The informational role of algorithmic traders in the option market

Rohini Grover ()
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Rohini Grover: Indira Gandhi Institute of Development Research

Indira Gandhi Institute of Development Research, Mumbai Working Papers from Indira Gandhi Institute of Development Research, Mumbai, India

Abstract: This paper investigates the information role of algorithmic traders (AT) in the Nifty index option market. I analyse a unique dataset to test for information-based trading by looking at the effect of net buying pressure options on implied volatilities. According to the direction-learning hypothesis, (directional) informed investors' net buying pressure of calls (puts) raises the implied volatilities of calls (puts) and lowers the implied volatilities of puts (calls). In addition, their net buying pressure can also predict future index returns. According to the volatility-learning hypothesis, (volatility) informed investors' net buying pressure is always positively related to implied volatilities. I find that these relations do not hold for AT and, therefore, infer absense of information-based trading by AT. On the contrary, I find the direction-learning hypothesis to hold for non-AT who, in this market, are primarily individual investors.

Keywords: mplied Volatility; Net buying pressure; Index option market (search for similar items in EconPapers)
JEL-codes: G13 G14 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2015-05
New Economics Papers: this item is included in nep-mst
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