Aggregate Volatility Research Articles

On the granularity of the German economy – first evidence from the top 100 companies panel database

ABSTRACT Applying the approach suggested by Gabaix (Econometrica 2011) this paper uses the newly available Top 100 Companies Panel Database for Germany to demonstrate that idiosyncratic shocks in the largest firms seem not to be important for an understanding of the aggregate volatility of the German economy. This evidence is in contrast with findings for other countries and it differs from earlier results for parts of the German economy.

The Collateral Link between Volatility and Risk Sharing

We show that aggregate volatility affects the extent to which agents can share idiosyncratic risks through the valuation of collateral. Both private and public assets are used in insurance markets as collateral, but their exposure to volatility differs. While aggregate volatility decreases the value of private assets—they are exposed to more variation—it increases the value of public assets—they become more valuable to smooth consumption intertemporally. Hence, a more volatile economy tends to damage risk sharing when the composition of collateral is biased toward private assets. As we show that a stable economy is more propitious to the creation of private collateral, stability makes risk sharing increasingly fragile to volatility shocks. We find empirical evidence that the higher use of private assets in the U.S. has affected the sensitivity of risk sharing to aggregate volatility as predicted by our model.

Financial Intermediation and Economic Growth: From Idiosyncratic Shocks to Aggregate Fluctuations

The objective of this paper is to explore the transmission of non-capital shocks through banking networks. We develop a methodology to construct non-capital (real) shocks, idiosyncratic shocks, using labor productivity shocks to large firms. We document a change in the relationship between (foreign) idiosyncratic shocks in state j and (domestic) economic growth in state i between 1978 and 2000 wherein the relation changes from positive to negative over this period. We show that the contemporaneous changes in banking integration across states is a key driver of this phenomenon. This is driven by geographically diversified banks diverting funds away from economies experiencing negative shocks towards other unaffected economies. This result operates through changes in bank loan supply. Our instrumental variable estimates suggest that a 1% increase in the bank loan supply is associated with a 0.05-0.26 pp increase in economic growth. Lastly, we argue that this mechanism can explain the decrease in covariance of state-level fluctuations, potentially explaining the Great Moderation, the period of relatively low aggregate volatility.

The Role of Incumbency, Ethnicity, and New Parties in Electoral Volatility in Slovakia

This paper analyzes electoral volatility in the 2020 Slovak elections at the level of individual voters using exit poll surveys. The availability of exit polls from the previous elections of 2012 and 2016 allows us to put the 2020 election in context and analyze the patterns (and deviances from them) observed across the three elections. Furthermore, the paper summarizes the aggregate volatility since 1992, demonstrating a high level of net volatility with peaks of over 30 percent. As for the individual level, the analysis concentrates on three important issues in volatility research: (1) vote losses of government parties and the incumbent effect; (2) the role of new parties in mobilization of previous non-voters and first-time voters; (3) since Slovakia is a country with a significant Hungarian minority, special attention is given to vote switching by Hungarian voters and more general patterns of ethnic voting.

Liquidity and Volatility

Liquidity provision is a bet against private information: if private information turns out to be higher than expected, liquidity providers lose. Since information generates volatility, and volatility co-moves across assets, liquidity providers have a negative exposure to aggregate volatility shocks. As aggregate volatility shocks carry a very large premium in option markets, this negative exposure can explain why liquidity provision earns high average returns. We show this by incorporating uncertainty about the amount of private information into an otherwise standard model. We test the model in the cross section of short-term reversals, which mimic the portfolios of liquidity providers. As predicted by the model, reversals have large negative betas to aggregate volatility shocks. These betas explain their average returns with the same risk price as in option markets, and their predictability by VIX in the time series. Volatility risk thus explains the liquidity premium among stocks and why it increases in volatile times. Our results provide a novel view of the risks and returns to liquidity provision.

Breaking the Word Bank: Effects of Verbal Uncertainty on Bank Behavior

Banks differ from non-financial firms as banks must communicate to both regulators and shareholders. Also, unlike non-financial firms, banks possess opaque and complex balance sheets and are the main providers of credit to the real economy. In this paper, I propose a new index to detect the idiosyncratic uncertainty banks face at the bank-quarter level by applying natural language processing techniques to earnings conference call transcripts. The index reveals which banks at a given quarter signal more uncertainty about their balance sheets. Higher uncertainty is associated with lower lending and higher liquidity the next quarter, suggesting active management of uncertainty. The active management of uncertainty is more pronounced during periods of high aggregate volatility and for banks with more skin in the game. Using loan level data and firm fixed effects, I control for demand-side factors and find that higher bank level uncertainty is associated with lower loan issuances the following quarter.

Aggregate volatility risk: International evidence

Using a procedure analogous to that of Ang et al. (2006), this paper documents that aggregate volatility risk does not appear to be priced in European equity markets. Specifically, based on the 2002–2016 period (for which European stock return data is available), the price of aggregate volatility risk is not statistically different from zero. Analysis based on GARCH-class and high-frequency intraday data models support these results. Consequently, contrary to what has been reported in some studies that examine U.S. data, whether aggregate volatility risk is priced in equity markets is an open question.

Evidence on aggregate volatility risk premium for the French stock market

PurposeThe purpose of this paper is to examine alternative six- and seven-factor equity pricing models directed at capturing a new factor, aggregate volatility, in addition to market, size, book to market, profitability, investment premiums of the Fama and French (2015) and Fama and French’s (2018) aggregate volatility augmented model.Design/methodology/approachThe models are tested using a time series regression and Fama and Macbeth’s (1973) methodology.FindingsThe authors show that both six- and seven-factor models best explain average excess returns on the French stock market. In fact, the authors outperform Fama and French’s (2018) model. The authors use sensitivity of aggregate volatility of a stock VCAC as a proxy to construct the aggregate volatility risk factor. The spanning tests suggest that Fama and French’s (1993, 2015, 2018) and Carhart’s (1997) models do not explain the aggregate volatility risk factor FVCAC. The results show that the FVCAC factor earns significant αs across the different multifactor models and even after controlling for the exposure to all the other in Fama and French’s (2018) model. The asset pricing tests show that it is systematically priced. In fact, the authors find a significant and negative (positive) relation between the aggregate volatility risk factor and the excess returns in the French stock market when it is rising (falling), in addition, periods with downward market movements tend to coincide with high volatility.Originality/valueThe authors contribute to the related literature in several ways. First, the authors test two new empirical six- and seven-factor model and the authors compare them to Fama and French’s (2018) model. Second, the authors give new evidence about the VCAC, using it for the first time to the authors’ knowledge, to construct a volatility risk premium.

Credit Market Frictions and the Linkage between Micro and Macro Uncertainty

Micro uncertainty (cross-sectional dispersion) and macro uncertainty (volatility of aggregate economic variables) are conceptually distinct. However, empirically, they comove and are countercyclical. This paper builds a general equilibrium model and demonstrates that credit market frictions allow micro uncertainty to drive time-varying macro uncertainty endogenously. In the model, as dispersion increases, 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.

Aggregate Volatility Risk and Empirical Factors: An International Study

ABSTRACT We study the aggregate volatility risk in international stock markets. We examine four regional (North America, Europe, Japan, and Asia Pacific) stock markets to see if the aggregate volatility risk is priced and find out its relationship with regional empirical factors. We find that the aggregate volatility risk is priced robustly across stocks in all regions but Japan. Within the intertemporal capital asset pricing model framework, we show that the aggregate volatility risk is closely connected with the momentum profits. Our theoretical framework coupled with the return and volatility spillover effects hints at an interesting explanation for the coexistence of global and local factors.

When do low-frequency measures really measure transaction costs?

We compare popular measures of transaction costs based on daily data with their high-frequency data-based counterparts. We find that for U.S. equities and major foreign exchange rates, (i) the measures based on daily data are highly upward biased and imprecise; (ii) the bias is a function of volatility; and (iii) it is primarily volatility that drives the dynamics of these liquidity proxies both in the cross section as well as over time. We corroborate our results in carefully designed simulations and show that such distortions arise when the true transaction costs are small relative to volatility. Many financial assets exhibit this property, not only in the last two decades, but also in the previous century. We document that using low-frequency measures as liquidity proxies in standard asset pricing tests may produce sizable biases and spurious inferences about the pricing of aggregate volatility or liquidity risk.

Dissecting the Yield Curve: The International Evidence

We develop a term structure model that decomposes nominal yields into the sum of an expectation, term premium, and convexity term, and in turn of their real and inflation counterparts. The model explicitly captures the interrelation between yield factors and macroeconomic conditions while allowing for aggregate stochastic volatility. We extract the components from the nominal and real yield curve of the US, Euro Area, UK, and Japan. We find that the bulk of yield dynamics comes from short rate expectations. Term premia vary over time and increase with maturity, but account for a smaller fraction of yield variance than previously documented. Over time, we observe a sustained decline in short real rate expectations and significant convexity effects. With regard to yield comovement, the US generates the strongest spillovers at the long-end of the yield curve, whereas the Japanese market is the top importer of shocks.

Does Unusual News Forecast Market Stress?

An increase in “unusual” news with negative sentiment predicts an increase in stock market volatility. Unusual positive news forecasts lower volatility. Our analysis is based on over 360,000 articles on 50 large financial companies, published during the period of 1996–2014. Unusualness interacted with sentiment forecasts company-specific and aggregate volatility several months ahead. Furthermore, unusual news is reflected more slowly in aggregate volatility than company-specific volatility. News measures from articles explicitly about the “market,” which are more easily accessible to investors, do not forecast volatility. The observed responses of volatility to news may be explained by attention constraints on investors.

The role of the volatility index in asset pricing: The case of the Indian stock market

This study examines whether the volatility index, a proxy for aggregate volatility risk, can be used as an additional factor in the standard asset pricing model for the Indian stock market after controlling for well-documented risk factors. This study first examines the empirical performance of the capital asset pricing model, the Fama-French three-factor model and the Carhart four-factor model. On the basis of a GRS test, the Fama-French three-factor model is used as the baseline model to explore the role of the volatility index as an additional factor considered in the asset pricing model. The factor mimicking portfolio returns for the volatility index innovations is constructed and is included as an additional factor in the Fama-French three-factor model. The study provides strong evidence of size effects but relatively weak value-growth and momentum effect. Further, stock’s sensitivity to volatility index innovation is a priced risk factor during high volatility period, but not during low volatility period. Nevertheless, the addition of volatility risk factor leads to marginal improvements in the capacity for the model to explain the variation in stock returns. The results of this study will help financial analysts and corporate financial managers to evaluate the performance of professionally managed portfolios.

Uncertainty Shocks as Second-Moment News Shocks

Abstract We provide evidence on the relationship between aggregate uncertainty and the macroeconomy. Identifying uncertainty shocks using methods from the news shocks literature, the analysis finds that innovations in realized stock market volatility are robustly followed by contractions, while shocks to forward-looking uncertainty have no significant effect on the economy. Moreover, investors have historically paid large premia to hedge shocks to realized but not implied volatility. A model in which fundamental shocks are skewed left can match those facts. Aggregate volatility matters, but it is the realization of volatility, rather than uncertainty about the future, that has been associated with declines.

The aggregate implications of changes in the labour force composition

Labour composition by gender, age, and education has undergone dramatic changes over the last half century in the United States. Furthermore, the volatility of total market hours differs systematically between genders, age, and education groups. I develop a large scale business cycle model where these demographic patterns and their transitional dynamics are taken into account and find that demographic change accounts for 30% of the observed changes in aggregate volatility over this period of time. Additionally, these demographic changes are responsible for a significant fraction of the GDP growth observed in the considered period of time.

A tale of two volatilities: Sectoral uncertainty, growth, and asset prices

What is the impact of higher technological volatility on asset prices and macroeconomic aggregates? I find the answer hinges on its sectoral origin. Volatility that originates from the consumption (investment) sector drops (raises) macroeconomic growth rates and stock prices. Moreover, consumption (investment) sector’s technological volatility has a positive (negative) market price of risk. I show that a quantitative two-sector DSGE model that features monopolistic power for firms and sticky prices, as well as early resolution of uncertainty, can explain the differential impact of sectoral volatilities on real and financial variables. In all, the sectoral decomposition of volatility can overturn the typical negative relation between aggregate volatility and economic activity.

Does the volatility of volatility risk forecast future stock returns?

This study investigates whether the forward-looking volatility of aggregate volatility (VOV) risk forecasts future stock returns in the US equity market. We find that stocks with higher sensitivities to changes in VOV constructed from VIX options have higher future returns than those with lower sensitivities. In particular, VOV constructed from deep out-of-the-money put options has the strongest predictive power, and the strongest predictability of VOV betas is found for investment horizons between 10-day to 1-month. Our findings are robust after considering estimation uncertainty of VOV betas and controlling for common pricing factors.

Business cycle implications of capacity constraints under demand shocks

When capacity constraints limit the production of heterogeneous firms, demand shocks can endogenously generate a number of important business cycle regularities: recessions are deeper than booms, economic volatility is countercyclical, the aggregate Solow residual is procyclical and the fiscal multiplier is countercyclical. The model's main mechanism is that the share of firms at their production limit is strongly procyclical. A baseline calibration of a basic New Keynesian DSGE model with capacity constraints delivers more than 25% of the empirically observed asymmetry in output, 18% of the additional cross-sectional dispersion in recessions and around 25% of the additional aggregate volatility, and more than 50% of the fluctuations in the Solow residual. The model implies fluctuations in the fiscal multiplier of around 0.12 between expansions and recessions.

Do (all) sectoral shocks lead to aggregate volatility? Empirics from a production network perspective

Productive diversification has long been acknowledged as a volatility-reducing strategy. Yet, recent theoretical contributions have shown that, in strongly diversified economies, idiosyncratic shocks could translate into aggregate volatility via the network of inter-industry linkages. By exploiting exogenous cross-country-sector variations in demand shocks during the 2008 Great Recession, we provide empirical evidence that the network properties of a sector affected by an individual shock determine its propensity to transmit volatility to the rest of the economy. More precisely, shocks to sectors that are located in denser parts of a production network fade out over a large number of alternative paths of propagation due to substitution effects, whereas shocks to sectors that are more influential within the network generate aggregate fluctuations through contagion effects. We also find that the impact of sectoral shocks on aggregate volatility (1) is not conditional on sector-level differences in trade intensity, and (2) is larger for developing countries because they tend to have more isolated influential sectors and larger structural holes in their production network. Our results thus help consolidate the two opposite views in the literature on the impact of productive diversification on aggregate volatility.