Search

I analyze the price discovery, liquidity provision, and transaction-cost components driven by the real-time firm-specific news at the Paris Bourse. I find that traders actively monitor and promptly react to the real-time information flow. The news impact depends on which type of news bulletin is released. Only earnings announcements and news items causing extreme price disruptions enlarge spreads and information asymmetry risk. In contrast, the greater part of real-time firm-specific news releases is a magnet for liquidity and trading. This research provides insights into the market quality of limit-order book markets that are able to provide additional liquidity during information events.

This paper proposes a new approach to identifying the effects of monetary policy shocks in an international vector autoregression. Using high-frequency data on the prices of eurodollar contracts, we measure the impact of the surprise component of the FOMC-day Federal Reserve policy decision on financial variables, such as the exchange rate and the foreign interest rate. We show how this information can be used to achieve identification without having to make the usual strong assumption of a recursive ordering.

This study addresses two questions: where does price discovery occur for internationally-traded firms and how do international stock prices adjust to an exchange rate shock ? These questions are answered by analyzing quotes originating in New York and Frankfurt for three large German firms, DaimlerChrysler, Deutsche Telekom, and SAP, during overlapping trading hours. A high-frequency sample of quotes from both locations along with the dollar/euro exchange rate yields evidence of one cointegrating relation among the three variables. Vector error correction models are estimated for each firm and the associated vector moving average representations are utilized to infer the share of price discovery coming from the exchange rate, New York, and Frankfurt quotes. The evidence suggests a structure of the international equity market that has the home-market largely determining the random walk component of the international value of a firm along with an independent role for exchange rate shocks to affect prices in the U.S. markets. However, there is a significant information share for New York in the case of DaimlerChrysler and an even bigger role for New York with respect to SAP. Following a shock to the exchange rate, we find that almost all of the adjustment comes through the New York price.

Nielsen (1999) showed the surprising fact that a nonparametric one-dimensional hazard as a function of time can be estimated-consistently if a high quality marker is observed. In this paper we show that the hazard relevant for predicting remaining duration time, given the current status of a high quality marker, can be estimated-consistently if a Markov type property holds for the high quality marker.

We use high frequency data for the mark–dollar exchange rate for the period 1992–1995 to evaluate the effects of central bank interventions on the foreign exchange market. We estimate an unobserved component model that decomposes volatility into non-stationary and stationary parts. Stationary components in turn are decomposed into seasonal and non-seasonal intra-day parts. Our results confirm the view that interventions are not particularly effective. The exchange rate moves in the desired direction for only about 50% of the time, and often with a substantial increase in volatility. The model suggests that the two components, which are affected the most by interventions, are the permanent and the stochastic intra-day.

This paper uses Reuters exchange rate data to investigate thecontributions to the price discovery process by individual banks in theforeign exchange market. We propose multivariate time series models aswell as models in tick time to study the dynamic relations between thequotes of individual banks. We investigate the hypothesis that Germanbanks are price leaders in the deutschmark/dollar market. Our empiricalresults suggest an important but not exclusive role for German banks inthe price discovery process. There is also a group of banks, German andnon-German, that lags behind the market and does not contribute to theprice discovery process. In contrast to Peiers~(1997) we do not findevidence for stronger price leadership of Deutsche bank on days withsuspected Bundesbank interventions in the foreign exchange market.

A new model for the analysis of durations, the stochastic conditional duration (SCD) model, is introduced. This model is based of the assumption that the durations are generated by a latent stochastic factor that follows a first order autoregressive process. The latent factor is pertubed multiplicatively by an innovation distributed as aWeibull or gamma variable. The model can capture a wide range of shapes of hazard functions. The estimation of the parameters is performed by quasi-maximum likelihood, after transforming the original nonlinear model into a space state representation and using the Kalman filter. The model is applied to stock market price-durations, looking at the relation between price durations, volume, spread and trading intensity.

This paper uses Reuters exchange rate data to investigate thecontributions to the price discovery process by individual banks in theforeign exchange market. We propose multivariate time series models aswell as models in tick time to study the dynamic relations between thequotes of individual banks. We investigate the hypothesis that Germanbanks are price leaders in the deutschmark/dollar market. Our empiricalresults suggest an important but not exclusive role for German banks inthe price discovery process. There is also a group of banks, German andnon-German, that lags behind the market and does not contribute to theprice discovery process. In contrast to Peiers~(1997) we do not findevidence for stronger price leadership of Deutsche bank on days withsuspected Bundesbank interventions in the foreign exchange market.

International audience; This paper presents a study of intra-day patterns of stock market activity and introduces duration based activity measures for single stocks and multiple assets. The proposed measures involve weighted durations, i.e. times necessary to sell (buy) a predetermined volume or value of stocks. As such, they capture dependencies between intra-trade durations, transaction volumes and prices, and can be interpreted as liquidity measures. This approach allows us to highlight the intra-day variations of liquidity, its costs and volatility, and to develop a liquidity based asset ordering. The extension to a multivariate analysis yields new insights into the dynamics of portfolio liquidity by revealing various aspects of asset substitution, including the e!ects of correlated trade intensities of portfolio components. Several examples are used to show that in practice, the proposed liquidity measures become e$cient instruments for strategic block trading and optimal portfolio adjustments. The paper also contains an empirical study of asset activity on the Paris Bourse. We examine the liquidity dynamics throughout the day and reveal the existence of periodic patterns resulting from world-wide interactions of major stock markets. In the multivariate setup, we report evidence on common patterns and correlations of trade intensities of selected stocks.

This paper proposes a new approach to modeling financial transactions data. A new model for discrete valued time series is proposed in the context of generalized linear models. Since the model is specified conditional on both the previous state, as well as the historic distribution, we call the model the Autoregressive Conditional Multinomial (ACM) model. When the data are viewed as a marked point process, the ACD model proposed in Engle and Russell (1998) allows for joint modeling of the price transition probabilities and the arrival times of the transactions. In this marked point process context, the transition probabilities vary continuously through time and are therefore duration dependent. Finally, variations of the model allow for volume and spreads to impact the conditional distribution of price changes. Impulse response studies show the long run price impact of a transaction can be very sensitive to volume but is less sensitive to the spread and transaction rate.

This paper systematically studies the use of mixed-frequency data sets and suggests that the use of high frequency data in forecasting economic aggregates can improve forecast accuracy. The best way of using this information is to build a single model, for example, an ARMA model with missing observations, that relates data of all frequencies. The implementation of such an approach, however, poses serious practical problems in all but the simplest cases. As a feasible and consistent alternative, we propose a two-stage procedure to obtain pseudo high frequency data and to subsequently use these artificial values as proxies for macroeconomic or financial models. This alternative method yields a sub-optimal forecast in general but avoids the computational problems of a full-blown single model. Our approach differs from classical interpolation since we only use past and current information to get the pseudo series. A proxy, which is constructed by classical interpolation, may fit very well in sample, but it is not useful for out-of-sample forecasts. As applications of linking series generated at different frequencies, we show that the use of monthly proxies of GDP improves the predictability of absolute stock returns and the unemployment rate compared to the use of industrial production as an alternative proxy.

The conditionally exponential decay (CED) model is used to explain the scaling laws observed in nancial data. This approach enables us to identify the distributions of currency exchange rate or economic indices returns (changes) corresponding to the empirical scaling laws. This is illustrated for daily returns of the Dow Jones industrial average (DJIA) and the Standard & Poor’s 500 (S&P500) indices as well as for high-frequency returns of the USD=DEM exchange rate. c 1999 Elsevier Science B.V. All rights reserved.

In this paper, we are dealing with financial high frequency data; any time an order reaches the market, any time a cancellation or transaction occurs, a new record is made, ending up with a huge amount of data. Hence the time interval between two events is not fixed, forbidding the use of standard statistical tools. In the recent literature, several authors proposed time deformation techniques to deal with this problem. The practical importance of time deformation is to give a preprocessing technique to obtain a regularly spaced grid of data. The main contribution of this paper is to survey most of the time deformations proposed in the literature in a general setting and to compare them from both a statistical and financial point of view. We provide a new trading strategy in which the time to invest is endogeneous. Moreover, we highlight the fact that changing time scale can improve the daily gain following such a strategy.

This paper proposes a class of asymmetric Autoregressive Conditional Duration models, which extends the ACD model of Engle and Russell (1997). The asymmetry consists of letting the duration process depend on the state of the price process in the beginning and at the end of each duration. If the price has increased, the parameters of the ACD can differ from what they are if the price has decreased. Thus, the model is also a transition model for the price process, with durations following an ACD process. The logarithmic version of the model is applied to the bid/ask price revision process by the specialist for the IBM stock on the New York Stock Exchange. The empirical evidence in favour of asymmetry is compelling.

In the classical ARCH model of Engle [1982] the conditional variance is a linear function of lagged squared residuals. In this paper I introduce nonlinearity, by adding a term that consists of a constant parameter multiplied by a transition function. Two different transition functions are considered, a logistic and an exponential. Furthermore, following Bollerslev [1986], I extend the model by introducing lagged conditional variances in the conditional variance equation. This specification reduces the number of parameters in the model, which proves to be important for successful estimation. The paper also describes a number of specification tests, that can determine if the smooth transition GARCH model can be the data generating process of a times series. The techniques proposed are illustrated on data from four stock index series.

We consider the following hidden Markov chain problem: estimate the finite-dimensional parameter θ in the equation v t =v 0 + ∫ 0 t σ(θ, v s ) d W s + drift , when we observe discrete data Xi/n at times i=0,…,n from the diffusion X t =x 0 + ∫ 0 t v s d B s + drift . The processes (Wt)t∈[0,1] and (Bt)t∈[0,1] are two independent Brownian motions; asymptotics are taken as n→∞. This stochastic volatility model has been paid some attention lately, especially in financial mathematics. We prove in this note that the unusual rate n−1/4 is a lower bound for estimating θ. This rate is indeed optimal, since Gloter (CR Acad. Sci. Paris, t330, Serie I, pp. 243–248), exhibited n−1/4 consistent estimators. This result shows in particular the significant difference between “high frequency data” and the ergodic framework in stochastic volatility models (compare Genon-Catalot, Jeantheau and Laredo (Bernoulli 4 (1998) 283; Bernoulli 5 (2000) 855; Bernoulli 6 (2000) 1051 and also Sorensen (Prediction-based estimating functions. Technical report, Department of Theoretical Statistics, University of Copenhagen, 1998)).

We consider a multidimensional diffusion X with drift coefficient b ( X t , α ) and diffusion coefficient e a ( X t , β ) where α and β are two unknown parameters, while e is known. For a high frequency sample of observations of the diffusion at the time points k / n , k = 1 , … , n , we propose a class of contrast functions and thus obtain estimators of ( α , β ) . The estimators are shown to be consistent and asymptotically normal when n → ∞ and e → 0 in such a way that e − 1 n − ρ remains bounded for some ρ > 0 . The main focus is on the construction of explicit contrast functions, but it is noted that the theory covers quadratic martingale estimating functions as a special case. In a simulation study we consider the finite sample behaviour and the applicability to a financial model of an estimator obtained from a simple explicit contrast function.

This paper introduces adaptiveness to the non-parametric estimation of volatility in high frequency data. We consider general continuous Ito processes contaminated by microstructure noise. In the context of pre-averaging, we show that this device gives rise to estimators that are within 7% of the commonly conjectured “quasi-lower bound” for asymptotic efficiency. The asymptotic variance is of the form constant × bound, where the constant does not depend on the process to be estimated. The results hold with mild assumptions on the noise, and extend to mildly irregular observations.

We test whether exchange rate trading is profitable in the emerging markets of Brazil, China, India, and South Africa. Using momentum trading strategies applied to high frequency data, we discover that: (a) momentum-based trading strategies lead to statistically significant profits from the currencies of all four emerging markets; (b) the South African Rand is generally the most profitable, followed by the Brazilian Real and the Indian Rupee; (c) profits are persistent during the day and increase substantially from 1-minute trade to 120-minute trade; and (d) during the period of the global financial crisis currency profits were maximised.

We provide the first intraday analysis on the contribution to price discovery of two emissions carbon credits: European Union Allowances (EUAs) and Certified Emission Reductions (CERs). We find that EUAs lead price discovery but CERs play a growing role and, therefore, should not be ignored.

The parent site consists of: two main research sites (Lake Santo Parmense and Lake Scuro Parmense) in the Northern Apennines, but also several temporary and permanent lentic systems sampled at a lower frequency; two main research sites in the Western Alps (Lake Paione Inferiore and Superiore), and about 30 high altitude lakes (above 1900 m a.s.l.) in the same area, with long-term irregular data; one research site located in the Brenta Dolomites (Lake Tovel) at 1178 m a.s.l. with a monthly sampling regime and long-term data; three main research sites in the province of Bozen, together with other 11 lakes all located below the treeline, monitored since 1979 with samplings at irregular intervals. For all the main research sites long-term data exist on water chemistry, plankton and benthos; however, the datasets of the various sites may be different in terms of size, temporal coverage, frequency of the series and monitored parameters. The main research topics considered are eutrophication and acidification, population dynamics, biodiversity, functional diversity, lake metabolism, plankton phenology, alien species, climate change effects, impacts of anthropogenic activities in lake catchments, high frequency monitoring.

The main goal of this paper is to present the method for describing and predicting trade intensity on the Warsaw Stock Exchange. The approach is based on generalized linear models, the variable selection is performed using shrinkage methods such as the Lasso or Ridge regression. The variable under investigation is the number of trades of a particular stock 5-minute interval. The main conclusion is that the number of trades during short intervals is predictable in the sense that the prediction, even based on relatively simple models, is with respect to statistical properties better than the prediction based on the random walk, which is used as a benchmark model.

The parent site consists of: two main research sites (Lake Santo Parmense and Lake Scuro Parmense) in the Northern Apennines, but also several temporary and permanent lentic systems sampled at a lower frequency; two main research sites in the Western Alps (Lake Paione Inferiore and Superiore), and about 30 high altitude lakes (above 1900 m a.s.l.) in the same area, with long-term irregular data; one research site located in the Brenta Dolomites (Lake Tovel) at 1178 m a.s.l. with a monthly sampling regime and long-term data; three main research sites in the province of Bozen, together with other 11 lakes all located below the treeline, monitored since 1979 with samplings at irregular intervals. For all the main research sites long-term data exist on water chemistry, plankton and benthos; however, the datasets of the various sites may be different in terms of size, temporal coverage, frequency of the series and monitored parameters. The main research topics considered are eutrophication and acidification, population dynamics, biodiversity, functional diversity, lake metabolism, plankton phenology, alien species, climate change effects, impacts of anthropogenic activities in lake catchments, high frequency monitoring.