship between the Borsa Istanbul (BIST) 100 index, a key indicator in the Turkish financial markets, and selected macroeconomic variables, including gold prices, inflation, money supply and the VIX fear index. Fourier-based asymmetric causality tests and cointegration analysis are used to assess the long-run and short-run effects of these variables on the BIST 100 index. The results show a strong negative relationship between gold prices and the BIST 100 index, while inflation has a positive impact on the stock market. Although the money supply does not have a direct effect on the markets, it can promote optimism when considered alongside inflation and other economic activity. The VIX index has no significant effect on the BIST 100. These findings underscore the importance of understanding the role of macroeconomic variables in stock market dynamics, providing both investors and policymakers with critical insights for making strategic decisions.

Cross-market volatility dynamics in crypto and traditional financial instruments: quantifying the spillover effect

PurposeThis paper examines the volatility spillover effects from traditional financial assets to cryptocurrency markets and vice versa. It aims to provide insights into the dynamic interconnectedness of these markets.Design/methodology/approachThis paper employs the time-varying parameter vector autoregression technique to examine the volatility spillover among the crypto markets (across leading cryptocurrencies such as Bitcoin (BTC), USD Tether, NEAR Protocol (NEAR), Immutable and Dogecoin) and traditional financial instruments (Treasury Bills (TBILL) and Volatility Index).FindingsThe results reveal significant bidirectional volatility spillovers between cryptocurrencies and traditional financial assets. NEAR and BTC act as a major transmitter of volatility, both influencing others significantly (71.63 and 68.17%, respectively) and being influenced by others (54.74 and 62.3%, respectively). TBILL and Grayscale Bitcoin Trust ETF are the largest net receivers of volatility, indicating a higher dependency on other assets’ volatility.Practical implicationsUnderstanding the volatility spillover dynamics can aid investors in portfolio diversification and risk management. The findings provide actionable insights for constructing portfolios that include both cryptocurrencies and traditional financial assets, allowing for more informed investment decisions under volatile market conditions.Originality/valueThis paper contributes to the literature by analyzing volatility spillovers among traditional financial markets and various major cryptocurrencies. It offers a framework for assessing how shocks in one market or cryptocurrency can propagate to others, thereby enhancing the understanding of interconnectedness between markets. This understanding improves our ability to risk manage modern portfolios, which increasingly include significant alternative assets like cryptocurrencies.

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.

Exploring the connectedness between major volatility indexes and worldwide sustainable investments

Exploring the connectedness between major volatility indexes and worldwide sustainable investments