New Economics Papers
on Financial Markets
Issue of 2014‒05‒17
four papers chosen by

  1. The Common Factor in Idiosyncratic Volatility: Quantitative Asset Pricing Implications By Bernard Herskovic; Bryan T. Kelly; Hanno Lustig; Stijn Van Nieuwerburgh
  2. A Multi-factor Adaptive Statistical Arbitrage Model By Wenbin Zhang; Zhen Dai; Bindu Pan; Milan Djabirov
  3. Gaussian-Chain Filters for Heavy-Tailed Noise with Application to Detecting Big Buyers and Big Sellers in Stock Market By Li-Xin Wang
  4. Stock return comovements and integration within the Latin American integrated market By Carlos Castro; Nini Johana Marin

  1. By: Bernard Herskovic; Bryan T. Kelly; Hanno Lustig; Stijn Van Nieuwerburgh
    Abstract: We show that firms’ idiosyncratic volatility obeys a strong factor structure and that shocks to the common factor in idiosyncratic volatility (CIV) are priced. Stocks in the lowest CIV-beta quintile earn average returns 6.4% per year higher than those in the highest quintile. We provide evidence that the CIV factor is correlated with income risk faced by households. These three facts are consistent with a canonical incomplete markets heterogeneous-agent model. In the model, CIV is a priced state variable because an increase in idiosyncratic firm volatility raises the typical investor’s marginal utility when markets are incomplete. The calibrated model matches the high degree of comovement in idiosyncratic volatilities, the CIV-beta return spread, and several other asset price moments.
    JEL: E44 G12
    Date: 2014–04
  2. By: Wenbin Zhang; Zhen Dai; Bindu Pan; Milan Djabirov
    Abstract: This paper examines the implementation of a statistical arbitrage trading strategy based on co-integration relationships where we discover candidate portfolios using multiple factors rather than just price data. The portfolio selection methodologies include K-means clustering, graphical lasso and a combination of the two. Our results show that clustering appears to yield better candidate portfolios on average than naively using graphical lasso over the entire equity pool. A hybrid approach of using the combination of graphical lasso and clustering yields better results still. We also examine the effects of an adaptive approach during the trading period, by re-computing potential portfolios once to account for change in relationships with passage of time. However, the adaptive approach does not produce better results than the one without re-learning. Our results managed to pass the test for the presence of statistical arbitrage test at a statistically significant level. Additionally we were able to validate our findings over a separate dataset for formation and trading periods.
    Date: 2014–05
  3. By: Li-Xin Wang
    Abstract: We propose a new heavy-tailed distribution --- Gaussian-Chain (GC) distribution, which is inspirited by the hierarchical structures prevailing in social organizations. We determine the mean, variance and kurtosis of the Gaussian-Chain distribution to show its heavy-tailed property, and compute the tail distribution table to give specific numbers showing how heavy is the heavy-tails. To filter out the heavy-tailed noise, we construct two filters --- 2nd and 3rd-order GC filters --- based on the maximum likelihood principle. Simulation results show that the GC filters perform much better than the benchmark least-squares algorithm when the noise is heavy-tail distributed. Using the GC filters, we propose a trading strategy, named Ride-the-Mood, to follow the mood of the market by detecting the actions of the big buyers and the big sellers in the market based on the noisy, heavy-tailed price data. Application of the Ride-the-Mood strategy to five blue-chip Hong Kong stocks over the recent two-year period from April 2, 2012 to March 31, 2014 shows that their returns are higher than the returns of the benchmark Buy-and-Hold strategy and the Hang Seng Index Fund.
    Date: 2014–05
  4. By: Carlos Castro; Nini Johana Marin
    Abstract: Abstract: Financial integration has been pursued aggressively across the globe in the last fifty years; however, there is no conclusive evidence on the diversification gains (or losses) of such efforts. These gains (or losses) are related to the degree of comovements and synchronization among increasingly integrated global markets. We quantify the degree of comovements within the integrated Latin American market (MILA). We use dynamic correlation models to quantify comovements across securities as well as a direct integration measure. Our results show an increase in comovements when we look at the country indexes, however, the increase in the trend of correlation is previous to the institutional efforts to establish an integrated market in the region. On the other hand, when we look at sector indexes and an integration measure, we find a decreased in comovements among a representative sample of securities form the integrated market.
    Keywords: Comovements, correlation, market integration
    Date: 2014–04–01

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.