Volatility Structure Research Articles

Non-Gaussian Stochastic Volatility models

Stochastic volatility models are fundamental tools in finance for accurately estimating and managing risks due to their ability to accommodate a time-varying volatility structure. However, a relevant constraint within these models is the reliance on Gaussian processes to model the latent (log-)variance, which can limit their ability to effectively capture events such as sudden jumps or spikes in the latent volatility. To address this limitation, we propose in this short paper a non-Gaussian SV model utilizing an inference procedure that combines Laplace and Variational Bayes approximations. Our study showcases the advantages of this correction in modeling the conditional variance of Bitcoin's return series.

Exploring the principal factors driving the volatility structure of stock trade levels in New Zealand: a Covid-19 perspective

This paper explores factors that drive the volatility structure of New Zealand stock trade levels, with the aim of understanding unusual volatility of stock trade levels that may pose a risk to the critical operations of a trading venue. Weekly number of trades from 2014–2024 for the sampled stocks were examined within principal axis factoring (PAF) and GARCH models. The results showed that three factors accounted for around 60% of volatility in the number of stock trades over the sample period; they were driven primarily by electricity generation stocks, consumer discretionary stocks, and commercial property stocks respectively. During the sample period, two subperiods of market shock showed unusual levels of volatility in all principal factors, with the second subperiod, which coincided with the Covid-19 outbreak, being particularly notable. An implication of the study is that given the volatilities of stock trade levels are little studied in the literature, the findings of this study could be a useful complement to existing studies examining stock price volatility for the purposes of mitigating unusual stock trading volatility.

Importance of geopolitical risk in volatility structure: New evidence from biofuels, crude oil, and grains commodity markets

This paper aims to explore the complex linkages and evolving structure of price volatility in the global oil, biofuels, and grain commodity markets during periods of global turbulence. With the growing urgency for energy stability amid climate change, biofuels are gaining traction as a viable alternative energy source. However, their production can significantly impact essential commodities like grains and vegetable oils, increasing food prices and heightened market volatility. We introduced a TVP-VAR frequency connectedness method to address this, analyzing data from January 1, 2013, to September 29, 2023. Our approach offers a fresh perspective on market dynamics and geopolitical risks.The study underscores the growing influence of agricultural shocks on energy markets, particularly within the ethanol sector. It confirms that the Russia-Ukraine war, a significant geopolitical event, has had a profound and enduring impact on the interconnectedness of these markets across various timeframes and frequencies. We offer concrete, actionable policy recommendations to mitigate the transmission of market shocks within the energy and food sectors, thereby bolstering investor and policymaker confidence and facilitating informed decision-making.

WITHDRAWN: the Impact of Monetary Policy on the Liquidity of Bond Market——Based on Chinese Local Government Bond Market

The bond market serves dual roles in fiscal and financial spheres, playing a crucial role in coordinating monetary policy. This paper investigates the impact of quantitative and price-based monetary policies on the liquidity level of China’s bond market. A comprehensive index measuring the liquidity of the local bond market is constructed using a combination weighting method that integrates the entropy method and the coefficient of variation. Employing the time-varying stochastic volatility structure vector autoregression (TVP-SV-SVAR) model on data spanning from 2013 to 2021, the study empirically compares the impulse response of local bond market liquidity to monetary policy shocks. Findings reveal that both types of monetary policy operations exhibit asymmetric, nonlinear, and time-varying impacts on bond market liquidity. Quantitative monetary instruments induce deeper impulse responses, with longer-lasting effects. These conclusions offer insights for monetary policy reforms and bond market development in China.

ESG performance and corporate volatility: an empirical exploration in an emerging economy

PurposeThis study aims to investigate into the dynamic connection between ESG scores and the volatility term structure for Indian companies listed BSE. The study divides the BSE-100 listed companies into two panels based on their median ESG scores in 2022, creating high and low ESG scoring groups to capture volatility structure.Design/methodology/approachThe study employs time-varying symmetric and asymmetric GARCH models and followed by continuous Wavelet to capture volatility structure and explore comparative resilience behavior.FindingsThe study found similar volatility patterns regardless of ESG scores, nudging doubt on the direct impact of ESG on volatility. Additionally, both high- and low-ESG-scored companies displayed high vulnerabilities during the pandemic, raising questions about the effectiveness of ESG frameworks in capturing risks. Finally, by examining the resilience behavior of ESG-scored companies during the pandemic, our study contributes to the evolving understanding of the intersection between ESG performance and crisis response.Practical implicationsThe study carries vital implications for investors and policymakers. It highlights the urgent need to strengthen the ESG framework and scores to shield investors from short- and long-term volatilities and economic vulnerabilities.Originality/valueTo the best of the authors’ knowledge, this is the first study investigating the Indian market by examining the volatility structure and resilience behavior of high- and low-ESG-scored companies during the pandemic.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2024-0113

Day-of-the-Week and Month-of-the-Year Effects in the Cryptocurrency Market

This study examines the day-of-the-week (DoW) and month-of-the-year (MoY) effects in the cryptocurrency market, with a focus on Bitcoin (BTC) and Ethereum (ETH). Due to the absence of a specific closing time in the cryptocurrency market, the closing time of the daily data is taken as 23:59 UTC. Initially, an appropriate volatility model for the cryptocurrency market is established using the GARCH, EGARCH, and TGARCH models. The most appropriate model for BTC is ARMA(1,0)-EGARCH(1,1) and ARMA(1,0)-GARCH(1,1) for ETH. The results of the analysis indicate a leverage effect in the cryptocurrency market, where negative shocks cause a more significant increase in volatility than positive shocks. Based on this volatility structure, the DoW and MoY are analyzed. For BTC, returns on other days are lower compared to Mondays. However, for ETH, returns on Thursdays are lower than those on Mondays. In terms of volatility, both BTC and ETH show that the highest volatility occurs on Mondays. For the MoY effect, neither BTC nor ETH don’t exhibit a significant effect in the mean equation. Nevertheless, the variance equation indicates that January has higher volatility compared to other months, indicating the presence of a MoY effect in terms of volatility.

Implied roughness in the term structure of oil market volatility

This paper analyses the attributes and the significance of the roughness of oil market volatility. We employ unspanned stochastic volatility models driven by rough Brownian motions that yield semi-analytical prices for future options entailing efficient calibration applications. By performing a Monte Carlo simulation study, we show that the semi-analytical pricing performs well thus establishing its efficiency for calibration applications. Thus we calibrate option prices written on oil futures and provide empirical evidence of the roughness in oil volatility. Introducing just one additional parameter, the Hurst parameter, indicating the roughness of the volatility improves the calibration by almost a factor of 10. The calibrated option-implied Hurst parameter varies over time, but the entire set of parameters becomes more stable than in the non-rough case corresponding to a fixed Hurst parameter 1/2. These results underscore the importance to model the time dependency of the roughness of oil market volatility.

Dynamic patterns and the latent community structure of sectoral volatility and jump risk contagion

This study explores the latent driving structure of sectoral risk contagion in terms of volatility and jump through the factor model framework for high-dimensional matrix-valued time series. We find strong inter-sector volatility and jump spillover effects at the mean level and higher effects at extreme tails, while jump spillovers reflect sharper structural shifts with the promotion of major financial or public events. Both volatility and jump are contagious in the form of a community-structure. Our findings provide new evidence about sectoral risk contagion and the importance of accurately identifying the underlying structure of risk contagion for efficiently realizing hierarchical regulation.

Comparative Analysis of the Volatility Structures of the Stock Prices of Energy Companies Traded on the Kazakhstan Stock Exchange and International Gold and Oil Prices

The return of its stock exchange and the companies traded within are one of the important indicators for a national economy. Due to the global structure of stock markets, returns are closely related to both national and international market variables. This study makes a comparative analysis of the volatility structures of the energy companies traded in the Kazakhstan Stock Exchange (KASE) and the combined stock market index and gold and oil prices in international markets for the period between January 01, 2021, and June 31, 2023. The research focused on two issues. The first is the analysis of the volatility structure of the six series examined. For this purpose, four different models were examined. The second focus is to determine whether the returns in international indices have a causal effect on the Kazakhstan stock market (composite stock market index) and the returns of oil and energy companies traded in the stock market. The results revealed that other indices and returns have a similar variable variance structure, except for the KASE. The relevant coefficient estimation was found to be significant in both conditional standard deviation models for the KASE index. The coefficient estimate of the GARCH-M(1,1) model in the OIL index was significant, whereas conditional standard deviation models and the relevant coefficients of both conditional standard deviation models were found to be statistically insignificant in the other returns. This is an indication of the structural compatibility of Kazakhstan's stock market composite index and energy and oil companies with international markets. Furthermore, the causality analysis results showing that international indices have a causal effect on KASE and KZAP is another indicator that the Kazakhstan market works in harmony with the international markets.

The factor structure of exchange rates volatility: global and intermittent factors

In this paper, we consider a fractionally integrated multi-level dynamic factor model (FI-ML-DFM) to represent commonalities in the hourly evolution of realized volatilities of several international exchange rates. The FI-ML-DFM assumes common global factors active during the 24 h of the day, accompanied by intermittent factors, which are active at mutually exclusive times. We propose determining the number of global factors using a distance among the intermittent loadings. We show that although the bulk of common dynamics of exchange rates realized volatilities can be attributed to global factors, there are non-negligible effects of intermittent factors. The effect of the COVID-19 on the realized volatility comovements is stronger on the first global-in-time factor, which shows a permanent increase in the level. The effects on the second global factor and on the intermittent factors active when the EU, UK and US markets are operating are transitory lasting for approximately a year after the pandemic starts. Finally, there seems to be no effect of the pandemic neither on the third global factor nor on the intermittent factor active when the markets in Asia are operating.

Povezanost volatilnosti među globalnim dionicama e-trgovine

This paper studies the volatility connectedness between the stock prices of e-commerce companies. For that matter, we implement the TVP-VAR-Based connectedness procedure to unearth the dynamic structure of volatilities. This approach helps us to determine the volatility spillover between assets that are risk receivers or risk transmitters. We utilize the daily log-return data of the largest e-commerce companies by market cap. The data set consists of the daily open, close, high, and low prices of stocks between 2019-01-02 and 2022-12-23. We obtain the volatility of stocks using the Garman-Klass range-based approach. The findings reveal that the average total connected index is relatively high by 65.45%, which means that the forecasting error variance in the variables is due to the transmission and connectedness between these variables. Furthermore, net pairwise directional connectedness results evidence that the most dominant stock is Amazon within the network. Ultimately, we find the strongest bilateral volatility interconnectedness to occur between the stocks of Alibaba and Jingdong Mall

(UN) Stable Cryptocurrency Markets: Insights From Volatility Modeling

The cryptocurrency market has attracted considerable attention from investors and researchers alike. This paper examines the volatility patterns of two major cryptocurrencies utilizing GARCH modeling: Bitcoin, based on a proof-of-work mechanism, and Cardano, operating on a proof-of-stake mechanism. Our findings reveal differences in the volatility structures of the two cryptocurrencies, with Cardano demonstrating a reduced long-term volatility compared to Bitcoin. This study suggests that transitioning from proof-of-work to proof-of-stake mechanisms might lead to a decrease in market volatility.

Transition E-Garch Model for Modeling and Forecasting Volatility of Equity Stock Returns in Nigeria Stock Exchange

The mean-equations that exhibit unconditional variance do not reflect real-world data characteristics and do not always fully embrace the thick tail properties of high-frequency financial time series. However, there are cases when mean-equations exhibit conditional and unconditional variances, which conventional Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models do not accommodate with assumptions of normal error innovation. The behaviour of equity in stock returns volatility was investigated using mean-equations that reflect both conditional and unconditional variances under the variants innovation distribution. The Nigeria Stock Exchange’s, (NSE’s), daily All Stock Index, (ASI) data from 3rd May 1999 to 18th March 2019 was utilized to characterize the volatility structure of equity in Nigeria Stock returns. The Transition Exponential GARCH Model (TEGM) with non-normal innovations was used to demonstrate the dynamic model performance and predictive power in modeling variances that transit between unconditional and conditional structures. Under various error innovations, the structural characteristics of the TEGM were examined, and the parameters were estimated. The TEGM result under Student’s t innovation distribution was significant for forecasting and predicting random series data. The error distribution has both the minimal and sufficient requirements for the best predictive capacity and forecasting capabilities. Over a longer period, the NSE’s daily ASI showed more unconditional variance than conditional variation. The Transition Exponential GARCH Model was found to be the best model for describing the volatility structure of financial assets in Nigerian stock returns, and is thus recommended for making informed investment decisions with better forecast performance.

마르코프 국면전환모형을 이용한 국제곡물 가격 예측

The purpose of this study is to analyze the volatility structure and fluctuation factors of international grain prices and forecast future price. The price dynamics are divided into normal and crisis periods according to the price fluctuations of the grain market and analyzed by applying the Markov Regime Switch Model, which can be estimated and predicted. The analyzed data comprises international grain prices and the corresponding determinants that explains them. The analysis period spans from January 2006 to June 2021. The empirical findings are summarized as follows: First of all, as a result of estimating the phase shift model considering the Markov process, it is found that it is more appropriate to estimate price volatility in the grain market by dividing the model into two phases - normal and crisis - than OLS which assumes a single phase. This implies that the presence of various phases holds significance in accordance with the price fluctuations observed in the grain market. Furthermore, it is cautiously expected that the phase-change model may prove to be an effective tool for predicting international grain prices in the future, showing superior predictability compared to other models in the evaluation of predictive performance using RMSE.

The Artist as a Subscription: Patching music as an artistic device

This article aims to explore the concept of patched/versioned musical works as creative ecologies. It identifies how the internet’s involvement in music creation and dissemination influences choices related to the release of such works. Throughout this writing, the author looks at the increasingly volatile structures surrounding recorded music in the early twenty-first century as a result of streaming platforms such as Spotify and video-based social media sites such as TikTok becoming the primary means for music consumption. It explores this volatility as a method for approaching the release of new music within dynamic musical ecosystems and looks at the growing art scene focusing on this way of working, drawing parallels between artistry and subscription-based services where content continually evolves over time.

The Dynamic Informativeness of Scheduled News

We propose a method to identify the informativeness of a future scheduled announcement at the daily level, exploiting the discontinuity it creates in the term structure of option volatility. We implement the strategy in a panel data model to estimate the relation between prior signals and the future announcement. This method allows us to separate substitutes from complements, it can isolate multiple signals within the same quarter, and it can condition on the timing and signal characteristics. We find that analyst forecasts substitute earnings announcement information and that recommendations do not provide extra information on top of forecasts. Moreover, our evidence suggests that insiders sell to avoid uncertainty when the announcement is far away but pull forward earnings information when they trade one month before. This paper was accepted by Eric So, accounting. Funding: This publication is part of the project “Eliciting the properties of private signals” (with project number VI.Veni.201E.029 of the research program VENI SGW, which is (partly) financed by the Dutch Research Council (NWO). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4970 .

Bond futures: Delivery Option with Term Structure Modelling

Bond futures are characterized by a set of underlying bonds; the short party has the option to deliver at expiry any of those underlying bonds. Bond futures thus embed a choice option between bonds with different maturities and coupons. The delivery mechanism also incorporates conversion factors that create an implicit strike. The option is impacted by different maturities and different moneyness for each bond. It is important to take into account the full term structure of volatility with smile. A recent paper Bang and Daboussi (2022) developed such an approach for swap ratebased products like CMS. In this paper, we extend their approach to cover futures and apply it to the specific case of bond futures. We show the impact of smile, term structure of volatility and correlations between rates on the delivery option and convexity adjustment values.

Shanghai crude oil futures: Returns Independence, volatility asymmetry, and hedging potential

Amidst the rise of Shanghai crude oil futures (SCOF) as a preeminent contender in the global oil arena, this study analyzes its returns and volatility structures in compelling contrast to West Texas Intermediate (WTI) and Brent oil futures (BRENT). A comprehensive examination is conducted using various GARCH models and News impact curves, with the analysis based on daily data spanning from April 2021 to March 2023. The results reveal distinct responses of SCOF when contrasted with WTI and Brent. Firstly, the study finds that SCOF returns exhibit a level of independence from global market movements. Secondly, the assessment of potential asymmetries in the volatility structures displays notable differences among the three markets. Specifically, WTI demonstrates the highest asymmetry, while SCOF exhibits lower asymmetry. These findings imply that SCOF returns exhibit stability and resilience and hold the potential to serve as a formidable hedge against adverse shocks. As investors and policymakers navigate the complex terrain of the global oil market, these insights underscore the strategic advantages and opportunities that SCOF may offer, both in individual investment decisions and broader risk management strategies.

Demystifying the Impact of COVID-19 on Structural Volatility of Indian Sectoral Market: A Quantile Regression Analysis

The present study explores the impact of COVID-19 on the volatility structure of the sectoral market in India. ARMA(p,q)- GJR-GARCH(1, 1)-std model is used to determine the daily conditional volatility for 13 selected sectors over the period starting from January 2020 to December 2021. The quantile regression model is employed to examine the changes in the structure of volatility in each sector over the pandemic duration. The results of the study show that the volatility of Metal, Oil–Gas and PSU are more sensitive to market volatility, whereas the volume of new COVID-19 cases exceeds the threshold limit, and no extreme spillover is observed from the market volatility. In addition to this, Bankex, Metal, Oil–Gas, Private Banks and Power sector volatility are more responsive to news sentiments during the period of increase in new COVID-19 cases. Furthermore, the results also reveal that news sentiments help to control the significant fluctuation in the sectoral market.

Forecasting VIX using two-component realized EGARCH model

In this paper, we propose the two-component realized EGARCH (REGARCH-2C) model, which accommodates the high-frequency information and the long memory volatility through the realized measure of volatility and the component volatility structure, to forecast VIX. We obtain the risk-neutral dynamics of the REGARCH-2C model and derive the corresponding model-implied VIX formula. The parameter estimates of the REGARCH-2C model are obtained via the joint maximum likelihood estimation using observations on the returns, realized measure and VIX. Our empirical results demonstrate that the proposed REGARCH-2C model provides more accurate VIX forecasts compared to a variety of competing models, including the GARCH, GJR-GARCH, nonlinear GARCH, Heston–Nandi GARCH, EGARCH, REGARCH and two two-component GARCH models. This result is found to be robust to alternative realized measure. Our empirical evidence highlights the importance of incorporating the realized measure as well as the component volatility structure for VIX forecasting.