David Veredas

Temporal Aggregation of Univariate and Multivariate Time Series Models: A Survey

We present a unified and up-to-date overview of temporal aggregation techniques for univariate and multivariate time series models explaining in detail, although intuitively, the technical machinery behind the results. Some empirical applications illustrate the main issues.

The Impact of Macroeconomic News on Quote Adjustments, Noise, and Informational Volatility

We study the impact of the arrival of macroeconomic news on the informational and noise-driven components in high-frequency quote processes and their conditional variances. Bid and ask returns are decomposed into a common (”efficient return”) factor and two market-side-specific components capturing market microstructure effects. The corresponding variance components reflect information-driven and noise-induced volatilities. We find that all volatility components reveal distinct dynamics and are positively influenced by news. The proportion of noise-induced variances is highest before announcements and significantly declines thereafter. Moreover, news-affected responses in all volatility components are influenced by order flow imbalances.

Indirect estimation of elliptical stable distributions

An indirect estimation approach for elliptical stable distributions is presented. The auxiliary model is another elliptical distribution, the multivariate Student-t distribution. It has parameters that have a one-to-one relationship with those of the elliptical stable, making the proposed indirect approach particularly suitable. The finite sample behaviour of the estimators is analyzed using a comprehensive Monte Carlo study. An application to 27 emerging markets stock indexes concludes the paper.

One-Step R-Estimation in Linear Models with Stable Errors

Classical estimation techniques for linear models either are inconsistent, or perform rather poorly, under αstable error densities; most of them are not even rate-optimal. In this paper, we propose an original one-step R-estimation method and investigate its asymptotic performances under stable densities. Contrary to traditional least squares, the proposed R-estimators remain root-n consistent (the optimal rate) under the whole family of stable distributions, irrespective of their asymmetry and tail index. While parametric stable-likelihood estimation, due to the absence of a closed form for stable densities, is quite cumbersome, our method allows us to construct estimators reaching the parametric efficiency bounds associated with any prescribed values (α0, b0) of the tail index α and skewness parameter b, while preserving root-n consistency under any (α, b) as well as under usual light-tailed densities. The method furthermore avoids all forms of multidimensional argmin computation. Simulations confirm its excellent finite-sample performances.

Semiparametric estimation for financial durations

We propose a semiparametric model for the analysis of time series of durations that show autocorrelation and deterministic patterns. Estimation rests on generalized profile likelihood, which allows for joint estimation of the parametric -an ACD type of modeland nonparametric components, providing consistent and asymptotically normal estimators. It is possible to derive the explicit form for the nonparametric estimator, simplifying estimation to a standard maximum likelihood problem.

Surfing Through the GFC: Systemic Risk in Australia

We provide empirical evidence on the degree of systemic risk in Australia before, during and after the Global Financial Crisis. We calculate a daily index of systemic risk from 2004 to 2013 in order to understand how real economy firms influence the outcomes for the rest of the economy. This is done via a mapping of the interconnectedness of the financial and non-financial sectors. The financial sector is in general the home to the most consistently systemically risky firms in the economy. The mining sector becomes occasionally as systemically risky as the financial sector, reflecting the importance of understanding the interrelationships between the financial sector and the real economy in monitoring systemic risks.

Marginal Quantiles for Stationary Processes

We establish the asymptotic normality of marginal sample quantiles for S-mixing vector stationary processes. S-mixing is a recently introduced and widely applicable notion of dependence. Results of some Monte Carlo simulations are given

A Monthly Volatility Index for the US Economy

We estimate the monthly volatility of the US economy from 1968 to 2006 by extending the coincidentindex model of Stock and Watson (1991). Our volatility index, which we call VOLINX, hasfour applications. First, it sheds light on the Great Moderation. VOLINX captures the decrease in thevolatility in the mid-80s as well as the different episodes of stress over the sample period. In the 70sand early 80s the stagflation and the two oil crises marked the pace of the volatility whereas 09/11 is themost relevant shock after the moderation. Second, it helps to understand the economic indicators thatcause volatility. While the main determinant of the coincident index is industrial production, VOLINXis mainly affected by employment and income. Third, it adapts the confidence bands of the forecasts.In and out-of-sample evaluations show that the confidence bands may differ up to 50% with respect to amodel with constant variance. Last, the methodology we use permits us to estimate monthly GDP, whichhas conditional volatility that is partly explained by VOLINX. These applications can be used by policymakers for monitoring and surveillance of the stress of the economy.

The Emergence of Systemically Important Insurers

The increasingly intertwined banking and insurance sectors have lead to calls for stronger regulatory oversight of the insurance industry as potentially systemically risky. Ultimately systemic risk impacts the real economy, and this paper measures the risk via interconnectedness of the banking, insurance and real economy firms in the US for 500 firms from 2003-2011. Systemic risk in the banking sector peaked with the top of the housing cycle in 2006, while in the insurance sector it continued to rise until September 2008. The rescue of AIG and announcement of TARP dramatically decreased this interconnectedness risk. The results clearly demonstrate that whilst banking firms are the most consistently systemically risky in the economy, insurance firms are a readilly identifiable group displaying substantial systemic risk via interconnectedneess with the financial sector and the real economy.

Testing weak exogeneity in the exponential family: an application to financial point processes

In this paper, two tests for weak exogeneity in the econometric modelling of financial point processes are proposed. They are motivated by the common practice in many econometric studies of tick-by-tick data of making inference on the joint density of durations and marks through the conditional (marks given durations) density. However, this inference is only valid if the process of the marginal (durations) is weakly exogenous for the parameters of the conditional density, a hypothesis which is often left untested. Under standard pseudo-maximum likelihood conditions, we first derive a simple parametric score/LM teststatistic when the potential dependence between the parameters of interest in the conditional model and the marginal process is assumed to be linear. Next, an alternative consistent test is proposed when the functional form of the dependence is left unspecified. To illustrate the use of these tests, we analyze two types of financial point processes, linked with market microstructure theory and stealth trading hypothesis, for five stocks traded at NYSE: (i) the relationship between tradesize and trade durations and (ii) the relationship between volume and price durations. In general we reject the null hypothesis of weak exogeneity, therefore questioning some results in the literature which rely on separate estimation of each density.