nep-ets New Economics Papers
on Econometric Time Series
Issue of 2011‒03‒12
two papers chosen by
Yong Yin
SUNY at Buffalo

  1. Record statistics for biased random walks, with an application to financial data By Gregor Wergen; Miro Bogner; Joachim Krug
  2. Testing for change in mean of heteroskedastic time series By Mohamed Boutahar

  1. By: Gregor Wergen; Miro Bogner; Joachim Krug
    Abstract: We consider the occurrence of record-breaking events in random walks with asymmetric jump distributions. The statistics of records in symmetric random walks was previously analyzed by Majumdar and Ziff and is well understood. Unlike the case of symmetric jump distributions, in the asymmetric case the statistics of records depends on the choice of the jump distribution. We compute the record rate $P_n(c)$, defined as the probability for the $n$th value to be larger than all previous values, for a Gaussian jump distribution with standard deviation $\sigma$ that is shifted by a constant drift $c$. For small drift, in the sense of $c/\sigma \ll n^{-1/2}$, the correction to $P_n(c)$ grows proportional to arctan$(\sqrt{n})$ and saturates at the value $\frac{c}{\sqrt{2} \sigma}$. For large $n$ the record rate approaches a constant, which is approximately given by $1-(\sigma/\sqrt{2\pi}c)\textrm{exp}(-c^2/2\sigma^2)$ for $c/\sigma \gg 1$. These asymptotic results carry over to other continuous jump distributions with finite variance. As an application, we compare our analytical results to the record statistics of 366 daily stock prices from the Standard & Poors 500 index. The biased random walk accounts quantitatively for the increase in the number of upper records due to the overall trend in the stock prices, and after detrending the number of upper records is in good agreement with the symmetric random walk. However the number of lower records in the detrended data is significantly reduced by a mechanism that remains to be identified.
    Date: 2011–03
  2. By: Mohamed Boutahar (GREQAM)
    Abstract: In this paper we consider a Lagrange Multiplier-type test (LM) to detect change in the mean of time series with heteroskedasticity of unknown form. We derive the limiting distribution under the null, and prove the consistency of the test against the alternative of either an abrupt or smooth changes in the mean. We perform also some Monte Carlo simulations to analyze the size distortion and the power of the proposed test. We conclude that for moderate sample size, the test has a good performance. We finally carry out an empirical application using the daily closing level of the S&P 500 stock index, in order to illustrate the usefulness of the proposed test.
    Date: 2011–02

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