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dc.contributor.authorGoel, Saurabh
dc.contributor.authorBag, Dinabandhu
dc.date.accessioned2015-07-08T07:07:50Z
dc.date.available2015-07-08T07:07:50Z
dc.date.issued2015
dc.identifier.citationGoel, 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, Ahmedabaden_US
dc.identifier.urihttp://hdl.handle.net/11718/14035
dc.description.abstractMarket 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.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Management, Ahmedabaden_US
dc.relation.ispartofseriesIC 15;040
dc.subjectCall Auctionen
dc.subjectMultivariate Log Normalen
dc.subjectMonte Carloen
dc.subjectVolatilityen
dc.titlePre-Opening Call Auction of Stock Price Discovery using Multivariate Lognormal Montecarlo Simulationen_US
dc.typeArticleen_US


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