Spread Volatility Research Articles

Impact of crude oil price innovations on global stock market volatility: Evidence across time and space

This study investigates the impact of crude oil price innovations on global stock market volatility through a ripple-spreading network model, incorporating four dimensions of crude oil price changes: realized volatility, implied volatility, variance risk premium, and realized skewness volatility. Additionally, we assess the effects of three types of crude oil shocks—oil-specific, aggregate demand, and oil supply shocks. The results indicate that while all four dimensions exhibit similar temporal diffusion patterns, their spatial impacts differ. Global stock markets demonstrate heightened sensitivity to implied volatility and variance risk premium, followed by realized volatility and, lastly, realized skewness volatility. Moreover, we find that realized volatility spreads through multiple transmission pathways, albeit at a slower pace compared to implied volatility and the variance risk premium. Among the crude oil shocks, oil-specific shock induces the most rapid volatility transmission across global markets, with aggregate demand shock following and oil supply shock exerting the smallest influence.

The determinants of Turkish CDS volatility: An ARDL approach covering COVID period

The accuracy of CDS premiums has been questioned in many former studies. However, we intend to show that the volatilities of these spreads rather than their basis point levels indicate and signal the status of sovereign risk and credit worthiness as they tend to reveal sudden deteriorations in key sovereign and global economic indicators. In that respect we aim to reveal the determinants of Turkish CDS spread volatility by using an ARDL Bounds Test framework. In line with our expectations exchange rate, stock market indice and oil price volatility have significant positive coefficients in the long run whereas US 10-year bond spreads have short run effects up to three lags. Also, our results show that COVID pandemic has remarkably increased Turkish CDS volatility. Moreover, the unorthodox monetary policies adopted after COVID has also raised CDS volatility with persistently high spread levels where a long-term memory effect was prevalent.

Economic policy uncertainty and volatility of corporate bond credit spread: Evidence from China and the United States

In the prevailing situation of economic instability, it is customary for governments to enact economic policies aimed at influencing the decision-making processes of corporations. Nevertheless, the uncertainty surrounding economic policies inevitably impacts the capital structure of firms, consequently giving rise to fluctuations in corporate bond credit spreads through various transmission channels. Therefore, this study examines the impact of economic policy uncertainty (EPU) on the volatility of corporate bond credit spread in China and the United States (U.S.) from January 2014 to July 2021. Developing a one-sided filtering single-factor and multi-factor GARCH-MIDAS model in which monthly EPU and daily corporate bond credit spread are used as variables with different frequencies, the total volatility of corporate bond credit spread is decomposed into long- and short-term volatility components. Utilising the EPU data, encompassing indices delineating EPU in the U.S. (Baker et al., 2016) and China (Huang and Luk, 2020), alongside the corporate bond credit spreads in both the Chinese and American markets, we find that the level and volatility of EPU positively impact the long-term volatility of corporate bond credit spread with different ratings. Moreover, the EPU level contributes to more than 12% to the total volatility of corporate bond credit spread in the Chinese samples and more than 4.31% in the U.S. samples. While the variance of EPU contributes to over 5.19% to the total volatility of corporate bond credit spread in the U.S. samples, which is more than that of the Chinese samples.

Volatility Connectedness of MOVE Index and Bond Returns

Research Originality: To the best of our knowledge, this is the first study which measures the impact of the MOVE index on bond yield volatility by comparing its effect on both short-term and long-term. The paper contributes to the existing literature by providing a better understanding of the relationship between maturity and volatility spillover. Research Objectives: This study examines the dynamic volatility connectedness between 2 and 10-year bond yields and the MOVE Index. Furthermore, it is aimed at detecting the transmitter of volatility among variables. Research Methods: Using a data set from 2010 to 2022, the study utilizes a Time-Varying Parameter Vector Autoregression (TVP-VAR) model to analyse the dynamics and relationship between bond yields and the MOVE Index. Empirical Results: The study finds a significant volatility spillover between the MOVE Index and Türkiye's bond yields. Notably, the linkage between the MOVE Index and the 10-year bond rates is stronger than with the 2-year rates. Additionally, the MOVE Index emerges as the primary transmitter of volatility, impacting both bond yields. Implications: This study sheds light on the complexity and dynamics of volatility spread in Türkiye's bond market, providing essential insights for forecasting bond yields and shaping financial policies.JEL Classification: G17, C22, G12How to Cite:Baykut, E., Gökgöz, H. (2024). Volatility Connectedness of MOVE Index and Bond Returns. Etikonomi, 23(1), 27 – 46. https://doi.org/10.15408/etk.v23i1.36131.

Quality issues of implied volatilities of index and stock options in the OptionMetrics IvyDB database

AbstractFor stock and index options in the United States, OptionMetrics records prices at 3:59 p.m., not 4:00 p.m. as assumed in previous literature. The resulting 1‐min time discrepancy with closing share prices creates artificial variability in implied volatility spreads and strongly affects market‐wide spreads. It leads to particularly large distortions at the onset of the COVID‐19 pandemic. For index options in Europe, OptionMetrics data show large deviations from put‐call parity even though the original option prices match the parity exactly. Finally, the implied volatilities of stock options in Europe show clusters of exceptional deviations due to incorrect dividend information.

Demand-and-supply imbalance risk and long-term swap spreads

We develop and test a model in which swap spreads are determined by end users' demand for and constrained intermediaries' supply of long-term interest rate swaps. Swap spreads reflect compensation both for using scarce intermediary capital and for bearing convergence risk—i.e., the risk spreads will widen due to a future demand-and-supply imbalance. We show that a proxy for the intermediated quantity of swaps—dealers' net position in Treasuries—flipped sign during the Global Financial Crisis when swap spreads turned negative and that this variable predicts the excess returns on swap spread trades. Exploiting our model's sign restrictions, we identify shifts in demand and supply and find that both contribute significantly to the volatility of swap spreads.

Early exercise, implied volatility spread and future stock return: Jumps bind them all

AbstractWe find that early exercise premiums of exchange‐traded single‐stock American puts, in excess of the GBM‐world premium, can negatively predict future stock returns. Simulations suggest that asset‐value jumps, especially the mean jump‐size, can positively drive this excess premium, while jump‐size can also negatively induce the implied volatility (IV) spread of equivalent American option‐pairs. Empirically, controlling for the effect of jump‐size in excess premiums, the premium loses its predictive power. Furthermore, controlling for the excess premium or jump‐size, IV spreads' predictability shown in the literature also diminishes. Our evidence survives under alternative explanations like informed trading, stock mispricing or market frictions.

Relationship between mateverse cryptocurruncies (meta coins) and sustainability indices

Purpose- The purpose of this study is to determine the relationship between metaverse and sustainability indices. MANA token was used as a metaverse indicator in the study. Dow Jones Sustainability Emerging Markets Index (DJS_EMI), Dow Jones Sustainability World Index (DJS_WI), Dow Jones Sustainability Europe Index (DJS_EI) were used as sustainability indices. Methodology- In this research, the dynamic relationships between Metaverse and 3 sustainable indices used in the study were analyzed with a time-varying parameter vector autoregressive (TVP-VAR) model. This method is based on the fixed parameter sliding window VAR approach, first developed by Antonakakis and Gabauer (2017) and later updated by Diebold and Yılmaz (2009, 2012, 2014). Findings- DSJ_EMI variable carries a volatility spread of 24.85% to “DSJ_WI”, 24.81% to “DSJ_EI” and 25.40% to “METAVERSE”. “DSJ_WI” has a 59.68% volatility spread to other variables. The variables “DSJ_EMI” 25.04%, “DSJ_EI” 25.10% and “METAVERSE” 24.69% carry out volatility spread. Finally, while 70.79% of the volatility change in the "METAVERSE" variable is due to itself, a total of 29.21% is due to other variables. The variables “DSJ_EMI” 9.74%, “DSJ_WI” 9.74% and “DSJ_EI” 9.73% perform volatility spread. Conclusion- Mana (Decentraland Token) is the cryptocurrency used in the virtual reality platform called Decentraland. Metaverse refers to a digital universe called virtual reality, augmented reality and virtual world. Decentraland is a decentralized virtual reality platform that allows users to create, trade and interact with their own digital content.As a result of the analysis, it was determined that MANA, a Metavarse crypto asset, spreads volatility to sustainability indices. In other words, the MANA token spreads volatility to sustainability indices. Keywords: Metaverse, metaverse cryptocurrincies, meta coins, sustainability ındex, TVP-VAR JEL Codes: C32, C55, C58

New ESG rating drivers in the cross‐section of European stock returns

AbstractWe assess the performance of two quantitative signals based on ESG scores across a large, multi‐national cross‐section of European stock returns. We test whether the cost of equity capital is more influenced by the upward momentum (measured over time) of the ESG scores of the firms issuing stocks or by their stability (identified as the volatility of the scores over time), measured around a changing mean level. We find that short‐term ESG momentum over 1 month has a significant impact on the cross‐section of stock returns, lowering the anticipated cost of capital and leading to positive average abnormal returns. This suggests that short‐term ESG momentum may represent a novel, priced systematic risk factor. Furthermore, we find strong evidence that an ESG volatility spread strategy which buys low ESG score volatility stocks and sells high volatility ones, generates a substantial alpha and affects the ex‐ante cost of capital. Both quantitative ESG signals result in portfolio sorting and long‐short strategies that enhance the overall sustainability profile of the issuing firms without compromising the raw average of their ESG scores.

Investor attention and the predictability of the volatility of CNY‐CNH spreads: Evidence from a GARCH‐MIDAS model

AbstractCombining the four aspects of self‐, macro, environmental, and policy attention, using backward‐looking rolling regressions, we construct novel international and domestic investor‐attention indices using the search volume index from Google Trends together with Baidu Index to investigate how investor attention affects the CNY‐CNH spreads volatility. Moreover, comparing different GARCH‐MIDAS models and conventional GARCH‐type models is conducted concerning the out‐of‐sample volatility forecasting capability. Our results show that: (i) international and domestic investor attention has a positive impact; and (ii) the GARCH‐MIDAS models involving investor attention improve forecast accuracy. In particular, the model with domestic investor attention has an advantage in forecasting.

The Effects of Option Trading Behavior on Option Prices

This paper investigates the relationship between option trading behavior and option pricing patterns. We argue that greater active trading in the options market due to investor overconfidence leads to higher volatility and larger discrepancies in option pricing, which may be captured by implied volatility spread and implied volatility skewness. Using two different measures of excess option trading, we find that trading activities are correlated in different ways with volatility, volatility spread, and volatility skewness. We also find that these relationships exist both over time and cross-sectionally. We suggest that options investors tend to chase “hot” stocks, as we find evidence of a positive relationship between option trading activities and past underlying equity returns. Heavier trading in the options market also tends to make out-of-the-money call options more (less) expensive than the at-the-money counterparts over time (cross-sectionally). Because trading activities do not predict future equity returns, investor overconfidence, and not informed trading, seems to be a more plausible explanation for our findings.

Volatility spillovers between sovereign CDS and futures markets in various volatility states: Evidence from an emerging economy around the pandemic

We study quantile connectedness across the realized volatility of the 5-year Turkish CDS spreads and four futures contracts of USDTRY, EURTRY, XU030, and XAUTRY around the pandemic period. The procedure identifies, on average, the XU030 (EURTRY) stock index futures as the main net transmitter (receiver) of volatility shocks irrespective of subperiods. The level of total connectedness (i) fluctuates over time; (ii) is highly sensitive to major events; and (iii) strengthens in the high volatility state. The dynamic connectedness reaches a peak in December 2021, one day after the introduction of a new scheme, FX-protected deposit accounts, to address higher financial dollarization rates and lower the depreciation pressure on the lira. We find that investing in currency futures is very attractive, while XAUTRY futures have the highest reward-to-volatility. The hedging costs are highly related to changes in the infectious disease equity-market volatility tracker, geopolitical, and economic policy index for the US.

Information flow and credit rating announcements

We employ the implied volatility spread (IVS) and the short lending fee as measures of private information conveyed by their respective markets. Using issuer credit rating announcements as an informational event, we find that both IVS and the short fee have significantly higher predictive power for returns on event days versus non-event days. Both also predict the direction and magnitude of credit rating changes. Consistent with the linkage between the short sale and options markets, in models with both explanatory variables, the short fee remains significant in all specifications, while IVS loses explanatory power.

The Only Constant Is Change: Nonconstant Volatility and Implied Volatility Spreads

AbstractWe examine the predictability of stock returns using implied volatility spreads (VS) from individual (nonindex) options. VS can occur under simple no-arbitrage conditions for American options when volatility is time-varying, suggesting that the VS-return predictability could be an artifact of firms’ sensitivities to aggregate volatility. Examining this empirically, we find that the predictability changes systematically with aggregate volatility and is positively related to the firms’ sensitivities to volatility risk. The alpha generated by VS hedge portfolios can be explained by aggregate volatility risk factors. Our results cannot be explained by firm-specific informed trading, transaction costs, or liquidity.

Asymmetric volatility spillover from retail trading of noise traders: evidence from the gamestop short squeeze

Purpose-Recently, it has been observed that social trading and social media-based stocks are very popular especially for retail investors in the financial world. However, coordinated transactions of retail investors through social platforms carry the risk of classical noise trading, and the noise-based transactions of these investors, described as irrational investors, constitute a new source of systematic risk in the market. Considering the mentioned issues, this study aims to reveal the volatility and asymmetric volatility effects of social media-based noise trading of retail investors through the recent GameStop short squeeze. Methodology-The volatility and asymmetrical volatility spillovers from the social media-based retail transactions of the noise traders to the stock market were investigated using the EGARCH method, following Verma and Verma (2007), on the GameStop short position squeeze case. Findings-In the study, positive and statistically significant volatility spillover coefficient representing the spillover effect of the volatility in the GME index towards the NYSE Composite index and a negative and significant asymmetry coefficient revealed that the volatility spread from the GME index to the NYSE Composite index was asymmetrical. In this context, negative shocks in the GME index have a greater impact on the volatility of the NYSE Composite index than positive shocks. In addition, the findings of the study showed that there was no asymmetric volatility spillover from the NYSE Composite index to the GME index, and that past volatility in the GME index had a greater impact on present conditional volatility than the NYSE Composite index. Conclusion-This study determined that there was an asymmetrical volatility spillover from the GME index to the NYSE Composite index, and revealed that the social media-based retail transactions of noise traders may have not only volatility but also asymmetric volatility spillover effects in the markets. Keywords: GameStop, meme stocks, noise traders, retail trading, social media, asymmetric volatility JEL Codes: G10, G11, G14, G41

Predicting ETF liquidity

Substantial transaction costs are incurred in exchange-traded fund (ETF) trading each year. This article examines a vector autoregressive (VAR) model’s performance and other trading schedules to time trades in a large sample of 1350 ETFs over the 2011–2017 period. We reject the notion of a one-size-fits-all trading schedule that maximizes spread savings for all ETF traders. ETF traders who want to split their orders could save 7.40% of ETF spread costs, whereas trading at the market closing time would be optimal for ETF traders without motives to split trades. The spread savings for ETF traders are diverse across ETF sectors and depend on the spread volatility. JEL Classification: G11, G23

Borrowing into debt crises

Quantitative models of sovereign default predict that governments reduce borrowing during recessions to avoid debt crises. A prominent implication of this behavior is that the resulting interest rate spread volatility is counterfactually low. We propose that governments borrow into debt crises because of frictions in the adjustment of their expenditures. We develop a model of government good production, which uses public employment and intermediate consumption as inputs. The inputs have varying degrees of downward rigidity, which means that it is costly to reduce them. Facing an adverse income shock, the government borrows to smooth out the reduction in public employment, which results in increasing debt and higher spread. We quantify this rigidity using the OECD Government Accounts data and show that it explains about 70% of the missing bond spread volatility.

Internet Appendix to the Only Constant is Change: Non-Constant Volatility and Implied Volatility Spreads

Internet Appendix to the Only Constant is Change: Non-Constant Volatility and Implied Volatility Spreads

Information Spillovers Prior to M&A Announcements

In this paper, I study trading activities prior to M&A announcements pertaining to the rivals of the merging firms. I find that not only acquirers and targets experience increases in abnormal trading activities in stock and option markets, but also their rivals. The rise in option trading is especially strong for options that informed traders are most likely to trade. I find that the implied volatility spread (IV spread) constructed from a rival’s option prices the day before the announcement can predict this rival’s cumulative abnormal return (CAR) over the M&A announcement window. As the IV spread is widely adopted as a proxy for informed trading activities in the option market, my findings provide evidence for information spillovers from merging firms to their rivals prior to the announcements of the M&A deals.

Complex network analysis of volatility spillovers between global financial indicators and G20 stock markets

This paper analyses the dynamic transmission mechanism of volatility spillovers between key global financial indicators and G20 stock markets. To examine volatility spillover relations, we combine a bivariate GARCH-BEKK model with complex network theory. Specifically, we construct a volatility network of international financial markets utilising the spatial connectedness of spillovers (consisting of nodes and edges). The findings show that spillover relations between global variables and G20 markets vary significantly across five identified sub-periods. Notably, networks are much denser in crisis periods compared to non-crisis periods. In comparing two crisis periods, Global Financial Crisis (2008) and COVID-19 Crisis (2020) periods, the network statistics suggest that volatility spillovers in the latter period are more transitive and intense than the former. This suggests that financial volatility spreads more rapidly and directly through key financial indicators to the G20 stock markets. For example, oil and bonds are the largest volatility senders, while the markets of Saudi Arabia, Russia, South Africa, and Brazil are the main volatility receivers. In the former crisis, the source of financial volatility concentrates primarily in the USA, Australia, Canada, and Saudi Arabia, which are the largest volatility senders and receivers. China emerges as generally the least sensitive market to external volatility.