Indirect estimation of elliptical stable distributions

We present an indirect estimation approach for elliptical stable distributions which relies on the use of a multivariate t distribution as auxiliary model. This distribution is also elliptical and we show that its parameters have a one-to-one relationship with those of the elliptical stable, therefore making the proposed indirect approach especially suitable.Standard asymptotic properties are also shown and we analyze the finite sample behavior of the estimators via a comprehensive Monte Carlo study. An application to 27 emerging markets stock indexes concludes the paper.stable, elliptical, high dimension, multivariate, indirect inference

Macro Surprises And Short-Term Behaviour In Bond Futures

This paper analyses how the macro news affect the future price of the ten year Treasure bond future (TY), one of the most important US bonds. We consider different fundamentals and we analyze the effect of their forecasting errors conditionally on their sign and the momentum of the business cycle. To obtain a smooth effect of the news arrival we consider a Polynomial Distributed Lag (PDL) model. We conclude that i)fundamentals affect TY for some hours, ii)their effect depends on the sign of the forecast error and iii) it depends on the business cycle. Finally the timeliness of the releases matters. Cet article discute de l'effet des nouvelles macroéconomiques sur le prix futur des bons du Trésor ayant échéance dans 10 ans, l une des classes d'obligations les plus importantes. On prendra en considération divers facteurs fondamentaux et on analysera l'effet de leurs erreurs de prédiction conditionnellement au signe et au momentum du cycle économique. Pour obenir un effet lisse sur l'arrivée des nouvelles, on prendra un modèle à retard polynomial distribué (PDL) On conclura que i) les facteurs fondamentaux affectent les rendements des obligations du Trésor pendant quelques heures, ii) leurs effets dépendent du signe de l'erreur de prédiction et iii)ils dépendent aussi du cycle économique. Finalement, le ``timing'' de l'arrivée de nouvelles macroéconomiques est important.US bonds, PDL model, business cycle, macro announcements, bons US, modèle PDL, cycle économique, annonce macroéconomique

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 how these techniques are employed. Some empirical applications illustrate the main issues.Temporal aggregation, ARIMA, Seasonality, GARCH, Vector ARMA, Spurious causality, Multivariate GARCH

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.efficient return, macroeconomic announcements, microstructure noise, informational volatility

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. --effcient return,macroeconomic announcements,microstructure noise,informational volatility

ON THE (INTRADAILY) SEASONALITY AND DYNAMICS OF A FINANCIAL POINT PROCESS: A SEMIPARAMETRIC APPROACH.

A component model for the analysis of financial durations is proposed. The components are the long-run dynamics and the seasonality. The later is left unspecified and the former is assumed to fall within the class of certain family of parametric functions. The joint model is estimated by maximizing a (local) quasi-likelihood function, and the resulting nonparametric estimator of the seasonal curve has an explicit form that turns out to be a transformation of the Nadaraya-Watson estimator. The estimators of the parameters of interest are shown to be root-N consistent and asymptotically efficient. Furthermore, the seasonal curve is also estimated consistently. The methodology is applied to the trade duration process of Bankinter, a medium size Spanish bank traded in Bolsa de Madrid. We show that adjusting data by seasonality produces important misspecifications.

What pieces of limit order book information do are informative? An empirical analysis of a pure order-driven market

This paper studies the importance of different pieces of limit order book information in characterizing order aggressiveness and the timing of trades, order submissions and cancellations. Using limit order book information on liquid and frequently traded Spanish stock, we evidence that most of the explanatory power of the book concentrates on the best quotes. However, the book beyond the best quotes also matters in explaining the aggressiveness of traders. Liquidity providers benefit more from an increased degree of pre-trade transparency than liquidity consumers. Finally, no piece of book information matters in explaining the timing of orders

What Pieces of Limit Order Book Information Matter in Explaining Order Choice by Patient and Impatient Traders?

In this paper, we extend the existing empirical evidence on the relationship between the state of the limit order book (LOB) and order choice. Our contribution is twofold: first, we propose a sequential ordered probit (SOP) model which allows studying patient and impatient traders’ choices separately; second, we consider two pieces of LOB information, the best quotes and the book beyond the best quotes. We find that both pieces of LOB information explain the degree of patience of an incoming trader and, afterwards, its order choice. Nonetheless, the best quotes concentrate most of the explanatory power of the LOB. The shape of the book beyond the best quotes is crucial in explaining the aggressiveness of patient (limit order) traders, while impatient (market order) traders base their decisions primarily on the best quotes. Patient traders’ choices depend more on the state of the LOB on the same side of the market, while impatient traders mostly look at the state of the LOB on the opposite side. The aggressiveness of both types of traders augments with the inside spread. However, patient (impatient) traders submit more (less) aggressive limit (market) orders when the depth of the own (opposite) best quote and the length of the own (opposite) side of the book increase. We also find that higher depth away from the best ask (bid) quote may signal that this quote is “too low (high)”, causing incoming impatient buyers (sellers) to be more aggressive and incoming patient sellers (buyers) to be more conservative

Disentangled jump-robust realized covariances and correlations with non-synchronous prices

We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyse – in a through Monte Carlo study – different combinations of quantile-and-median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes and in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that pre-averaged disentangled estimators provide a precise, computationally efficient and easy alternative to measure integrated covariances on basis of noisy and asynchronous prices. Moreover, the gain is not only statistical but also financial. A minimum variance portfolio application shows the superiority of the disentangled realized estimators in terms of numerous performance metrics

A model for vast panels of volatilities

Realized volatilities, when observed over time, share the following stylised facts: comovements, clustering, long-memory, dynamic volatility, skewness and heavy-tails. We propose a dynamic factor model that captures these stylised facts and that can be applied to vast panels of volatilities as it does not suffer from the curse of dimensionality. It is an enhanced version of Bai and Ng (2004) in the following respects: i) we allow for longmemory in both the idiosyncratic and the common components, ii) the common shocks are conditionally heteroskedastic, and iii) the idiosyncratic and common shocks are skewed and heavy-tailed. Estimation of the factors, the idiosyncratic components and the parameters is simple: principal components and low dimension maximum likelihood estimations. A Monte Carlo study shows the usefulness of the approach and an application to 90 daily realized volatilities, pertaining to S&P100, from January 2001 to December 2008, evinces, among others, the following fi ndings: i) All the volatilities have long-memory, more than half in the nonstationary range, that increases during fi nancial turmoils. ii) Tests and criteria point towards one dynamic common factor driving the co-movements. iii) The factor has larger long-memory than the assets volatilities, suggesting that long–memory is a market characteristic. iv) The volatility of the realized volatility is not constant and common to all. v) A forecasting horse race against 8 competing models shows that our model outperforms, in particular in periods of stressCuando se observan a través del tiempo, las volatilidades realizadas comparten una serie de características comunes: comovimiento, comportamiento de racimo (clustering), memoria larga, volatilidad de la volatilidad dinámica, asimetría y colas gruesas. En este artículo proponemos un modelo dinámico factorial que captura estas características y que puede ser aplicado a paneles de volatilidad de grandes dimensiones dado que no sufre la maldición de la dimensionalidad. El modelo es una adaptación del de Bai y Ng (2004) en los siguientes aspectos: i) permitimos memoria larga en los componentes comunes e idiosincráticos, ii) las sacudidas (shocks) comunes son condicionalmente heterocedásticas, y iii) las sacudidas comunes e idiosincráticas son asimétricas y con colas gruesas. La estimación de los factores, los componentes idiosincráticos y los parámetros es simple: componentes principales y estimaciones de máxima verosimilitud de baja dimensión. Un profundo estudio de Monte Carlo muestra la utilidad de la estrategia de estimación y una aplicación a un panel de 90 volatilidades realizadas correspondiente a compañías pertenecientes al índice S&P100, desde enero de 2001 a diciembre de 2008, muestra, entre otros resultados, que i) todas las volatilidades tienen memoria larga, más de la mitad en el rango no estacionario, que se incrementa durante períodos de estrésii) contrastes y criterios indican la presencia de un factor común dinámicoiii) el factor tiene memoria más larga que las volatilidades de las compañías, lo que sugiere que la memoria larga es una característica del mercadoiv) la volatilidad de la volatilidad realizada es dinámica y común para todas las compañíasv) una comparación entre 8 modelos en términos de predicción muestra que nuestro modelo es superior, sobre todo en períodos de estré