New Economics Papers
on Financial Markets
Issue of 2011‒06‒18
two papers chosen by



  1. Financial factor influence on scaling and memory of trading volume in stock market By Wei Li; Fengzhong Wang; Shlomo Havlin; H. Eugene Stanley
  2. Financial Frictions and Credit Spreads By Ke Pang; Pierre L. Siklos

  1. By: Wei Li; Fengzhong Wang; Shlomo Havlin; H. Eugene Stanley
    Abstract: We study the daily trading volume volatility of 17,197 stocks in the U.S. stock markets during the period 1989--2008 and analyze the time return intervals $\tau$ between volume volatilities above a given threshold q. For different thresholds q, the probability density function P_q(\tau) scales with mean interval <\tau> as P_q(\tau)=<\tau>^{-1}f(\tau/<\tau>) and the tails of the scaling function can be well approximated by a power-law f(x)~x^{-\gamma}. We also study the relation between the form of the distribution function P_q(\tau) and several financial factors: stock lifetime, market capitalization, volume, and trading value. We find a systematic tendency of P_q(\tau) associated with these factors, suggesting a multi-scaling feature in the volume return intervals. We analyze the conditional probability P_q(\tau|\tau_0) for $\tau$ following a certain interval \tau_0, and find that P_q(\tau|\tau_0) depends on \tau_0 such that immediately following a short/long return interval a second short/long return interval tends to occur. We also find indications that there is a long-term correlation in the daily volume volatility. We compare our results to those found earlier for price volatility.
    Date: 2011–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1106.1415&r=fmk
  2. By: Ke Pang; Pierre L. Siklos
    Abstract: This paper uses the credit-friction model developed by Curdia and Woodford, in a series of papers, as the basis for attempting to mimic the behavior of credit spreads in moderate as well as crisis times. We are able to generate movements in representative credit spreads that are, at times, both sharp and volatile. We then study the impact of quantitative easing and credit easing. Credit easing is found to reduce spreads, unlike quantitative easing, which has opposite effects. The relative advantage of credit easing becomes even clearer when we allow borrowers to default on their loans. Since increases in default offset the beneficial effects of credit easing on spreads, the policy implication is that, in times of financial stress, the central bank should be aggressive when applying credit easing policies.
    Keywords: Credit easing, credit spread, financial friction, quantitative easing.
    JEL: E43 E44 E51 E58
    Date: 2010–12
    URL: http://d.repec.org/n?u=RePEc:cnb:wpaper:2010/15&r=fmk

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