nep-mst New Economics Papers
on Market Microstructure
Issue of 2019‒03‒18
two papers chosen by
Thanos Verousis

  1. Uncovering networks amongst stocks returns by studying nonlinear interactions in high frequency data of the Indian Stock Market using mutual information By Charu Sharma; Amber Habib
  2. Forecasting the Realized Variance in the Presence of Intraday Periodicity By Dumitru, Ana-Maria; Hizmeri, Rodrigo; Izzeldin, Marwan

  1. By: Charu Sharma; Amber Habib
    Abstract: In this paper, we explore the detection of clusters of stocks that are in synergy in the Indian Stock Market and understand their behaviour in different circumstances. We have based our study on high frequency data for the year 2014. This was a year when general elections were held in India, keeping this in mind our data set was divided into 3 subsets, pre-election period: Jan-Feb 2014; election period: Mar-May 2014 and :post-election period: Jun-Dec 2014. On analysing the spectrum of the correlation matrix, quite a few deviations were observed from RMT indicating a correlation across all the stocks. We then used mutual information to capture the non-linearity of the data and compared our results with widely used correlation technique using minimum spanning tree method. With a larger value of power law exponent {\alpha}, corresponding to distribution of degrees in a network, the nonlinear method of mutual information succeeds in establishing effective network in comparison to the correlation method. Of the two prominent clusters detected by our analysis, one corresponds to the financial sector and another to the energy sector. The financial sector emerged as an isolated, standalone cluster, which remain unaffected even during the election periods.
    Date: 2019–02
  2. By: Dumitru, Ana-Maria; Hizmeri, Rodrigo; Izzeldin, Marwan
    Abstract: This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects forecasting. To account for this, we propose a periodicity-adjusted model, HARP, where predictors are built from the periodicity-filtered data. We demonstrate empirically (using 30 stocks from various business sectors and the SPY for the period 2000--2016) and via Monte Carlo simulations that the HARP models produce significantly better forecasts, especially at the 1-day and 5-days ahead horizons.
    Keywords: realized volatility,forecast,intraday periodicity,heterogeneous autoregressive models
    JEL: C14 C22 C58 G17
    Date: 2019

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