Aggregate Volatility Research Articles

Credit Market Frictions and the Linkage Between Dispersion and Macro Uncertainty

Cross-sectional dispersion and macro uncertainty (volatility of aggregate economic variables) are conceptually distinct. However, empirically, they both comove and are countercyclical. This paper builds a general equilibrium model and demonstrates that credit market frictions allow cross-sectional dispersion to drive time-varying macro uncertainty endogenously. In the model, as firm-level productivity becomes more dispersed, more firms are pushed to the left tail of the productivity distribution, resulting in more defaults and a depletion in aggregate net worth. This mechanism generates countercyclical leverage and aggregate volatility. The model implies that the government can inject equity in economic downturns to stabilize aggregate volatility. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00166 .

The collateral link between volatility and risk sharing

We show that the effect of aggregate volatility on idiosyncratic risk sharing depends on the nature of collateral sustaining insurance. While volatility increases the value of public assets—more useful for consumption smoothing—it decreases the value of private assets—more exposed to aggregate variation. Hence, a more volatile economy weakens risk sharing when collateral composition is biased towards private assets. When applied to financial intermediaries that rely heavily on private collateral to share risks, aggregate instability is more likely to induce financial instability. We empirically show that the sensitivity of risk sharing to aggregate volatility indeed depends on the collateral composition as predicted by the theory.

Mergers, firm size, and volatility in a granular economy

We study the firm dynamics associated with mergers and acquisitions (M&A) and their implications at the micro and macro levels. Our paper presents three main findings: (i) mergers generate a more fat-tailed firm-size distribution, thereby amplifying granular fluctuations and increasing aggregate volatility; (ii) the impact of mergers depends on strategic market power and endogenous markups; and (iii) under endogenous markups, we provide a novel characterization of the firm size-volatility relationship in which volatility declines disproportionately with size. We build a quantitative model of domestic horizontal mergers and find a sizeable impact of mergers on aggregate volatility using counterfactual analysis.

Sectoral shocks, production linkages, and business cycles in China

This paper develops a dynamic multisector model to examine the contribution of sectoral productivity shocks and input-output linkages to the dynamics of the Chinese economy. Our baseline simulations replicate the volatility and comovement in the data fairly well. More importantly, we show that the idiosyncratic components of sectoral productivity shocks together with the production linkages are the primary drivers of volatility and comovement, while the common components of sectoral productivity shocks mainly result in resource reallocation across sectors through substitution effects. The sensitivity analysis highlights the importance of elasticity-of-substitution parameters. Finally, the share of state-owned enterprises in each sector is shown to have significant impact on the aggregate volatility.

Higher order moments of stock market volatility and the cross section of stock returns

This study investigates whether risks captured by higher order moments of stock market volatility are priced in the cross section of stocks. These moments are estimated from daily snapshots of VIX option prices and, therefore, are conditional and forward-looking. Empirical results show that exposure to kurtosis of aggregate volatility (KOV) is a significant cross-sectional determinant of stock returns. The KOV risk premium is negative and robust to controlling for exposures to alternative measures of economic uncertainty as well as stock characteristics.

Do Granular Shocks Generate Sizeable Aggregate Volatility?

Do Granular Shocks Generate Sizeable Aggregate Volatility?

Asymmetric and time-frequency volatility connectedness between China and international crude oil markets with portfolio implications

This paper tries to examine asymmetric and time-frequency volatility connectedness between the Chinese crude oil futures market and international oil benchmarks. To this end, we separate the realized volatility as bad or good volatility and decompose the aggregate volatility connectedness among these oil markets into the short-, medium-, and long-term components. Our results first show that these crude oil markets are highly connected, whereas the Chinese crude oil futures market is a net receiver in the volatility system. Second, the spillover effect caused by bad volatility is significantly different from that of good volatility, revealing significant asymmetry in the volatility spillovers. Third, the volatility spillovers in the short-term frequency band account for the most of total volatility spillovers. Finally, our results prove that the volatility connectedness information among different oil markets helps design trading strategies and shed light on the arbitrage opportunities in the Chinese new crude oil futures market.

Wage rigidity and destabilizing spirals

This paper investigates the impact of real wage rigidity on the (de)stabilizing role of demand feedback. I show that destabilizing supply–demand feedback driven by countercyclical precautionary savings demand against uninsured unemployment risk is fundamentally a matter of rigid real wage adjustments over business cycles. Given the estimated wage rigidity consistent with aggregate labor market dynamics in the United States, the quantitative results suggest that the unemployment risk channel has a minor impact on aggregate volatility.

Forecasting stock market volatility: The sum of the parts is more than the whole

The volatility of financial assets can be decomposed into upside volatility and downside volatility. However, these two components have unique properties, so their predictability is completely different. In this paper, we explore a new forecasting method to predict the S&P 500 volatility by separately modeling upside volatility and downside volatility and summing the forecasts up. Our new method is proved to have better performance compared with directly modeling aggregate volatility. Moreover, the gains in forecast accuracy are robust concerning the individual and combined models.

Idiosyncratic Volatility, Growth Options, and the Cross-Section of Returns

Abstract The value effect and the idiosyncratic volatility (IVol) discount arise because growth firms and high IVol firms beat the CAPM during periods of increasing aggregate volatility (market volatility and average IVol), that makes their risk low. All else equal, growth options’ value increases with volatility, an effect that is stronger for high IVol firms, for which growth options take a larger fraction of the firm value and firm volatility responds more to aggregate volatility changes. The factor model with the market factor, the market volatility risk factor, and the average IVol factor explains the value effect and the IVol discount. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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.

CLIMATE POLICIES AND BUSINESS CYCLES: THE EFFECTS OF A DYNAMIC CAP

AbstractEmissions are directly linked to economic output and consequently subject to business cycle fluctuations. The present study analyses the interactions between climate policies and business cycles through the lens of a New Keynesian dynamic stochastic general equilibrium model. We compare a static cap-and-trade policy with a dynamically adjusting policy in terms of macroeconomic stabilisation, welfare and emissions price dynamics. The results of the quantitative evaluation suggest that a constant policy leads to lower aggregate volatility but is associated with larger welfare costs. In contrast, under the dynamic policy emissions prices and labour markets display less variations.

Credit Competition and Attenuation of Aggregate Volatility

Credit Competition and Attenuation of Aggregate Volatility

Dynamic asymmetric connectedness in technological sectors

This paper investigates the asymmetric, time, and frequency-based volatility spillovers in global IT industries. To this end, we introduce a new Wavelet-Time Varying Parameter-VAR (W-TVP-VAR) approach to compute connectedness combined with the asymmetrical connectedness of (Barndorff-Nielsen et al., 2010) and (Baruník et al., 2016, 2017) at different frequencies. Daily stock prices of the IT sector in thirteen countries representing the top technologically advanced countries ranging from January 15, 2016, until June 24, 2022, are used. The empirical results show that the aggregate volatility is slowly transmitted across markets with an effect lasting more than twenty days. The result also supports the presence of asymmetrical transmission as downside spillovers dominate upside spillovers, regardless of the frequency. Furthermore, the time-varying spillover shows the dominance of downside spillovers in various crisis periods, especially during the pandemic. The time and frequency-based spillover indicate that the overall spillover increased during the recent COVID-19 pandemic crisis period, which is mostly driven by the short-term, suggesting that panic decisions and herd behavior result in extreme connectedness. These findings are helpful to participants and policymakers.

Profitability anomaly and aggregate volatility risk

Firms with lower profitability have lower expected returns because such firms perform better than expected when market volatility increases. The better-than-expected performance arises because unprofitable firms are distressed and volatile, their equity resembles a call option on the assets, and call options value increases with volatility, all else fixed. Consistent with this hypothesis, the profitability anomaly and its exposure to aggregate volatility risk are stronger for distressed and volatile firms; for such firms, aggregate volatility risk explains roughly half of the profitability anomaly, while in single sorts on profitability about 70% of the anomaly is explained.

Banking sector concentration, credit shocks and aggregate fluctuations

This paper studies whether the raise in concentration experienced by the Spanish banking sector has lead to the increase of bank-specific credit shocks contribution to aggregate credit. We decompose aggregate credit volatility and find that (i) the Spanish banking sector is granular, (ii) the direct effect of bank-specific shocks accounts for the overwhelming majority of the variation in aggregate volatility, contrary to the manufacturing sector, and (iii) the raise in concentration translated into an increase of bank-specific shocks contribution to aggregate volatility.

Synthetic Options and Implied Volatility for the Corporate Bond Market

AbstractWe synthetically create option contracts on a corporate bond index using CDX swaptions, overcoming the limitations that stem from the lack of traded corporate bond options. Our approach allows us to estimate forward-looking moments concerning the corporate bond market in a model-free manner. By constructing an aggregate volatility measure and the associated variance risk premium, we examine the role of volatility risk in the corporate bond market. We highlight that the ex ante conditional second and higher moments we estimate from synthetic corporate bond options carry important implications for credit risk models, providing an extra basis for testing their validity.

Brexit news propagation in financial systems: multidimensional visibility networks for market volatility dynamics

In this paper, we propose a multivariate procedure based on multidimensional visibility graphs to detect changes in the market volatility of UK financial indices, considered both before and after Brexit main events. We produce a graph-theoretical representation of volatility time series derived from equity indexes, government rates and currencies to investigate the behavior of the aggregate market volatility through the use of global centrality measures. By employing a stylized agent-based model, we show that the proposed approach is able to discriminate between periods of high and low volatility, both in the temporal dimension and cross-sectionally among multiple time series. We aim at recognizing whether external news related to the Brexit process could induce significant ‘after-shocks’ (and also ‘pre-shocks’) in the system, by producing dynamic relaxation in the values of centrality measures, in line with the cascade effects described by the Omori earthquake law. In particular, high volatility cascades dissipate into the market via power-law relaxation. When compared with other categories of events, such as Bank of England monetary policy announcements, we observe significant market inefficiency in processing Brexit related news. We also find that strong market surprise related to specific Brexit news or a correct discount of some Brexit announcements can produce an inverse Omori law exhibiting convex relaxation.

What drives dynamic connectedness of the U.S equity sectors during different business cycles?

The study investigates (i) the time-varying and directional connectedness of nine equity sectors through intra- and inter-sector volatility spillover periods and (ii) assesses the impact of state variables on aggregate volatility spillovers. The study finds about 76% of volatility linkage is associated with cross-sector volatility transmissions. Aggressive sectors, which are sensitive to macroeconomic risk, play the net volatility transmission role. Defensive sectors that are largely immune to macroeconomic risk play the net volatility receiving role. The intensity and direction of volatility transmissions among the sectors vary with economic expansion and recession periods. Over time, some sectors switching from net transmitting to net receiving role and vice versa. Macro and financial market uncertainty variables significantly impact volatility spillover at lower volatility spillover (economic expansion period) and higher volatility (economic recession periods) volatility spillover quantiles. Political signals are seemingly more imprecise and uninformative during economic expansion or low quantiles, intensifying volatility spillover. Overall, the causal effects of macro, financial, and policy uncertainty variables on aggregate volatility spillover are asymmetric, nonlinear, and time-varying. The study's result supports the cross-hedging and financial contagion views of volatility transmission across nine US equity sectors.

Volatility expectations and disagreement

This paper examines the use of survey-based measures in volatility forecasting. We argue that an aggregate volatility forecast built up from individual forecasts should be the sum of individual expected volatilities and the dispersion in mean return forecasts. We use data coming from a repeated survey to capture volatility expectations and mean returns of investors, and to produce aggregate volatility forecasts. Our survey-based volatility forecasts are consistent and quantitatively similar with forecasts based on GARCH and implied volatility models. This result is robust to both in-sample and out-of-sample comparisons and in response to news.