Time-varying Stochastic Volatility Research Articles

The dynamics of labor force participation: Is all quiet on the Appalachian front?

This study examines the divergence and synchronicity of labor force participation rate (LFPR) dynamics across the USA. Using a dynamic factor model with time-varying stochastic volatility, we decompose each state’s LFPR into a national, regional, and state-specific latent factor. We find significant time variation in our factors and heterogeneous labor market responses and relative sensitivities. Our results show that, save for West Virginia, there is no strong Appalachian regional component, and instead, the national and state-specific components explain much of the variation in state LFPRs. Our results suggest the need for more targeted and localized labor market policies during periods of divergence in LFPRs (i.e., recessions and shocks) and federal policies during national economic booms or periods of recovery.

Examining Individualistic Driving Volatility Changes During and One Year into the COVID-19 Pandemic Using Paneled Connected Vehicle Trajectories

The coronavirus disease 2019 (COVID-19) pandemic led to a substantial reduction in activity-travel resulting from a shift in the primary purpose of travel from work/study to shopping and essential trips. Further, several researchers noted changes to roadway safety during the early phases of the pandemic, such as a decrease in traffic crashes but an increase in traffic fatalities with respect to vehicle miles traveled. However, the existing literature is limited to holistic trends, with little inference to microscopic driving styles. This research utilizes a paneled approach to compare the freeway behavior of select passenger car drivers with respect to changes in driving volatility stemming from the lockdown period and one year into the COVID-19 pandemic. The methodological design employs a rich paneled dataset obtained from the connected vehicles pilot study in Tampa, Florida. Crash data visualization is also performed to identify crash hotspots within the study area. Volatility measures such as the standard deviation, coefficient of variation, and time-varying stochastic volatility ( TVSV) are then generated and fused with traffic and weather information. Hierarchical modeling is then performed using a panel approach to account for unobserved heterogeneity within multiple observations per individual driver. The results show that the series of jerk-related driving volatility measures and the TVSV of speed increase one year into the pandemic, suggesting a reduction in overall roadway safety along the study segment. Based on the observed individual volatility changes, policy recommendations such as enhanced traffic enforcement guidelines, data-driven safety campaigns, and systematic implementation of lockdown protocols were identified.

(A)Synchronous Housing Markets of Global Cities

In this paper we examine house price synchronization in 15 global cities using real house price data from 1995:Q1-2020:Q2. We find that although there is evidence for bilateral positive phase synchronization, there is no evidence for an integrated global housing market for our sample of cities. Using a hierarchical clustering approach, we identify three clusters of cities with similar housing price cycles that are not solely determined by geographic proximity. We interpret this finding as suggestive of a rather segmented housing market for the global cities in our sample. Using a dynamic factor model with time-varying stochastic volatility, we decompose a city’s real housing price growth into a global component, a cluster-based component, and an idiosyncratic component. For most cities in our sample, the global component plays a minor role, whereas the cluster-based factor explains a large fraction of the observed variation in real house price growth, with its contribution peaking during the Great Recession of 2007-09.

Comovements in Military Spending: Evidence from a Dynamic Factor Model with Time-Varying Stochastic Volatility

ABSTRACT This study examines the trend and comovements of military expenditures across 70 countries from 1967 to 2018. While previous research recognizes the degree of cross-correlations using principal components analysis, we adopt a dynamic factor model with time-varying stochastic volatility to examine the trend and comovements in military spending. Specifically, the dynamic factor model allows us to partition the trend of military spending into global, regional, and country-specific components. We find significant evidence of the global factor that explains a considerable portion of the variation in military spending across countries. Moreover, our findings reveal a significant time variation in the importance of the global factor. We also show the presence of regional and country-specific factors in the military spending of countries, but the importance of the regional factor is less than that of the global and country-specific factors. Additionally, we find that countries under consideration are becoming less exposed to global influences with respect to their decisions in military spending over time.

In no uncertain terms: The effect of uncertainty on credit frictions and monetary policy

We examine the interaction of uncertainty and credit frictions in a New Keynesian framework. The model considers credit frictions arising from costly-state verification in the provision of loans to fund the acquisition of capital by entrepreneurs and includes three types of time-varying stochastic volatility shocks related to monetary policy uncertainty, financial risk (micro uncertainty), and macro uncertainty. Key parameters are estimated by the simulated method of moments using data from the United States from 1984:Q1 until 2014:Q4. We find that (1) micro uncertainty has first-order effects that are significantly larger than the effects of macro uncertainty and monetary policy uncertainty; (2) poor credit conditions exacerbate the economic drag from micro uncertainty shocks, amplify the effects of monetary policy shocks, and mitigate the impact of total factor productivity (TFP) shocks; (3) a degree of asymmetry and nonscalability appears in response to monetary policy shocks depending on the degree of nominal rigidities and initial conditions; and (4) monetary policy uncertainty accounts for about one-third of business cycle volatility largely by affecting the size of monetary policy shocks.

(A)Synchronous Housing Markets of Global Cities

In this paper we examine house price synchronization in 15 global cities using real house price data from 1995:Q1-2020:Q2. We find that although there is evidence for bilateral positive phase synchronization, there is no evidence for an integrated global housing market for our sample of cities. Using a hierarchical clustering approach, we identify three clusters of cities with similar housing price cycles that are not solely determined by geographic proximity. We interpret this finding as suggestive of a rather segmented housing market for the global cities in our sample. Using a dynamic factor model with time-varying stochastic volatility we decompose a city's real housing price growth into a global component, a cluster-based component, and an idiosyncratic component. For most cities in our sample, the global component plays a minor role, whereas the cluster-based factor explains a large fraction of the observed variation in real house price growth with its contribution peaking during the Great Recession of 2007-09.

Stochastic Online Generation Control of Cascaded Run-of-the-River Hydropower for Mitigating Solar Power Volatility

To exploit the massive solar energy available in the region, photovoltaic plants have been built in the mountain areas in Southwest China, coexisting with many small cascaded run-of-the-river hydropower plants, interconnected by short-distance transmission lines. Network constraints inside these systems are often negligible. However, due to the volatile nature of solar power and the weak connection to the external power grid, without proper coordination, solar power curtailment and water spillage are inevitable. To address this issue, this paper proposes an ultra-short-term stochastic generation control method for cascaded hydropower to mitigate solar power volatility. Two technical challenges are specifically addressed: to tackle the time-varying stochastic volatility of solar power, the Itô process model is introduced; to characterize the spatial-temporal hydraulic coupling of the cascaded hydropower plants and river operation constraints, a state-space dynamic river model derived from shallow water equations is included. Followingly, a stochastic control (SC) approach for hydropower generation based on stochastic programming is proposed, where hydropower generation commands are parameterized as affine functions of measured solar power, enabling quick response to solar power volatility. Simulation of a real-life cascaded hydro-solar system and a modified system verifies the proposed control method.

Estimating the Output Gap for Emerging Countries: Evidence from Five Southeast Asia Countries

This paper presents an application of a bi-variate unobserved components model of output and inflation to estimate the output gap for five emerging economies in Southeast Asia, including the Philippines, Malaysia, Vietnam, Indonesia, and Thailand. In this paper, time-varying stochastic volatility terms are added in the model to exhibit the change in the size of shocks to the trend and cyclical components of output and inflation of these economies. The results show that estimated output gaps are able to identify the recessions of these economies. Although the shape and magnitude of estimated gaps differ from country to country, these gaps imply that these economies tend to converge to a more stable business cycle over time, except for that of Malaysia. Secondly, inflation is very sensitive to the output gap in Vietnam and Indonesia, but not to those in the Philippines, Malaysia, and Thailand. Thirdly, results suggest that time-varying stochastic volatility is clearly seen in the innovations to the output gaps of the five emerging economies, and Indonesia's potential output. Meanwhile, results confirm that there is no need to add stochastic volatility terms in trend components of output and inflation, except for Indonesia's potential output.

Understanding the Aggregate Effects of Credit Frictions and Uncertainty

We examine the interaction of uncertainty and credit frictions in a New Keynesian framework. To do so, uncertainty is modeled as time-varying stochastic volatility – the product of monetary policy uncertainty, financial risk (micro-uncertainty), and macrouncertainty. The model is solved using a pruned third-order approximation and estimated by the Simulated Method of Moments. We find that: 1) Micro-uncertainty aggravates the information asymmetry between lenders and borrowers, worsens credit conditions, and has first-order effects on real economic activity. 2) When credit conditions are poor, as indicated by elevated credit spreads, additional micro-uncertainty shocks produce even larger real effects. 3) Poor credit conditions notably affect the transmission mechanism of monetary policy amplifying the real effects of monetary shocks while mitigating the economic boost from TFP shocks. 4) While macro-uncertainty and policy uncertainty exert relatively little direct impact on aggregate economic activity, policy uncertainty accounts for around 40% of the business cycle volatility by affecting the size of monetary policy shocks in the presence of nominal rigidities.

Extracting Useful Information from Basic Safety Message Data: An Empirical Study of Driving Volatility Measures and Crash Frequency at Intersections

With the emergence of high-frequency connected and automated vehicle data, analysts can extract useful information from them. To this end, the concept of “driving volatility” is defined and explored as deviation from the norm. Several measures of dispersion and variation can be computed in different ways using vehicles’ instantaneous speed, acceleration, and jerk observed at intersections. This study explores different measures of volatility, representing newly available surrogate measures of safety, by combining data from the Michigan Safety Pilot Deployment of connected vehicles with crash and inventory data at several intersections. For each intersection, 37 different measures of volatility were calculated. These volatilities were then used to explain crash frequencies at intersection by estimating fixed and random parameter Poisson regression models. Given that volatility reflects the degree to which vehicles move, erratic movements are expected to increase crash risk. Results show that an increase in three measures of driving volatility are positively associated with higher intersection crash frequency, controlling for exposure variables and geometric features. More intersection crashes were associated with higher percentages of vehicle data points (speed & acceleration) lying beyond threshold-bands. These bands were created using mean plus two standard deviations. Furthermore, a higher magnitude of time-varying stochastic volatility of vehicle speeds when they pass through the intersection is associated with higher crash frequencies. These measures can be used to locate intersections with high driving volatilities. A deeper analysis of these intersections can be undertaken, and proactive safety countermeasures considered to enhance safety.

Understanding the Aggregate Effects of Credit Frictions and Uncertainty

This paper integrates a financial accelerator mechanism a la Bernanke et al. (1999) and time-varying uncertainty into a Dynamic New Keynesian model. We examine the extent to which uncertainty and credit conditions interact with one another. The idea is that uncertainty aggravates the information asymmetry between lenders and borrowers, and worsens credit conditions. Already poor credit conditions amplify the effect of shocks (to both the mean and variance) on the aggregate economy. In our model, uncertainty modelled as time-varying stochastic volatility emerges from monetary policy (policy uncertainty), financial risks (micro-uncertainty), and the aggregate state of the economy (macro-uncertainty). Using a third order approximation, we find that micro-uncertainty has first order effects on economic activity through its direct impact on credit conditions. We also find that if credit conditions (as measured by the endogenous risk spread) are already poor, then additional micro-uncertainty shocks have even larger real effects. In turn, shocks to aggregate uncertainty (macro- and policy-uncertainty) have relatively small direct effects on aggregate economic activity.

Local approximate MLE of time-varying stochastic volatility model based on log-likelihood expansion

Local approximate MLE of time-varying stochastic volatility model based on log-likelihood expansion

Do commodity investors herd? Evidence from a time-varying stochastic volatility model

Commodities markets due to their unique characteristics that are they exhibit negative correlation with returns of traditional asset classes and are among the few assets that offer protection from the effects of inflation have recently garnered investors’ attention especially through the development of commodity index financial products. This financialization process that started in the early 2000s and escalated after 2004 has precipitated price comovements among various types of commodities creating a proper setting for the examination of herding behavior. Employing a comprehensive dataset of investable commodities indices we examine the existence of herding behavior via static and time varying models. Our findings reveal no evidence of herding behavior according to static model. However, when rolling window analysis is in place significant anti-herding behavior is detected. These behavioral patterns are corroborated through a time varying stochastic volatility model. Our results contain significant implications for investors, commodities producers and policy makers.

THE GREAT MODERATION OF THE SOUTH AFRICAN BUSINESS CYCLE

ABSTRACT The international financial crisis that started in 2007 and the subsequent end of the long expansion in South Africa has refocused attention on the business cycle. Prior to the crisis, the economies of both developed and developing countries experienced an extended period of low and stable inflation and stable real economic growth, an episode that has been called the “great moderation”. The disruption of this era by the financial crisis has highlighted the importance of understanding the nature and causes of the great moderation, to assist policy makers in facilitating its resumption. This paper considers the historical evidence for the great moderation in South Africa with the aid of a time-varying stochastic volatility model and various break-point tests.

Yield curve factors, term structure volatility, and bond risk premia

We introduce a Nelson-Siegel type interest rate term structure model with the underlying yield factors following autoregressive processes revealing time-varying stochastic volatility. The factor volatilities capture risk inherent to the term struc- ture and are associated with the time-varying uncertainty of the yield curve’s level, slope and curvature. Estimating the model based on U.S. government bond yields applying Markov chain Monte Carlo techniques we find that the yield factors and factor volatilities follow highly persistent processes. Using the extracted factors to explain one-year-ahead bond excess returns we observe that the slope and cur- vature yield factors contain the same explanatory power as the return-forecasting factor recently proposed by Cochrane and Piazzesi (2005). Moreover, we identify slope and curvature risk as important additional determinants of future excess returns. Finally, we illustrate that the yield and volatility factors are closely con- nected to variables reflecting macroeconomic activity, inflation, monetary policy and employment growth. It is shown that the extracted yield curve components have long-term prediction power for macroeconomic fundamentals.

Yield Curve Factors, Factor Volatilities, and the Predictability of Bond Excess Returns

We introduce a Nelson-Siegel type interest rate term structure model with the underlying yield factors following autoregressive processes revealing time-varying stochastic volatility. The factor volatilities capture risk inherent to the term structure and are associated with the time-varying uncertainty of the yield curve's level, slope and curvature. Estimating the model based on U.S. government bond yields applying Markov chain Monte Carlo techniques we find that the yield factors and factor volatilities follow highly persistent processes. Using the extracted factors to explain one-year-ahead bond excess returns we observe that the slope and curvature yield factors contain the same explanatory power as the return-forecasting factor recently proposed by Cochrane and Piazzesi (2005). Moreover, we identify slope and curvature risk as important additional determinants of future excess returns. Finally, we illustrate that the yield and volatility factors are closely connected to variables reflecting macroeconomic activity, inflation, monetary policy and employment growth. It is shown that the extracted yield curve components have long-term prediction power for macroeconomic fundamentals.