Temporal Volatility Research Articles

Electoral institutions and growth volatility: Theory and evidence

What accounts for the substantial variation in the temporal volatility of economic growth rates in democratic regimes? We claim that institutional differences between the majoritarian and proportional representation (PR) electoral systems explain why growth volatility is high in some democracies, but not others. Specifically, we suggest that unlike PR democracies, the pronounced career concerns of policymakers in majoritarian systems give them incentives to use their discretionary spending power to alter government spending levels sharply, which generates higher spending volatility in these countries. As a result, policymakers in majoritarian systems cannot credibly commit themselves to stabilizing spending levels. This causes uncertainty among economic actors about future spending levels and leads to unstable investment patterns that generate a higher volatility of growth rates in majoritarian democracies. Results from statistical models provide robust statistical support for our theoretical predictions.

Risk Segmentation of American Homes

Home value risk is among the largest financial risks for American households, and the distribution of home value risk across homeowners has important policy implications. This paper empirically examines spatial variation in home value risk across 99 neighborhoods in metropolitan Denver. Four measurements of housing market performance between 2002:1 and 2007:4 - house price levels, average appreciation rates, temporal volatility in those rates and the sensitivity of neighborhood appreciation rates to the overall metropolitan housing market - are regressed on neighborhood socioeconomic characteristics, including income, wealth, unemployment, poverty, etc. The regressions provide strong evidence that all four housing market performance measurements correlate significantly with neighborhood socioeconomic characteristics. More affluent neighborhoods experienced significantly higher average appreciation rates, lower volatility and less sensitivity. This paper also finds that there is no positive relationship between risk and returns of home values across neighborhoods. In fact, neighborhoods with higher house price levels experienced higher average appreciation rates but lower volatility and less sensitivity. This paper concludes that homeownership is more risky and provides significantly lower financial benefits for low-income households.

Defining the relationship between Plasmodium falciparum parasite rate and clinical disease: statistical models for disease burden estimation.

BackgroundClinical malaria has proven an elusive burden to enumerate. Many cases go undetected by routine disease recording systems. Epidemiologists have, therefore, frequently defaulted to actively measuring malaria in population cohorts through time. Measuring the clinical incidence of malaria longitudinally is labour-intensive and impossible to undertake universally. There is a need, therefore, to define a relationship between clinical incidence and the easier and more commonly measured index of infection prevalence: the "parasite rate". This relationship can help provide an informed basis to define malaria burdens in areas where health statistics are inadequate.MethodsFormal literature searches were conducted for Plasmodium falciparum malaria incidence surveys undertaken prospectively through active case detection at least every 14 days. The data were abstracted, standardized and geo-referenced. Incidence surveys were time-space matched with modelled estimates of infection prevalence derived from a larger database of parasite prevalence surveys and modelling procedures developed for a global malaria endemicity map. Several potential relationships between clinical incidence and infection prevalence were then specified in a non-parametric Gaussian process model with minimal, biologically informed, prior constraints. Bayesian inference was then used to choose between the candidate models.ResultsThe suggested relationships with credible intervals are shown for the Africa and a combined America and Central and South East Asia regions. In both regions clinical incidence increased slowly and smoothly as a function of infection prevalence. In Africa, when infection prevalence exceeded 40%, clinical incidence reached a plateau of 500 cases per thousand of the population per annum. In the combined America and Central and South East Asia regions, this plateau was reached at 250 cases per thousand of the population per annum. A temporal volatility model was also incorporated to facilitate a closer description of the variance in the observed data.ConclusionIt was possible to model a relationship between clinical incidence and P. falciparum infection prevalence but the best-fit models were very noisy reflecting the large variance within the observed opportunistic data sample. This continuous quantification allows for estimates of the clinical burden of P. falciparum of known confidence from wherever an estimate of P. falciparum prevalence is available.

Livestock Pricing and Markets Performance

Livestock markets have a very important effect on pastoralists' welfare. We analyzed thousands of livestock transactions in three sites in Kenya to examine market performance. Key findings include: (1) markets can exacerbate climate risks for pastoralists because livestock prices often decline during dry periods; (2) a high degree of inter-market price variability and temporal volatility occur that can lead to lower producer prices and discourage trader and pastoralist market participation; and (3) quarantines are a significant source of producer price risk because they impede commerce in the rangelands. Such problems of market inefficiency could be dealt with by investing more resources in roads, telecommunications, and security in pastoral areas. Alternative methods of animal disease control should also be considered since quarantines have a disproportionately negative effect on poor pastoral producers compared to those for highlands consumers or ranchers.

Distributed intelligence in an astronomical Distributed Sensor Network

AbstractThe Telescope Alert Operations Network System (TALONS) was designed and developed in the year 2000, around the architectural principles of a distributed sensor network. This network supported the original Rapid Telescopes for Optical Response (RAPTOR) project goals; however, only with further development could TALONS meet the goals of the larger Thinking Telescope Project. The complex objectives of the Thinking Telescope project required a paradigm shift in the software architecture – the centralised intelligence merged into the TALONS network operations could no longer meet all of the new requirements. The intelligence needed to be divorced from the network operations and developed as a series of peripheral intelligent agents, distributing the decision making and analytical processes based on the temporal volatility of the data. This paper is presented as only one part of the poster from the workshop and in it we will explore the details of this architecture and how that merges with the current Thinking Telescope system to meet our project goals. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

The preservational fidelity of evenness in molluscan death assemblages

The richness (number of species) and evenness (uniformity of species abundances) of death assemblages can differ from corresponding living communities due to processes such as between-habitat transport, environmental condensation, and differential taphonomic destruction. Analysis of 132 single-census live-dead comparisons of benthic molluscs from a variety of soft-bottom marine settings indicates thaton averageevenness does not differ greatly between live and dead assemblages, regardless of the particular depositional setting or grain size of associated sediment. However,individualdeath assemblages can deviate quite substantially from their corresponding living assemblages, especially if processed using a fine mesh. In addition, death assemblages collected using sieves with 2 mm mesh or coarser showed consistently and significantly greater evenness than corresponding living assemblages. These results are encouraging for broad-scale assessments of evenness in the fossil record based on the comparison ofaveragevalues (rather than for individual assemblages) and wheretrendsin evenness are the aim of the study.Our live-dead comparisons of richness sample-size corrected by rarefaction revealed that death assemblages were on average ~1.45 times richer than the corresponding living assemblages regardless of rarefied size. In 63.6% of death assemblages both dead richness and dead evenness were greater than live, suggesting sufficient time-averaging to catch significant random or directional changes in the living community and/or introduction of individuals from outside the sampled habitat. In 12.9% of collections both dead richness and dead evenness were less than live, suggesting either rapid loss of dead shells so that dead diversity is depressed below the local living community or selective loss of taphonomically vulnerable taxa. In 18.2% of data sets dead richness was elevated but dead evenness was depressed relative to live: these are interpreted to reflect the addition of low-evenness allochthonous material. The remaining 4.5% of data sets had elevated dead evenness but depressed dead richness, suggesting that live and dead in this case may not be closely related.In seven available time series, temporal volatility in living communities over 6–24 months was considerable but could not account for observed (mostly higher) evenness values in corresponding death assemblages, whose evenness and composition were quite stable in the few examined studies. A densely sampled spatial transect shows that changes in living-assemblage evenness along an environmental gradient were preserved in the corresponding death assemblages, although dead evenness at any location on the gradient was substantially higher than living evenness.

ASSOCIATION BETWEEN INCOME INEQUALITY AND MORTALITY AMONG US STATES: CONSIDERING POPULATION AT RISK

Lynch et al.’s recent analysis of the last 6 US census periods suggests that the relationship between income inequality and mortality is less enduring than was previously thought.1 Statistically significant correlations between state household Gini coefficients and age-adjusted death rates were reported only for the years 1979, 1989, and 1999. The graphs presented by Lynch et al. omit the population at risk. The authors treat the state as the unit of analysis, which ignores the substantial variation in state populations. From the perspective of population exposure, state-based correlations will favor less populous states. Weighting the correlations by state population will correct this bias and provide an alternate perspective. A population-weighted analysis is also reasonable because income inequality is considered to be a risk factor for individual health.2,3 In Figure 1 ▶, state-based correlations between state household Gini coefficients and age-adjusted death rates are compared with population-weighted correlations; the year 2000 is used as the standard.4,5 Each state is represented by a circle, and the size of the circle indicates the size of the state’s population. The filled circles represent the 10 most populous states (in descending order, California, Texas, New York, Florida, Illinois, Pennsylvania, Ohio, Michigan, New Jersey, and Georgia). FIGURE 1— State-based correlations between state household Gini coefficients and age-adjusted death rates compared with population-weighted correlations: United States, 1979 (a), 1989 (b), and 1999 (c). Except for the year 1989, the population-weighted correlations are substantially weaker than the ones reported by Lynch et al. The filled circles in Figure 1 ▶ reveal the reason for this. There appears to be no association between income inequality and mortality in the 10 most populous states in 1989. In 1979 and 1999 the associations appear to be negative, results that conflict with theory.6 Incidentally, the most populous states include some of the largest cities with the highest degrees of income inequality.7 As observed by Lynch et al., the population-based correlations indicate that only the year 1989 was exceptional, showing a strong correlation between income inequality and mortality in US states. The lack of correlation for the year 1999 is certainly unexpected, as US income inequality did not decline in the 1990s.8 The inconsistent results found in the population-based analysis suggest that the association between income inequality and mortality is less pervasive than reported by Lynch et al. The temporal volatility of the population-based correlations probably reflects the work of known confounders such as educational attainment, racial composition, population residence, and household income.9,10 Statistically controlling for such factors might clarify the association between income inequality and mortality in the United States.

Temporal aggregation, volatility components and volume in high frequency UK bond futures

This paper examines volatility in UK Long Gilt and Short Sterling futures over several intra-day frequencies. Initial GARCH model estimates are found to exhibit remaining residual structure and to be inconsistent with theoretical temporal aggregation results for all frequencies other than the full day. Further estimates suggest that intra-day volatility is more adequately characterized by a component model which decomposes volatility into short-run effects which dominate intra-day periods and long-run effects which dominate inter-day horizons, and that such components are associated with the arrival of information flows as proxied by volume. This component volatility model is also able to account for all dependence in Long Gilt futures at frequencies of 15 minutes and lower, and in Short Sterling futures at 1 hour and lower.

The dynamics of exchange rate volatility: A multivariate latent factor ARCH model

AbstractWe study temporal volatility patterns in seven nominal dollar spot exchange rates, all of which display strong evidence of autoregressive conditional heteroskedasticity (ARCH). We first formulate and estimate univariate models, the results of which are subsequently used to guide specification of a multivariate model. The key element of our multivariate approach is exploitation of factor structure, which facilitates tractable estimation via a substantial reduction in the number of parameters to be estimated. Such a latent‐variable model is shown to provide a good description of multivariate exchange rate movements: the ARCH effects capture volatility clustering, and the factor structure captures commonality in volatility movements across exchange rates.

Information reusability, competition and bank asset quality

This paper explains the recent decline m bank asset quality using the notion of information reusability. Banks are viewed as information processors; they exist because of their advantage in extracting the surplus associated with the reusability of borrower-specific information. It is shown that a bank's incentive to screen loan applicants, and hence maintain the quality of its assets, depends on the surplus this screening can produce, which in turn depends on information reusability. Two recent changes in banks' operating environment are increased competitition and greater temporal volatility in borrower credit risks. The former has directly reduced banks' informational surplus while the latter has impaired information reusability. Hence screening expenditures have been reduced and the diminution of screening has lowered the quality of bank assets. It is also shown that an increase in deposit insurance premia has an effect similar to that of narrowing interest spreads and therefore will result in reduced asset screening and impaired asset quality.