Modeling Information Linkages in the Stock and Options Markets

dc.contributor.authorHo, Kin-Yip
dc.contributor.authorZheng, L
dc.contributor.authorZhang, Z Y
dc.coverage.spatialPerth Australia
dc.date.accessioned2015-12-07T22:46:35Z
dc.date.createdDecember 12-16 2011
dc.date.issued2011
dc.date.updated2016-02-24T09:21:17Z
dc.description.abstractWhen markets are assumed to be complete, option trading should not contain new information for market participants, as options derive their prices from the underlying stocks. However, if markets are incomplete, then this unidirectional relationship may not be true, because informed traders may prefer to trade options instead of the underlying stocks for several reasons: one, option trading involves lower transaction costs and higher financial leverage; and two, investors who have private information about stock price volatility can only make their bet on volatility in the option market. Compared with the research on the relationship between options trading activity and stock prices, there is little analysis on the information embodied in option transactions volume for stock market volatility, which undoubtedly is an important variable for risk management and portfolio allocation. This study focuses on the dynamic linkages between option trading volume and stock market volatility. We compare the significance of option trading activity in explaining the volatilities of the underlying stocks with that of stock market volume by selecting 15 New York Stock Exchange (NYSE) stocks that are most actively traded in the option markets during the period from December 11 2002 to August 31 2006. Our approach implies the following two distinctive features: • instead of the put/call volume ratio conventionally used in the literature, we measure the influence of option volume on stock market volatility by constructing the relative put (RPUT) and relative call (RCALL) ratios. • our approach also allows us to quantify the impact of option volume on the existence of persistence and asymmetry in stock market volatility. Instead of the usual generalized autoregressive conditional heteroskedasticity (GARCH) model that is commonly used to analyze the stock volume-volatility relation, we adopt Nelson's (1991) exponential GARCH (EGARCH) approach in this study. For each stock, it is noted that the trading activities in the put and call options markets have significant explanatory power for stock market volatility. In addition, the results indicate that the call options trading activity has a stronger impact on stock volatility compared with that of the put options. Our results demonstrate that information and sentiment in the option market is useful for the estimation of stock market volatility. Also, the significance of the effects of option trading activity on stock price volatility is observed to be comparable to that of stock market trading activity. Furthermore, the persistence and asymmetric effects in the volatility of some stocks tend to disappear once option trading activity is taken into account.
dc.identifier.urihttp://hdl.handle.net/1885/25842
dc.publisherModelling and Simulation Society of Australia and New Zealand Inc.
dc.relation.ispartofseriesInternational Congress on Modelling and Simulation (MODSIM 2011)
dc.rightsAuthor/s retain copyrighten_AU
dc.sourceProceedings of MODSIM 2011 International Congress on Modelling and Simulation
dc.source.urihttp://www.mssanz.org.au/modsim2011/index.html
dc.subjectKeywords: Asymmetric effects; Distinctive features; Explanatory power; Generalized autoregressive conditional heteroskedasticity; Information linkage; Market participants; New York Stock Exchange; Option markets; Option trading; Option trading volume; Options marke Asymmetric effects; Information linkage; Option trading volume; Stock market volatility
dc.titleModeling Information Linkages in the Stock and Options Markets
dc.typeConference paper
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.lastpage1553
local.bibliographicCitation.startpage1547
local.contributor.affiliationHo, Kin-Yip, College of Business and Economics, ANU
local.contributor.affiliationZheng, L, City College of New York
local.contributor.affiliationZhang, Z Y, Edith Cowan University
local.contributor.authoruidHo, Kin-Yip, u4867077
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor140305 - Time-Series Analysis
local.identifier.ariespublicationf5625xPUB41
local.identifier.scopusID2-s2.0-84863360084
local.type.statusPublished Version

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