118 research outputs found
Variety and Volatility in Financial Markets
We study the price dynamics of stocks traded in a financial market by
considering the statistical properties both of a single time series and of an
ensemble of stocks traded simultaneously. We use the stocks traded in the
New York Stock Exchange to form a statistical ensemble of daily stock returns.
For each trading day of our database, we study the ensemble return
distribution. We find that a typical ensemble return distribution exists in
most of the trading days with the exception of crash and rally days and of the
days subsequent to these extreme events. We analyze each ensemble return
distribution by extracting its first two central moments. We observe that these
moments are fluctuating in time and are stochastic processes themselves. We
characterize the statistical properties of ensemble return distribution central
moments by investigating their probability density functions and temporal
correlation properties. In general, time-averaged and portfolio-averaged price
returns have different statistical properties. We infer from these differences
information about the relative strength of correlation between stocks and
between different trading days. Lastly, we compare our empirical results with
those predicted by the single-index model and we conclude that this simple
model is unable to explain the statistical properties of the second moment of
the ensemble return distribution.Comment: 10 pages, 11 figure
Common Scaling Patterns in Intertrade Times of U. S. Stocks
We analyze the sequence of time intervals between consecutive stock trades of
thirty companies representing eight sectors of the U. S. economy over a period
of four years. For all companies we find that: (i) the probability density
function of intertrade times may be fit by a Weibull distribution; (ii) when
appropriately rescaled the probability densities of all companies collapse onto
a single curve implying a universal functional form; (iii) the intertrade times
exhibit power-law correlated behavior within a trading day and a consistently
greater degree of correlation over larger time scales, in agreement with the
correlation behavior of the absolute price returns for the corresponding
company, and (iv) the magnitude series of intertrade time increments is
characterized by long-range power-law correlations suggesting the presence of
nonlinear features in the trading dynamics, while the sign series is
anti-correlated at small scales. Our results suggest that independent of
industry sector, market capitalization and average level of trading activity,
the series of intertrade times exhibit possibly universal scaling patterns,
which may relate to a common mechanism underlying the trading dynamics of
diverse companies. Further, our observation of long-range power-law
correlations and a parallel with the crossover in the scaling of absolute price
returns for each individual stock, support the hypothesis that the dynamics of
transaction times may play a role in the process of price formation.Comment: 8 pages, 5 figures. Presented at The Second Nikkei Econophysics
Workshop, Tokyo, 11-14 Nov. 2002. A subset appears in "The Application of
Econophysics: Proceedings of the Second Nikkei Econophysics Symposium",
editor H. Takayasu (Springer-Verlag, Tokyo, 2003) pp.51-57. Submitted to
Phys. Rev. E on 25 June 200
Self-organized model of cascade spreading
We study simultaneous price drops of real stocks and show that for high drop
thresholds they follow a power-law distribution. To reproduce these collective
downturns, we propose a minimal self-organized model of cascade spreading based
on a probabilistic response of the system elements to stress conditions. This
model is solvable using the theory of branching processes and the mean-field
approximation. For a wide range of parameters, the system is in a critical
state and displays a power-law cascade-size distribution similar to the
empirically observed one. We further generalize the model to reproduce
volatility clustering and other observed properties of real stocks.Comment: 8 pages, 6 figure
Emissões públicas de ações, volatilidade e insider information na Bovespa
O trabalho utiliza um estudo de evento para
examinar os retornos de ações relacionados a emissões públicas por empresas brasileiras listadas na BOVESPA, realizadas entre 1992 e 2002, buscando
determinar como o mercado reagiu antes, durante e
depois da data do anúncio da emissão. Após utilizar a metodologia convencional de mensuração de retornos
anormais por OLS, foram utilizados modelos
ARCH e GARCH, que levam em consideração a heteroscedasticidade
condicional da volatilidade dos
retornos anormais, em mais de 70% da amostra,
após a constatação da presença desses processos
nos resíduos originais. Os resultados mostram que
1) há evidências de insider information antes da data do anúncio, (2) que ocorrem retornos anormais
negativos na data do anúncio e (3) que, no período
de um ano após as emissões, as ações das empresas
que captaram recursos via underwriting tiveram
retornos negativos após ajuste ao risco e ao mercado
Stock price reaction to profit warnings: The role of time-varying betas
This study investigates the role of time-varying betas, event-induced variance and conditional heteroskedasticity in the estimation of abnormal returns around important news announcements. Our analysis is based on the stock price reaction to profit warnings issued by a sample of firms listed on the Hong Kong Stock Exchange. The standard event study methodology indicates the presence of price reversal patterns following both positive and negative warnings. However, incorporating time-varying betas, event-induced variance and conditional heteroskedasticity in the modelling process results in post-negative-warning price patterns that are consistent with the predictions of the efficient market hypothesis. These adjustments also cause the statistical significance of some post-positive-warning cumulative abnormal returns to disappear and their magnitude to drop to an extent that minor transaction costs would eliminate the profitability of the contrarian strategy
Robust term structure estimation in developed and emerging markets
Despite powerful advances in interest rate curve modeling for data-rich countries in the last 30 years, comparatively little attention has been paid to the key practical problem of estimation of the term structure of interest rates for emerging markets. This may be partly due to limited data availability. However, emerging bond markets are becoming increasingly important and liquid. It is, therefore, important to be understand whether conclusions drawn from developed countries carry over to emerging markets. We estimate model parameters of fully flexible Nelson–Siegel–Svensson term structures model which has become one of the most popular term structure model among academics, practitioners, and central bankers. We investigate four sets of bond data: U.S. Treasuries, and three major emerging market government bond data-sets (Brazil, Mexico and Turkey). By including both the very dense U.S. data and the comparatively sparse emerging market data, we ensure that are results are not specific to a particular data-set. We find that gradient and direct search methods perform poorly in estimating term structures of interest rates, while global optimization methods, particularly the hybrid particle swarm optimization introduced in this paper, do well. Our results are consistent across four countries, both in- and out-of-sample, and for perturbations in prices and starting values. For academics and practitioners interested in optimization methods, this study provides clear evidence of the practical importance of choice of optimization method and validates a method that works well for the NSS model
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