Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/14035
Title: Pre-Opening Call Auction of Stock Price Discovery using Multivariate Lognormal Montecarlo Simulation
Authors: Goel, Saurabh
Bag, Dinabandhu
Keywords: Call Auction;Multivariate Log Normal;Monte Carlo;Volatility
Issue Date: 2015
Publisher: Indian Institute of Management, Ahmedabad
Citation: Goel, S., Bag, D.. (2015). Pre-Opening Call Auction of Stock Price Discovery using Multivariate Lognormal Montecarlo Simulation. 4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. Indian Institute of Management, Ahmedabad
Series/Report no.: IC 15;040
Abstract: Market microstructure design and testing of efficient market hypotheses have been of long interest in security research. Market microstructure discussions also include the impact of call auctions in price discovery and bringing out efficiency in the functioning of markets. Call auctions form a method of trading, where orders over a small time period are collected and the market opening price are derived based on aggregated supply and demand for the underlying security. Empirically the effect of introduction of opening call has not been found to be unambiguous. This paper uses Monte Carlo simulation of co-dependent prices to explain the equilibrium price formation process. It also simulates the equilibrium prices using multivariate lognormal distribution and derives the price discovery impact of the pre-opening call auctions on normal markets. Our simulation results can generate volatility estimates close to the actual historical volatility of pre-opening equilibrium price and discovery efficiency. We provide an application of Multivariate Monte Carlo simulation to improve upon the volatility estimates by using daily pre-opening auction data for over 50 stocks listed in NSE.
URI: http://hdl.handle.net/11718/14035
Appears in Collections:4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

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