Aggregate Volatility Research Articles

Predictive Regressions for Aggregate Stock Market Volatility with Machine Learning

We investigate whether machine learning techniques and a large set of financial and macroeconomic variables can be used to predict future S&P realized volatility. We evaluate the aggregate volatility predictions of regularization methods (Ridge, Lasso, and Elastic Net), tree-based methods (Random forest and Gradient boosting), and forecast combination methods. We find that the machine learning algorithms outperform autoregressive benchmark models, both statistically and economically, and that the tree-based methods perform the best. In addition to its past realizations, our analysis reveals that the main drivers of aggregate volatility are several financial and macroeconomic uncertainty proxies.

Great volatility, great moderation and great moderation again

We investigate the sources of changes in GDP volatility observed from 1966 to 2018. We develop a general equilibrium model and calibrate it to US data to characterize the contribution of micro level productivity shocks, inter-sectoral linkages and households' behavior to aggregate volatility. Our results show that changes in sectoral volatility played an important role in shaping GDP volatility and that asymmetries in the economy had a different impact on aggregate volatility over time. Moreover, we show that, despite an increase before the financial crisis of 2007, aggregate volatility has remained low until 2018.

Price Setting and Volatility: Evidence from Oil Price Volatility Shocks

<ns2:p>How do changes in aggregate volatility alter the impulse response of output to monetary policy? To analyze this question, I study whether individual prices in Producer Price Index micro data are more likely to change and to move in the same direction when aggregate volatility is high, which would increase aggregate price exibility and reduce the effectiveness of monetary policy. Taking advantage of plausibly exogenous oil price volatility shocks and heterogeneity in oil usage across industries, I find that price changes are more dispersed and less frequent, implying that prices are less likely to move in the same direction when aggregate volatility is high. This contrasts with findings in the literature about idiosyncratic volatility. I use a state-dependent pricing model to interpret my findings. Random menu costs are necessary for the model to match the positive empirical relationship between oil price volatility and price change dispersion. This is the case because random menu costs reduce the extent to which firms with prices far from their optimum all act in a coordinated fashion when volatility increases. The model implies that increases in aggregate volatility do not substantially reduce the ability of monetary policy to stimulate output.</ns2:p>

The rise of big U.S. banks and the fall of big European banks: A statistical decomposition

Bank asset concentration has risen in the U.S. since the 1980s and declined in Europe since 2008. We decompose this rise and fall of big banks using rank-based empirical methods that characterize dynamic power law distributions into two shaping factors: the growth rates and idiosyncratic volatilities of assets for different size-ranked banks. Higher relative growth rates for the largest U.S. banks led to greater asset concentration. Idiosyncratic volatilities for U.S. banks declined, resulting in lower fundamental volatility of bank assets — the aggregate volatility due to idiosyncratic, bank-specific shocks — despite the rise in concentration since the 1990s. In contrast, the relative growth rates for the largest European banks declined and this led to the lower concentration of European bank assets. Over this same time period, the idiosyncratic volatilities and fundamental volatility of European bank assets have been stable.

Motivated Beliefs in Coordination Games

We characterize how wishful thinking affects the interpretation of information in economies with strategic and external effects. While players always choose to exhibit overconfidence in private information, their interpretation of public information depends on how non-fundamental volatility affects payoffs. When volatility increases payoffs, players may endogenously disagree: some under-react to public news, while others overreact. In contrast to rational expectations, public information can increase dispersion in actions while private information can increase aggregate volatility. Our analysis has novel implications for the social value of information and demonstrates how endogenous beliefs can reconcile recent evidence on forecast revisions and information rigidities.

Investor attention and oil market volatility: Does economic policy uncertainty matter?

This paper aims to investigate the impact of investor attention on the oil market volatility by using the Google search volume index as the measurement of investor attention. In particular, we decompose aggregate volatility into good volatility and bad volatility to gain a deeper insight into this issue. Additionally, we discuss whether the relationship between investor attention and oil market volatility changes with the adjustment of economic policy uncertainty in G7 and BRIC. The empirical results show that changes in investor attention mainly affect bad volatility rather than good volatility, and this impact is positive, symmetric, and transient. Interestingly, this impact can also lead to changes in future oil prices through the volatility feedback effect channel. Moreover, we find that the economic uncertainty policy can enhance the positive relationship between investor attention and bad volatility, but this relationship seems to be only sensitive to economic policy uncertainty in the US and Canada

Financial crises, firm‐level shocks and large downturns: Evidence from Greece

AbstractHow do firm‐specific shocks contribute to large economic downturns associated with financial crises? Using a large and representative dataset on Greek firms covering all sectors of the economy over the period 2000–2014, we find that the contribution of firm‐specific shocks to the volatility of aggregate sales growth increased substantially (about 30%) during the Greek financial crisis and dominated the contribution of macro‐economic and sectoral shocks. We also find that, throughout the sample period, inter‐firm linkages are two and a half times as important as the direct effect of firm shocks in driving aggregate fluctuations. However, during the financial crisis, the Greek economy became more granular, and the direct effect of firm‐specific shocks had increased importance in driving aggregate volatility.

Global Value Chains, Volatility and Safe Openness: Is Trade a Double-Edged Sword?

Modern production has become increasingly reliant on inputs sourced from abroad. These multi-country production processes are known as global value chains (GVCs). While it is generally accepted that GVCs increase productivity, by allowing producers to reap the gains from their individual comparative advantage, there is disagreement on the effects they have on macroeconomic volatility. In particular, some have argued that GVCs are a ‘double-edged sword’: they increase productivity, but also increase volatility. In this paper, we show that the relationship between GVC integration and volatility is ambiguous in theory, and insignificant in the data. This supports our headline conclusion that there is no compelling reason to fear the double-edged sword: a blanket reduction in GVC integration would impose economic costs without necessarily, or significantly, reducing economic volatility. Another feature of today’s GVCs is their concentration around a few central ‘hubs’. This has led to a discussion of alternative industrial policies that can reshape GVCs in the longer-run, in particular re-shoring and diversification. Within our model, policies to re-shore production lead to an increase in aggregate volatility, as they effectively increase the concentration of value chains on domestic sources. On the other hand, diversification of GVCs among foreign suppliers can lower volatility, by lowering the exposure to any single country. Finally, while the debate around the effects of trade on business-cycle volatility pre-dates the Covid-19 pandemic, recent events have increased the broader discussion of ‘safe trade openness’. We discuss the scope for policy actions to make trade safe and open, drawing out an analogy with the financial sector reforms enacted following the global financial crisis. We emphasise the merits of co-operation and multilateralism in order to underpin safe openness in the global trading system, and caution against direct policy interventions that are not targeted to address well-identified market failures.

Predicting firm-level volatility in the United States: the role of monetary policy uncertainty

Theoretically, there is exists a strong link between monetary policy rate uncertainty and equity return volatility, since asset pricing models assume the risk-free rate to be a key factor for equity prices. Most studies, however, focus on aggregate volatility proxies, ignoring the evidence that idiosyncratic risk could also be an important consideration, particularly for under-diversified investors and arbitrageurs. Given this, we examine firm-level annual data for the United States over 1997 to 2016, and show that monetary policy uncertainty does indeed contain significant predictive information over realized and implied volatilities at both the firm- and industry-level. The predictive power of monetary policy uncertainty is found to be robust across the low and high quantiles of volatility with higher policy uncertainty predicting higher firm-level volatility in subsequent periods. While the strongest possible volatility effect is observed in the case of Retail Trade, we observe opposite resuThis paper provides novel evidence for the This paper provides novel evidence for the predictive power of monetary policy uncertainty (MPU) over stock return volatility at the firm level based on a dataset constructed from 9,458 U.S. firms. Our findings show that monetary policy uncertainty contains significant predictive information over realized and implied volatilities at both the firm- and industry-level, with higher policy uncertainty predicting higher volatility in subsequent periods. While the strongest possible volatility effect is observed in the case of Retail Trade, we observe opposite results for Mining with high policy uncertainty predicting lower volatility in this sector. We argue that the dual nature of the underlying commodity for Mining companies, both as a consumption and investment asset, drives the negative effect of policy uncertainty on volatility in this sector. Nevertheless, the findings highlight the predictive information captured by monetary policy actions on the idiosyncratic component of equity market volatility.predictive power of monetary policy uncertainty (MPU) over stock return volatility at the firm level based on a dataset constructed from 9,458 U.S. firms. Our findings show that monetary policy uncertainty contains significant predictive information over realized and implied volatilities at both the firm- and industry-level, with higher policy uncertainty predicting higher volatility in subsequent periods. While the strongest possible volatility effect is observed in the case of Retail Trade, we observe opposite results for Mining with high policy uncertainty predicting lower volatility in this sector. We argue that the dual nature of the underlying commodity for Mining companies, both as a consumption and investment asset, drives the negative effect of policy uncertainty on volatility in this sector. Nevertheless, the findings highlight the predictive information captured by monetary policy actions on the idiosyncratic component of equity market volatility.lts for Mining with high policy uncertainty predicting lower volatility in this sector. We argue that the dual nature of the underlying commodity for Mining companies, both as a consumption and investment asset, drives the negative effect of policy uncertainty on volatility in this sector. Nevertheless, the findings highlight the predictive information captured by monetary policy actions on the idiosyncratic component of equity market volatility.

Equilibrium Asset Pricing with Leverage and Default

ABSTRACTWe develop a general equilibrium model linking the pricing of stocks and corporate bonds to endogenous movements in corporate leverage and aggregate volatility. The model features heterogeneous firms making optimal investment and financing decisions and connects fluctuations in macroeconomic quantities and asset prices to movements in the cross section of firms. Empirically plausible movements in leverage produce realistic asset return dynamics. Countercyclical leverage drives predictable variation in risk premia, and debt‐financed growth generates a high value premium. Endogenous default produces countercyclical aggregate volatility and credit spread movements that are propagated to the real economy through their effects on investment and output.

The relationship between driving volatility in time to collision and crash-injury severity in a naturalistic driving environment

As a key indicator of unsafe driving, driving volatility characterizes the variations in microscopic driving decisions. This study characterizes volatility in longitudinal and lateral driving decisions and examines the links between driving volatility in time to collision and crash-injury severity. By using a unique real-world naturalistic driving database from the 2nd Strategic Highway Research Program (SHRP), a test set of 671 crash events featuring around 0.2 million temporal samples of real-world driving are analyzed. Based on different driving performance measures, 16 different volatility indices are created. To explore the relationships between crash-injury severity outcomes and driving volatility, the volatility indices are then linked with individual crash events including information on crash severity, drivers’ pre-crash maneuvers and behaviors, secondary tasks and durations, and other factors. As driving volatility prior to crash involvement can have different components, an in-depth analysis is conducted using the aggregate as well as segmented (based on time to collision) real-world driving data. To account for the issues of observed and unobserved heterogeneity, fixed and random parameter logit models with heterogeneity in parameter means and variances are estimated. The empirical results offer important insights regarding how driving volatility in time to collision relates to crash severity outcomes. Overall, statistically significant positive correlations are found between the aggregate (as well as segmented) volatility measures and crash severity outcomes. The findings suggest that greater driving volatility (both in longitudinal and lateral direction) in time to collision increases the likelihood of police reportable or most severe crash events. Importantly, compared to the effect of volatility in longitudinal acceleration on crash outcomes, the effect of volatility in longitudinal deceleration is significantly greater in magnitude. Methodologically, the random parameter models with heterogeneity-in-means and variances significantly outperformed both the fixed parameter and random parameter counterparts (with homogeneous means and variances), underscoring the importance of accounting for both observed and unobserved heterogeneity. The relevance of the findings to the development of proactive behavioral countermeasures for drivers is discussed.

Input-output linkages in Pigouvian industrial fluctuations

Input-output linkages facilitate expectations-driven fluctuations. Signals about yet-to-be-realized aggregate (macro) and idiosyncratic (micro) fundamentals in the future affect current equilibrium outcome through chains of input needs when inputs require time to build. Depending on their importance on these chains, firms respond differently to common signals. The network structure, together with input time to build, determines the magnitude of signal-induced aggregate fluctuations. Macro signals need large input shares on average to produce sizable aggregate volatility, whereas micro signals require cross-sectional variations of input importance. Changing input time to build and altering the network structure have similar effects on equilibrium outcomes.

Information, Interdependence, and Interaction: Where Does the Volatility Come from?

We analyze a class of games with interdependent values and linear best responses. The payouncertainty is described by a multivariate normal distribution that includes the pure common and pure private value environment as special cases. We characterize the set of joint distributions over actions and states that can arise as Bayes Nash equilibrium distributions under any multivariate normally distributed signals about the payostates. We characterize maximum aggregate volatility for a given distribution of the payostates. We show that the maximal aggregate volatility is attained in a noise-free equilibrium in which the agents confound idiosyncratic and common components of the payostate, and display excess response to the common component. We use a general approach to identify the critical information structures for the Bayes Nash equilibrium via the notion of Bayes correlated equilibrium, as introduced by Bergemann and Morris (2013b). Jel Classification: C72, C73, D43, D83.

Breaking the Word Bank: Measurement and Effects of Bank Level Uncertainty

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.

Information and Interaction

We study a linear interaction model with asymmetric information. We first characterize the linear Bayes Nash equilibrium for a class of one dimensional signals. It is then shown that this class of one dimensional signals provide a comprehensive description of the first and second moments of the distribution of outcomes for any Bayes Nash equilibrium and any information structure. We use our results in a variety of applications: (i) we study the connections between incomplete information and strategic interaction, (ii) we explain to what extent payoff environment and information structure of a economy are distinguishable through the equilibrium outcomes of the economy, and (iii) we analyze how equilibrium outcomes can be decomposed to understand the sources of individual and aggregate volatility.

Production network structure, service share, and aggregate volatility

This paper shows that GDP growth volatility declines with production network diversification. To account for this evidence, I build a multisector model with CES technologies and a cost of complexity in the bundle of intermediates. Production network diversification decreases volatility when intermediate inputs and labor are substitute inputs. U.S. sectoral data suggest that labor and intermediates are substitutes in service sectors. Therefore, a calibrated model that then also matches each country's production network can quantitatively generate the empirical patterns. The model also explains why service-oriented countries are less volatile: service sectors have a more diversified set of suppliers.

Financial Crises, Firm-Level Shocks, and Large Downturns: Evidence From Greece

How do firm-specific shocks contribute to large economic downturns associated with financial crises? Using a large and representative dataset on Greek firms covering all sectors of the economy over the period 2000-2014, we find that the contribution of firm-specific shocks to the volatility of aggregate sales growth increased substantially (about 30%) during the Greek financial crisis and dominated the contribution of macroeconomic and sectoral shocks. We also find that, throughout the sample period, inter-firm linkages are two and a half times as important as the direct effect of firm shocks in driving aggregate fluctuations. However, during the financial crisis, the Greek economy became more granular and the direct effect of firm-specific shocks had increased importance in driving aggregate volatility.

Market returns and risk factors for the emerging economies

ABSTRACT This paper creates daily factors (much like Fama and French) and monthly aggregate idiosyncratic volatility measures for 15 emerging economies. Data deficiencies, including firm balance sheet data, are bypassed by constructing daily country factors through principal components. We focus on each country’s aggregate idiosyncratic volatility. We derive empirical measures for these volatilities and test them against prior known results. In addition, we examine the attributes of these measures by employing regime-switching, trend tests, and multiple structural breaks. An expository effort is also made to interpret the derived factors and examine their performance. Our findings indicate that the derived volatility series are robust and empirically valid in correctly detecting most of the exact timing of known volatility periods. The derived factors and volatility series will be posted online for free public access and are expected to be updated on a regular basis.

Macroeconomic fundamentals, jump dynamics and expected volatility

In this paper, we develop a new volatility model capturing the effects of macroeconomic variables and jump dynamics on the stock volatility. The proposed GARCH-Jump-MIDAS model is applied to the S&P 500 index. Our in-sample results indicate that macroeconomic activities have important impacts on aggregate market volatility. Out-of-sample evidence suggests that our model with macroeconomic variables significantly outperform a wide range of competitors including the original GARCH(1,1), GARCH-MIDAS and GJR-A-MIDAS models. The volatility timing results also show that the information from jumps and macroeconomic activity is helpful for improving the portfolio performance.

Is aggregate volatility a priced risk factor?

AbstractThis study shows that the relationships between sensitivity to changes in aggregate volatility and expected return on stocks documented by Ang et al. (Journal of Finance, 2006, 61, 259–299) for the 15‐year period from 1986 to 2000 have disappeared in the following 15‐year period. Aggregate volatility betas in the portfolio preformation month have not predicted postformation returns. Alphas from time‐series regressions of excess returns on the high‐minus‐low sensitivity to aggregate volatility portfolio with respect to the CAPM, the Fama–French three‐factor model, and the Fama–French five‐factor model have not been statistically different from zero. Finally, the price of aggregate volatility risk has not been statistically different from zero. Analysis based on high‐frequency data support these results. Thus, the importance of aggregate volatility as a factor in the presence of well‐known factors such as SMB and HML appears to be unclear.