Volatility Surprises Research Articles

Commodity connectedness of the petrochemical industrial chain: A novel perspective of “good” and “bad” volatility surprises

This paper uses the TVP-VAR-DY approach to investigate the risk transmission among seven China's petrochemical commodities futures, i.e., crude oil and its six downstream products, from the perspective of “good” and “bad” unexpected volatility spillovers. The results show that there are great differences between the dynamics of “good” and “bad” volatility surprise transmission. Linear low-density polyethylene and polypropylene tended to behave as net risk transmitters, and asphalt, PTA, and PVC tended to reactively receive the information spillovers. China's petrochemical commodity futures system was vulnerable to the violent shocks of international oil prices provoked by extreme events.

A Critical Analysis of Volatility Surprise in Bitcoin Cryptocurrency and Other Financial Assets

The purpose of this paper is to investigate the viability as compared with other financial assets of cryptocurrencies as a currency or as an asset investment. This paper also aims to see which macro variable relates more to the price of cryptocurrencies, especially Bitcoin. Since the whole concept of cryptocurrencies is quite novel, an attempt has been made to briefly explain the underlying blockchain technology that forms the bedrock of cryptocurrencies. In this study, we use secondary data, i.e., the price history of Bitcoin from September 2014 to September 2021 for the last seven years, captured from trading exchanges. We predicted monthly returns of Bitcoin with that of Standard & Poor’s 500 Index (S&P 500), gold, and Treasury Bonds. Our findings show that Bitcoin has very high volatility compared to S&P 500, Gold and Treasury Bonds. Also, our findings show that there is a positive correlation between Bitcoin’s price volatility and the other three financial assets before and during COVID-19. Hence, Bitcoin is acting more as a speculative asset rather than a steady store of value. This can be drawn from the comparison with the debt market i.e., a Treasury Bond that invests in long-dated (30 years) US treasuries with which Bitcoin shows no relationship. The findings of this study could help with understanding the future of Bitcoin. This has important implications for Bitcoin investors. The current study contributes to the extant literature by providing empirical evidence on long-term social sustainability vis-à-vis supply chain traceability.

Using transfer entropy to measure information flows between cryptocurrencies

In this paper, we use the transfer entropy to quantify information flows between three cryptocurrencies, namely Bitcoin, Ethereum and Ripple. We also employ the concept of Rényi transfer entropy that allows for capturing rare and frequent events separately as well as non-linear market dependencies, focusing on extreme (tail) observations of the return distributions. We find that Bitcoin and Ripple share a bidirectional information transmission, while there is only one directional information transmission from Ripple to Ethereum. There is no nonlinear information transmission according to the Rényi’s measure, which implies the linear dependency among the three cryptocurrencies. This information transmission between cryptocurrencies occurs mostly in the pre-crash period while they become independent after the 2017 cryptocurrency crash. We finally use the concept of volatility surprise to examine linkages among the volatility of our series, and find a highly significant information transmission flow in one direction from Bitcoin to Ripple. Our results should be useful to investors in helping them in developing investment strategies by considering these three cryptocurrencies.

The Nature of Volatility Spillovers Across the International Capital Markets

This paper studies the nature of volatility spillovers across countries from the perspective of network theory and by relying on data of US-listed ETFs. I use a Lasso-related technique to estimate the International Volatility Network (IVN) where the nodes correspond to large-cap international stock markets while the links account for significant volatility lead-lags. Also included in the analysis is the International Trade Network (ITN), whose links measure bilateral export-import flows thus, capturing fundamental interconnections between countries. I find that the IVN and the ITN resemble each other closely pointing out that volatility does not disseminate randomly but tends to spread across fundamentally related economies. I also note that the amount of lagged volatility reactions in the IVN is consistent with the notion of gradual diffusion of information across investors who are subject to limited attention and home bias. I formally test this hypothesis by using as a direct proxy of investors’ attention the aggregate search frequency in Google. The empirical results support this intuition indicating that higher volatility surprises in key foreign markets predict higher domestic attention upon those markets in subsequent days. Once domestic attention is captured by such external shocks, it is contemporaneously transformed into higher domestic volatility.

The volatility surprise of leading cryptocurrencies: Transitory and permanent linkages

There is scarce literature examining the volatility linkages among leading cryptocurrencies, and none exists on the linkages among unexpected volatility, called ‘volatility surprise’. To address this literature gap, we build on the concept of volatility surprise and examine the causal linkages among the volatility of leading cryptocurrencies via the frequency-domain test of Bodart and Candelon (2009), discriminating between transitory and permanent causalities. Permanent shocks are more important in explaining the Granger-causality that does not necessarily emanate from the largest, Bitcoin, over short horizons, whereas transitory shocks dominate the causality across smaller cryptocurrencies over long horizons.

Modelling the mean and volatility dynamic relationship between the environmental risk or exchange rate volatility surprise and Commodity Research Bureau future price index

This paper focuses on volatility interactions between energy, agricultural, and environmental risk (proxy variable, DJSI) and exchange rate volatility surprise. Using five MGARCH models, our empirical results present significant own short-term and long-term persistence effects and also cross-market spillover short-term and long-term persistence effects among energy, agricultural, and environmental risk for the pre- and post-2008 financial crisis. This paper also finds that the mean and volatility spillover effects are more significant post the financial crisis than for the pre-crisis period, implying that the impact of the financial crisis is bigger post the crisis. The results provide strong evidence of spillover effects coming from the volatility surprise across markets.

Modelling the mean and volatility dynamic relationship between the environmental risk or exchange rate volatility surprise and Commodity Research Bureau future price index

Modelling the mean and volatility dynamic relationship between the environmental risk or exchange rate volatility surprise and Commodity Research Bureau future price index

Is Volatility Your Friend or Nemesis? The Links Between Transaction Costs, Market Conditions, and Trading Styles

This article summarizes results of our extensive empirical study motivated by the intuitively appealing statement that institutional clients’ average transaction costs are sensitive to market conditions. Using a comprehensive sample of client execution data covering two years of trading, we confirm that average cost of institutional trades varies considerably and systematically with volatility, volume, and trade imbalance surprises. For the overwhelming majority of buy-side institutions, implementation shortfall is higher than normal when volatility and volume exceed their historical average values. However, the deviations of trading volume in excess of the values typically observed in high volatility conditions dampen the effect of high volatility environment on execution costs of institutional orders. We document a strong dependence of transaction costs on contemporaneous trade imbalances, which is amplified by higher than normal contemporaneous volatility. We observe that cost curves are more sensitive to order size at the times of less favorable buy-sell trade imbalances, reflecting the role played by directional market pressure indicators. In summary, buy-side institutions should not neglect market conditions monitoring, as failure to adjust promptly to market conditions may result in deteriorated performance and missed cost savings opportunities.

The dynamics of return co-movements and volatility spillover effects in Greater China public property markets and international linkages

Purpose– The purpose of this paper is to examine weekly dynamic conditional correlations (DCC) and vector autoregressive (VAR)-based volatility spillover effects within the three Greater China (GC) public property markets, as well as across the GC property markets, three Asian emerging markets and two developed markets of the USA and Japan over the period from January 1999 through December 2013.Design/methodology/approach– First, the author employ the DCC methodology proposed by Engle (2002) to examine the time-varying nature in return co-movements among the public property markets. Second, the author appeal to the generalized VAR methodology, variance decomposition and the generalized spillover index of Diebold and Yilmaz (2012) to investigate the volatility spillover effects across the real estate markets. Finally, the spillover framework is able to combine with recent developments in time series econometrics to provide a comprehensive analysis of the dynamic volatility co-movements regionally and globally. The author also examine whether there are volatility spillover regimes, as well as explore the relationship between the volatility spillover cycles and the correlation spillover cycles.Findings– Results indicate moderate return co-movements and volatility spillover effects within and across the GC region. Cross-market volatility spillovers are bidirectional with the highest spillovers occur during the global financial crisis (GFC) period. Comparatively, the Chinese public property market's volatility is more exogenous and less influenced by other markets. The volatility spillover effects are subject to regime switching with two structural breaks detected for the five sub-groups of markets examined. There is evidence of significant dependence between the volatility spillover cycles across stock and public real estate, due to the presence of unobserved common shocks.Research limitations/implications– Because international investors incorporate into their portfolio allocation not only the long-term price relationship but also the short-term market volatility interaction and return correlation structure, the results of this study can shed more light on the extent to which investors can benefit from regional and international diversification in the long run and short-term within and across the GC securitized property sector, with Asian emerging market and global developed markets of Japan and USA. Although it is beyond the scope of this paper, it would be interesting to examine how the two co-movement measures (volatility spillovers and correlation spillovers) can be combined in optimal covariance forecasting in global investing that includes stock and public real estate markets.Originality/value– This is one of very few papers that comprehensively analyze the dynamic return correlations and conditional volatility spillover effects among the three GC public property markets, as well as with their selected emerging and developed partners over the last decade and during the GFC period, which is the main contribution of the study. The specific contribution is to characterize and measure cross-public real estate market volatility transmission in asset pricing through estimates of several conditional “volatility spillover” indices. In this case, a volatility spillover index is defined as share of total return variability in one public real estate market attributable to volatility surprises in another public real estate market.

Cross-market spillovers with ‘volatility surprise’

This article adopts the asymmetric DCC with one exogenous variable (ADCCX) model developed by Vargas (2008), by updating the concept of ‘volatility surprise’ to capture cross-market relationships. Current methods for measuring spillovers do not focus on volatility interactions, and neglect cross-effects between the conditional variances. This paper aims to fill this gap. The dataset includes four aggregate indices representing equities, bonds, foreign exchange rates and commodities from 1983 to 2013. The results provide strong evidence of spillover effects coming from the ‘volatility surprise’ component across markets. Against the background of the recent financial crisis, the aim is to contribute to the literature on the interdependencies of financial markets, both in conditional means and (co)variances. In addition, asset management implications are derived.

Cross-Market Spillovers with Volatility Surprisee

This article adopts the asymmetric DCC with one exogenous variable (ADCCX) model developed by Vargas (2008), by updating the concept of ‘volatility surprise’ to capture cross-market relationships. Current methods for measuring spillovers do not focus on volatility interactions, and neglect cross-effects between the conditional variances. This paper aims to fill this gap. The dataset includes four aggregate indices representing equities, bonds, foreign exchange rates and commodities from 1983 to 2013. The results provide strong evidence of spillover effects coming from the ‘volatility surprise’ component across markets. Against the background of the recent financial crisis, the aim is to contribute to the literature on the interdependencies of financial markets, both in conditional means and (co)variances. In addition, asset management implications are derived.

Cross-market volatility index with Factor-DCC

This paper proposes a new empirical methodology for computing a cross-market volatility index – coined CMIX – based on the Factor DCC-model, implemented on volatility surprises. This approach solves both problems of treating high-dimensional data and estimating time-varying conditional correlations. We provide an application to a multi-asset market data composed of equities, bonds, foreign exchange rates and commodities during 1983–2013. This new methodology may be attractive to asset managers, since it provides a simple way to hedge multi-asset portfolios with derivative contracts written on the CMIX.

Volatility returns with vengeance: Financial markets vs. commodities

To assess how financial markets and commodities are inter-related, this paper introduces a ‘volatility surprise’ component into the asymmetric DCC with one exogenous variable (ADCCX) framework. We develop an econometric model in which returns and volatility allow to influence pairs of assets, and derive several case studies linking commodities to stocks, bonds and currencies from 1983 to 2013. The innovative feature of our model is that these volatility spillovers are modeled consistently within the correlation dynamics of the ADCCX. We find evidence that return and volatility spillovers do exist between commodity and financial markets and that in turn, their relative impact on each other is very substantial.

The cross-market index for volatility surprise

This article proposes a new empirical methodology for computing a cross-market volatility index – coined CMIX – based on the Factor-Dynamic Conditional Correlation (DCC) model, implemented on volatility surprises. This approach solves problems in treating high-dimensional data and estimating time-varying conditional correlations. We provide an application to multi-asset market data composed of equities, bonds, foreign exchange rates and commodities during 1983–2013. This new methodology may be attractive to asset managers, because it provides a simple way to hedge multi-asset portfolios with derivatives contracts written on the CMIX.

Effect of Oil Price Volatility on Tunisian Stock Market at Sector-level and Effectiveness of Hedging Strategy

In this work, our objective is to study in a first step links and interaction between oil and stock markets in Tunisia in terms of volatility at the sector-level, and then in a second step to determine the best hedging strategy for oil-stock portfolio against the risk of negative variation in stock market prices. Our methodology consist to model the data by a bivariate GARCH model to capture the effect in terms of volatility in the variation of the oil price on the different sector index, and to use the conditional variances and conditional correlation to calculate the hedging ratio and determinate the best hedging strategy. The empirical results indicate that the majority of relationships are unidirectional from the oil market to Tunisian stock market, and the conditional variance of a stock sector returns is affected not only by the volatility surprises of the stock market, but also by those of oil market. The model GARCH-BEKK is more effective than the others versions to minimize the risk of oil-stock portfolio.

Volatility equicorrelation: A cross-market perspective

This paper contains the first empirical application of the Dynamic Equicorrelation (DECO) model to a cross-market dataset composed of equities, bonds, foreign exchange rates and commodities during 1983–2013. The originality of our approach consists of examining the volatility equicorrelations, by updating the concept of ‘volatility surprise’. We document that the average volatility equicorrelation across markets is around 15%, while being time-varying with regime shifts before/after September 2005 and with a low mean-reversion level.

Individual and collective stock dynamics: intra-day seasonalities

We establish several new stylized facts concerning the intra-day seasonalities of stock dynamics. Beyond the well-known U-shaped pattern of the volatility, we find that the average correlation between stocks increases throughout the day, leading to a smaller relative dispersion between stocks. Somewhat paradoxically, the kurtosis (a measure of volatility surprises) reaches a minimum at the open of the market, when the volatility is at its peak. We confirm that the dispersion kurtosis is a markedly decreasing function of the index return. This means that during large market swings, the idiosyncratic component of the stock dynamics becomes sub-dominant. In a nutshell, the early hours of trading are dominated by idiosyncratic or sector-specific effects with little surprises, whereas the influence of the market factor increases throughout the day, and surprises become more frequent.

Volatility Pricing in the Stock and Treasury Markets

Unexpected shifts in stock market volatility, often associated with financial crises, carry a significantly negative risk premium across the stock and Treasury markets, which suggests the existence of a unified pricing model for these markets. Results show that investors require a premium for holding the risky assets (stocks), which correlate negatively to volatility surprises, while they are willing to pay a premium for holding the safe assets (Treasury bonds), which correlate positively (the so-called flight-to-safety). Interestingly, when I break down stocks into large-cap and small-cap, I find that the former have a greater ability to weather volatility surprises, which partially accounts for their lower expected returns.

Intradaily periodicity and volatility spillovers between international stock index futures markets

This paper examines short-run information transmission between the U.S. and U.K. markets using the S&P 500 and FTSE 100 index futures. Ultrahighfrequency futures data are employed—which have a number of advantages over the low-frequency spot data commonly used in previous studies—in establishing that volatility spillovers are in fact bidirectional. The generalized autoregressive conditionally heteroskedastic model (GARCH) is employed to estimate the mean and volatility spillovers of intraday returns. A Fourier flexible function is utilized to filter the intradaily periodic patterns that induce serial correlation in return volatility. It was found that estimates of volatility persistence and speed of information transmission are seriously affected by intradaily periodicity. The bias in parameter estimation is removed by filtering out the intradaily periodic component of the transaction data. Contrary to previous findings, there is evidence of spillovers in volatility between the U.S. and U.K. markets. Results indicate that the volatility of the U.S. market is affected by the most recent volatility surprise in the U.K. market. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:553–585, 2005

Cross-border transmission of stock price volatility: evidence from the overlapping trading hours

Using high-frequency (5-minute returns) data, the transmission pattern of intraday volatility among three international stock markets (i.e., the United States, the United Kingdom, and Canada) during their overlapping trading hours (9:30–11:30 a.m. New York time). The major findings are as follows. First, the conditional variance of a domestic market is affected not only by the volatility surprises of its own market, but also by those of foreign markets. This finding holds for the United States as well as for Canada and the United Kingdom, implying that the information contained in the volatility surprises of each national market is clearly transmitted to other national markets. The volatility spillover is not unidirectional. Second, the magnitude of volatility spillover does not decrease monotonically as the lag length increases, indicating that the effect of a foreign volatility shock on the conditional variance of the domestic market tends to persist.