Please use this identifier to cite or link to this item: https://repository.iimb.ac.in/handle/2074/19273
Title: Asymmetry variance as a tool for return on stocks
Authors: Saini, Gajanand 
Patil, Sneha 
Keywords: Stock market;Stock returns
Issue Date: 2018
Publisher: Indian Institute of Management Bangalore
Series/Report no.: PGP_CCS_P18_050
Abstract: Return on asset are generally calculated using the historical data, which are backward looking and might not be the correct measure for the future performance of the return on that asset. Therefore, we are proposing to analyse a forward-looking measure which is asymmetry in the variance of the asset returns, estimated from the option prices. This measure is a close approximation for the asset risk premium. We will do this analysis on the Indian stocks. This will be empirically tested by sufficient condition whether the risk-neutral variance asymmetry ranks stocks based on their expected returns. Thus, empirically we will examine the correlation between this measure and future stock returns. In this paper, we propose a method to analyse the option prices and see if forward-looking information on future returns of asset from options can be obtained without extracting real probabilities for Indian stocks. In Indian stocks, those stocks will be picked for analysis which are liquid. Risk neutral probability measure will be used. To accomplish this objective, we will define a forward-looking measure, asymmetry of variance (AVAR). AVAR is the difference between the upside semi-variance and downside semi-variance under the risk-neutral distribution. Thus, AVAR will measure how much more risk investors face when the price increases relative to when it decreases. To analyse this, we will provide an empirically sufficient condition under which we will rank stocks based on AVAR and examine the correlation between this measure and future stock returns. Thus, we will inspect AVAR’s predictive power on future returns.
URI: https://repository.iimb.ac.in/handle/2074/19273
Appears in Collections:2018

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