Volatility Index Research Articles

Modelling Asymmetric Volatility in the Crypto Currency and its Dynamic Relationship with Stock Market

The paper investigates the asymmetric volatility effect of five major cryptocur- rencies and their bilateral linkages with major indices in the Indian stock market. To inves- tigate the bilateral relationship of crypto currency with stock indices, researchers used two major stock indices in the Indian stock market namely, BSE Sensex and NSE Nifty. The study used GARCH, EGARCH and TGARCH models, asymmetric to model the asymmetric volatility effect in the conditional volatility of crypto currencies and stock indices. Johansen’s cointegration and Vector Error Correction Model is used for examining the presence of cointe- gration between selected variables and to analyse the strength of causality among them. The study finds the evidence of cointegration between cryptocurrencies and stock market indices, implying that cryptocurrencies are related to stock indices. Further there is unidirectional relationship among stock and crypto market and crypto currencies having short-lived re- sponse to shocks in stock markets. Even with these currencies’ explosive growth, there are still not many research examining their connection to stock markets. This study will help investor’s those who making investment in currency market or in the stock market to eval- uate the pattern of volatility, interconnection among them, so that they can make crucial investment decisions and diversification strategies. This will help them to gain knowledge about how these two markets move together so they may avoid underestimating risk when building portfolios that contain both kinds of assets.

Exploring the Relationship between Economic Indicators, Investor Distrust, and Stock Market Volatility: case study of the KASE index dynamics

Object: The aim of the article is to investigate the relationship between investor distrust and the overall financial stability of the Kazakhstan Stock Exchange (KASE) through a comprehensive analysis of qualitative and quantitative methods. Methods: Qualitative analysis involves examining the activities of KASE and investor relations practices, while quantitative analysis employs regression modeling to explore the impact of macroeconomic factors on KASE index volatility. Findings: In the research varying levels of investor engagement and investor relations activities in Kazakhstan were revealed with significant correlations between macroeconomic indicators such as inflation, unemployment, GDP, and KASE index volatility. Results indicate that while inflation and GDP have limited impact, unemployment significantly influences market volatility, leading to the fact that investors less actively participate in the market activities in terms of poor economic conditions. Conclusions: Insufficient investor confidence, influenced by macroeconomic conditions and financial literacy, hinders the growth of the stock market. Building trust among investors requires transparent regulations, effective governance, and investor education initiatives to foster market participation and development.

Market Reactions to U.S. Financial Indices: A Comparison of the GFC versus the COVID-19 Pandemic Crisis

This study delves into the impacts of the 2008 global financial crisis (GFC) and the COVID-19 health crisis on U.S. financial indices, exploring the intricate relationship between economic shocks and these indices during downturns. Using Markov switching regression models and control variables, including GDP, consumer sentiment, industrial production, and the ratio of inventories-to-sale, it quantifies the effects of these crises on the CBOE Volatility Index (VIX), Standard & Poor’s 500 (S&P 500), and the Dow Jones Industrial Average (DJIA) from Q1 2000 to Q2 2023, covering crucial moments of both crises and stable periods (dichotomous variables). Results reveal that the 2008 crisis significantly heightened financial volatility and depreciated the valuation of S&P 500 and DJIA indicators, while the COVID-19 crisis had a diverse impact on market dynamics, particularly negatively affecting specific sectors. This study underscores the importance of consumer confidence and inventory management in mitigating financial volatility and emphasises the need for robust policy measures to address economic shocks, enhance financial stability, and alleviate future crises, especially during endogenous crises such as financial downturns. This research sheds light on the nuanced impact of crises on financial markets and the broader economy, revealing the intricate dynamics shaping market behaviour during turbulent times.

Interconnections and contagion among cryptocurrencies, DeFi, NFT and traditional financial assets: Some new evidence from tail risk driven network

For digital assets (cryptocurrencies, DeFi tokens and NFTs) and traditional financial assets such as stock indices and commodities, this paper established a dynamic downside risk network to explore the network aggregating structure, important nodes and the tail risk spillover between asset groups. This study reveals that there exist tail risk spillover between digital financial assets and traditional financial assets, where the risk spillover between the digital assets and the commodities is bidirectional and the risk spillovers from the stocks to the digital assets as well as the commodities to stocks are unidirectional. For important assets, the NFT Decentraland and Brazil’s BVSP stock index play central roles in the network, while the FTSE stock index and gold exhibit strong hedging potential against global downside risk spillovers. Furthermore, we consider the impact of uncertainty in US economic policy and US stock volatility on tail risk contagion, which is the network aggregation level. Regarded as exogenous shocks, the US stock circuit breaker in March 2020 and the US Federal Reserve rate rising in March 2022 were found to significantly increase the tail risk contagion, especially the risk spillover from the stocks market to the digital financial market. Moreover, by using the TVP-SV-VAR model, we find that increases in the US EPU index and the US VIX index can both exacerbate the tail-risk contagion between digital and traditional financial assets. This expands the influencing factors of risk contagion between financial assets. The empirical findings carry important implications not only for portfolio managers and investors in portfolio allocation and investment strategies between digital and traditional financial assets, but also for government regulators in risk management and economic policy formulation.

The Impact of Foreign Stock Market Indices on Predictions Volatility of the WIG20 Index Rates of Return Using Neural Networks

AbstractThe paper investigates the issue of volatility of stock index returns on the Warsaw Stock Exchange (WIG20 index returns volatility). The purpose of this review is to compare how other stock market indexes as HANG SENG, NIKKEI 225, FTSE 250, DAX, S&P 500 and NASDAQ 100 influance the volatility of WIG20 index returns. The innovation of this work is the usage of a new neural network with three different activation functions to predict future volatility of WIG20 index returns. The input for this network is the last 3 values of WIG20 index returns volatility and the last 3 values of one of the considered foreign index returns volatility. As measurements for the best forecasting performance of neural networks are taken common used forecast errors: ME (mean error), MPE (mean percentage error), MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root mean square error). The study shows that the Polish stock market is mainly influenced by the European and US markets.

Volatility and Return Connectedness Between the Oil Market and Eurozone Sectors During the Financial Crisis: A TVP-VAR Frequency Connectedness Approach

This paper analyzes the returns and volatility connectedness between oil prices and Eurozone sector returns during the global financial crisis. We employ the TVP-VAR frequency connectedness approach with daily data of Brent prices and 18 Eurozone supersector indices from 15 November 2014 to 24 November 2023. Our results show a high average connectedness of the returns and volatilities. Industrial Goods are the largest transmitter contrariwise Media supersector is the largest receiver of shocks on returns. The same finding is for volatility, the result shows that Industrial Goods and Services transmit the highest risk in contrast, the Media has the highest receiver volatility indices. The time-varying connectedness (TCI) of returns and volatilities in both show a drastic increase in March 2020. This increase is a result of COVID-19. Whereas, there has been no rise in connectivity following Russia’s invasion of Ukraine. Our result highlighted that Brent was a net receiver of volatility shocks during the Russian invasion of Ukraine.

Investor sentiment and sustainable investment: evidence from North African stock markets

This paper examines the connectedness between investor sentiment and returns and volatility on environmental, social, and governance (ESG) indices in Morocco and Egypt. Therefore, we construct a new investor sentiment index and use weekly data from January 2018 to December 2023, along with the time, frequency and quantile connectedness methods. The results show that investor sentiment sometimes influences the returns and volatility of the ESG indices, and sometimes it is influenced by them. This connectedness is stronger during distress events, namely, the COVID-19 outbreak and geopolitical tensions (the Russian-Ukrainian and Israeli-Palestinian conflicts). Furthermore, the spillover effect between sentiment and returns on the ESG indices is mainly due to short-term spillovers, except during the COVID-19 period, when long-term spillovers dominate. However, the spillover effect between sentiment and volatility on the ESG indices is mainly due to long-term spillover, especially during the COVID-19 outbreak and the Russia-Ukraine War, implying the persistence of shock transmission due to high uncertainty. The findings also highlight the impact of market conditions on spillovers. These findings can help socially responsible investors successfully diversify their portfolios and adjust their strategy according to investor sentiment; they also have beneficial implications for policymakers in achieving sustainable development goals.

Forecasting VIX using Bayesian deep learning

AbstractRecently, deep learning techniques are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. In this paper, we leverage probabilistic deep learning for inferring the volatility index VIX. We employ the probabilistic counterpart of WaveNet, Temporal Convolutional Network (TCN), and Transformers. We show that TCN outperforms all models with an RMSE around 0.189. In addition, it has been well known that modern neural networks provide inaccurate uncertainty estimates. For solving this problem, we use the standard deviation scaling to calibrate the networks. Furthermore, we found out that MNF with Gaussian prior outperforms Reparameterization Trick and Flipout models in terms of precision and uncertainty predictions. Finally, we claim that MNF with Cauchy and LogUniform prior distributions yield well-calibrated TCN, and Transformer and WaveNet networks being the former that best infer the VIX values for one and five-step-ahead forecasting, and the probabilistic Transformer model yields an adequate forecasting for the COVID-19 pandemic period.

The Study of Bond Spreads and Credit Ratings in Honduras

This research rigorously examines the intricate influences on Honduras' cost of borrowing from 2013 to 2022. The study employs the Corruption Perceptions Index (CPI) and Standard and Poor's as main determinants. Control variables include macroeconomic fundamentals that pertain to a country’s performance such as GDP growth, inflation and international reserves, fiscal variables such as debt-to-GDP ratio, and the central administration’s balance, and global factors such as global risk aversion measured by the Chicago Board Options Exchange's CBOE Volatility Index (VIX) and U.S rates. The study finds that country-specific fundamentals, global factors, corruption perceptions, sovereign credit ratings, and political risk all play significant roles in influencing Honduras’ cost of borrowing. It also exposes the limitations of credit ratings in capturing the truth about bond pricing in Honduras. These findings highlight the complex interplay of economic, global, and sociopolitical factors in shaping Honduras' bond spreads, emphasizing the need for a comprehensive approach to credit risk assessment and bond market analysis. The results from this research contribute to economic stability strategies for policymakers, enhance understanding for economists, and assists investors and financial analysts in making informed decisions based on identified variables impacting bond spreads.

Empirical analysis of crude oil dynamics using affine vs. non-affine jump-diffusion models

This paper investigates the dynamics of the United States oil (USO) exchange traded fund (ETF). Daily USO returns are modelled using stochastic volatility (SV) frameworks derived from three different model classes: SV models with contemporaneous jumps in returns and volatility (SVCJ); SV model with jumps in returns only (SVJ); and a pure SV model class without jumps. Six affine and non-affine models are considered within each model class that depend on specification of the drift and the diffusion terms in the variance process, resulting in a total of 18 models that are estimated using particle Markov Chain Monte Carlo (PMCMC) approach. Model evaluation is conducted using the Deviance Information Criterion (DIC), Bayes factors, probability plots, and deviation measures to assess the discrepancy between the estimated volatility and key benchmarks, the crude oil ETF volatility index (OVX) and the realised volatility (RV). Our analysis indicates that models incorporating jumps, particularly the SVCJ-PLY-0.5 and SVCJ-PLY-1.0, more accurately capture USO dynamics than standard SV models. The SVCJ-PLY-0.5 model ranks highest based on DIC statistics and Bayes factors, and both models excel in aligning their estimated volatility with the OVX and RV benchmarks. Overall, the statistical criteria employed in our comparison favour models with jumps over the standard SV model class, suggesting that models incorporating jumps in both return and variance processes (SVCJ) are superior to those with jumps solely in the return process (SVJ). The affine models SVJ-LIN-0.5 and SVCJ-LIN-0.5 with linear variance drift and square root diffusion that are particularly interesting for theoretical finance applications are highly ranked among considered frameworks, outperforming several non-affine alternatives. Our analysis of the regression model for volatility forecasting reveals a significant predictive accuracy in the evaluated models, demonstrating their effectiveness in anticipating future volatility trends.

Factors Influencing the Performance of Indonesia's Sukuk Index During the Covid-19 Pandemic

The performance of Sharia-compliant bonds or sukuk can be affected by both internal and external factors, such as coupon rates and macroeconomic variables. This study aims to examine the impact and contribution of variables, namely the volatility index (VIX index), global sukuk index, BI rate, Consumer Price Index, and exchange rates, on the return of the Indonesian sukuk index from January 2018 to February 2020 and from March 2020 to December 2022, before and during the Covid-19 pandemic. As a research method, the Vector Error Correction Model (VECM) is applied. Prior to the Covid-19 pandemic, the long-term return of the Indonesian sukuk index was driven by the VIX index, the BI rate, and exchange rates, whilst the global sukuk index had no influence. During the Covid-19 epidemic, the return of the Indonesian sukuk index was influenced by the VIX Index, the global sukuk index, inflation, and exchange rates, but was unaffected by the BI rate variable. During the Covid-19 epidemic, the return of Indonesian sukuk index was primarily driven by the global sukuk index.

The COVID-19 Outbreak and Volatility Spillover Between Stock Exchange’s Overall and Small- and Medium-enterprise Indices: Evidence from India

The research examines the dynamic correlation between the volatility of the Bombay Stock Exchange (BSE) and the Small- and Medium-enterprises (SMEs) Index of BSE in India. It focuses on analysing the volatility spillover between SME and BSE by utilising the generalised autoregressive conditional heteroskedasticity (GARCH) model. The study extends its scope by examining the impact of the current pandemic, COVID-19, on the volatility of both SME and BSE by utilising the exponential GARCH and threshold GARCH models. Findings suggest that there is volatility spillover between the two indices. The results also highlight that the pandemic significantly affected the volatility of both indices. The findings of the study provide important implications for policymakers, portfolio managers, and investors, particularly when the Indian government has been emphasising on the growth and continuous support of the small and medium enterprises and increasing foreign investments in India’s SMEs in recent years.

Comparative analysis of responses of risky and safe haven assets to stock market risk before and after the yield curve inversions in the U.S.

This study examines the response of safe-haven assets (gold, US dollar) and Bitcoin to market risk before and after yield curve inversions in the U.S. market. Using the VIX volatility index and static and dynamic cross-quantilogram approach, the analysis reveals that gold and the U.S. dollar act as safe havens, showing positive responses to increased VIX values, while Bitcoin behaves as a risky asset, negatively responding to higher VIX values during market turbulence. Changes in the VIX index have an immediate impact on asset price returns, but the effect diminishes over time, suggesting the need for timely updates to investment strategies. Yield curve inversions have altered the VIX-US dollar relationship: pre-inversion changes were influential in calmer markets, but post-inversion, they played a bigger role during turbulent phases, suggesting potential changes in investor behavior and market dynamics. The findings offer practical insights for investors seeking stability and protection during uncertain market conditions.

The non-linear impact of monetary policy on shifts in economic policy uncertainty: evidence from the United States of America

A stochastic volatility estimation of VIX index’s latent volatility is used for the United States of America, as a proxy for the adjustments in the levels of investors’ uncertainty related to current and future economic policies. The impact of monetary policy stance on such measure is examined in the framework of the distributed lag non-linear models (DLNM). We place this analysis in the literature stream emphasizing the various sources of heterogeneity concerning investors’ expectations. The main finding is that the monetary policy does impact non-linearly the adjustments in investors’ predictions. While a tighter monetary policy does generally contribute to an increase in VIX’s latent volatility, the shape of such effect varies across different GLM and GAM specifications of DLNM. This outcome remains robust, even if: (1) we control for the global price of Brent crude and consumers’ confidence; (2) we use, instead of the stochastic framework, a Markov-switching GARCH-based estimator; or (3) we replace the monetary policy instrument with monetary policy uncertainty. We argue that accounting for its nonlinear effects on financial markets is of critical importance for the design of a monetary policy pursuing global financial stability.

Dynamics effect of volatility index, interest rates, and commodity prices on Indonesian bond yields

Several factors influence the movements and dynamics of bond yields in financial markets. The determination of monetary policy, specifically the decisions regarding interest rates made by central banks, is a critical factor. Moreover, bond yields can be influenced by various factors such as geopolitical events, financial volatility, market sentiment, and investor risk appetite. These factors can impact the demand and supply dynamics in bond markets. This research aims to analyze the influence of Interest Rates, IDR to USD Exchange Rates, Volatility Index (VIX), Gold and Oil Prices on Bond Yields in Indonesia. The data used in this research is secondary data, which consists of time series data from 2019-2023. This research investigates the impact of financial and commodity prices on bond yields in Indonesia by using the autoregressive distributed lag (ARDL) model to examine both the long-run correlation and short-run effect. Empirical results found that Interest Rate, Volatility Index (VIX), and Oil Prices have a significant positive influence. Meanwhile, the Gold Price variable has a significant negative influence. This research has several crucial policy implications for investors concerning the national monetary policy, exchange rate fluctuation, and global volatility index to create profitable and sustainable portfolio strategies. Moreover, investment managers and investors should be concerned about the global commodities prices that will affect bond yield performances. This research contributes to the recent literature presenting causal relations of global volatility index (VIX) on Indonesian bond yield.

Discover the Impact by COVID-19 Pandemic on Biopharmaceutical Index and Its Related Industry Portfolio

This research is trying to explore the changes in biopharmaceutical and other medically related indices under the impact of the pandemic, and how pharmacy related portfolio impact by the pandemic. The research utilizes EGARCH and VAR models to analyze the volatility of the biopharmaceutical index and the dynamic relationships among various related indices. The research mainly demonstrates the changes in risk of the biopharmaceutical index before and during the pandemic, providing a certain degree of alert to investors who are interested in investing in biopharmaceutical-related stocks but are relatively risk-averse. It also explores the impact of the biopharmaceutical index on other pharmaceutical-related indices, verifying its influence on other indices and whether it has predictive capabilities. Finally, by comparing the portfolios constructed using the biopharmaceutical index and its related industry indices before and during the pandemic, it provides recommendations for investors interested in investing in pharmaceutical stocks during large-scale health events.

VIX-managed portfolios

We propose a simple portfolio management strategy that gauges the leverage based on the observed implied volatility index (VIX). The strategy involves taking less risk when the cumulative previous-month VIX is high and more when it is low. We show that the strategy yields more stable weights and thus requires less rebalancing than comparable strategies based on realized volatility. As a result, it produces substantially higher spanning regression alphas when transaction costs are taken into account. We document this for ten equity factors, six classes of mean–variance efficient portfolios and 176 anomaly portfolios. We argue that the superior performance of the VIX-based strategy is driven by its ability to time volatility and tail risk simultaneously, resulting from the forward-looking nature of the information entailed in the index and the higher-order return moments embedded in the implied volatility smile.

Do agricultural swaps co-move with equity markets? Evidence from the COVID-19 crisis

Using proprietary data reported by swap dealers to the Commodity Futures Trading Commission, we first present new evidence on the size and composition of 13 over-the-counter agricultural swaps markets. We then utilize our novel dataset to show the existence of linkages with the equity markets. We use the spike in the Chicago Board Options Exchange Volatility Index in early 2020 to show that swaps trader positions were significantly impacted by the financial market volatility created by the COVID-19 pandemic. Following similar methods as Cheng et al. (2015), we find index swaps traders reduce their net long positions in response to tightening financial conditions, while commercial swaps traders absorb some of this risk by decreasing their net short positions. This internal swap market netting occurs in three of the four largest agricultural markets: corn, soft red winter wheat, and sugar. Concurrently, we observe a limited swap dealer hedging response in the futures market, especially when compared to other financial traders, consistent with swap market netting. Our results confirm that equity market shocks can affect financial traders in both commodity swaps and futures markets.

A Study on Determinants of Non-Performing Assets in Indian Banks

The purpose of this paper is to empirically examine the effect of macroeconomic determinants on non-performing assets (NPAs) in the India’s commercial banks using a panel regression model. This study considers all public, private, and foreign sector scheduled commercial banks during a period of seventeen years from 2004-05 to 2021-22, and uses four macroeconomic variablesGDP growth rate, inflation rate, interest rate and global volatility index- to assess the impact of these variables on NPAs. The findings show that NPAs have a negative relationship with GDP growth rate, confirming a increase in economic growth would rise the incomes of people and businesses and improve the ability of repayment of loans to the banks. Remaining three macroeconomic determinants negatively associated with NPAs but they were statistically insignificant. The result of the empirical study suggests that efficient management policies at the macro level are required to reduce the level of NPAs and to maintain the stability of the performance of banks in India.

ML Model for Stock Classification

Abstract: This study examines how effectively volatility indices can forecast stock market movements by applying different machine learning techniques. It uses a dataset from Kaggle for the TCS Company, including variables such as 'Date,' 'Symbol,' 'Series,' 'Prev Close,' 'Open,' 'High,' 'Low,' 'Last,' 'Close,' 'VWAP,' 'Volume,' 'Turnover,' 'Deliverable Volume,' and '%Deliverable.' Various models were tested and evaluated based on metrics like the AUC-ROC Curve, Accuracy, Precision, Recall, F1-Score, Cross-Validation Accuracy, and the Confusion Matrix. The findings reveal that the Linear Regression (Classification model) surpasses other models in forecasting stock market directions with the highest levels of accuracy across all evaluation metrics. This highlights the potential of machine learning methods in leveraging volatility indices to accurately predict stock market trends