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Options Exchange (option + exchange)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting75%

Selected Abstracts

Empowering Automated Trading in Multi-Agent Environments

COMPUTATIONAL INTELLIGENCE, Issue 4 2004
David W. Ash
Trading in the financial markets often requires that information be available in real time to be effectively processed. Furthermore, complete information is not always available about the reliability of data, or its timeliness,nevertheless, a decision must still be made about whether to trade or not. We propose a mechanism whereby different data sources are monitored, using Semantic Web facilities, by different agents, which communicate among each other to determine the presence of good trading opportunities. When a trading opportunity presents itself, the human traders are notified to determine whether or not to execute the trade. The Semantic Web, Web Services, and URML technologies are used to enable this mechanism. The human traders are notified of the trade at the optimal time so as not to either waste their resources or lose a good trading opportunity. We also have designed a rudimentary prototype system for simulating the interaction between the intelligent agents and the human beings, and show some results through experiments on this simulation for trading of the Chicago Board Options Exchange (CBOE) options. [source]

PRICING AND HEDGING AMERICAN OPTIONS ANALYTICALLY: A PERTURBATION METHOD

MATHEMATICAL FINANCE, Issue 1 2010
Jin E. Zhang
This paper studies the critical stock price of American options with continuous dividend yield. We solve the integral equation and derive a new analytical formula in a series form for the critical stock price. American options can be priced and hedged analytically with the help of our critical-stock-price formula. Numerical tests show that our formula gives very accurate prices. With the error well controlled, our formula is now ready for traders to use in pricing and hedging the S&P 100 index options and for the Chicago Board Options Exchange to use in computing the VXO volatility index. [source]

FORECASTING STOCK INDEX VOLATILITY: COMPARING IMPLIED VOLATILITY AND THE INTRADAY HIGH,LOW PRICE RANGE

THE JOURNAL OF FINANCIAL RESEARCH, Issue 2 2007
Charles Corrado
Abstract The intraday high,low price range offers volatility forecasts similarly efficient to high-quality implied volatility indexes published by the Chicago Board Options Exchange (CBOE) for four stock market indexes: S&P 500, S&P 100, NASDAQ 100, and Dow Jones Industrials. Examination of in-sample and out-of-sample volatility forecasts reveals that neither implied volatility nor intraday high,low range volatility consistently outperforms the other. [source]

The impact of electronic trading on bid-ask spreads: Evidence from futures markets in Hong Kong, London, and Sydney

THE JOURNAL OF FUTURES MARKETS, Issue 7 2004
Michael J. Aitken
During 1999 and 2000, three major futures exchanges transferred trading in stock index futures from open outcry to electronic markets: the London International Financial Futures and Options Exchange (LIFFE); the Sydney Futures Exchange (SFE); and the Hong Kong Futures Exchange (HKFE). These changes provide unique natural experiments to compare relative bid-ask spreads of open outcry vs. electronically traded markets. This paper provides evidence of a decrease in bid-ask spreads following the introduction of electronic trading, after controlling for changes in price volatility and trading volume. This provides support for the proposition that electronic trading can facilitate higher levels of liquidity and lower transaction costs relative to floor traded markets. However, bid-ask spreads are more sensitive to price volatility in electronically traded markets, suggesting that the performance of electronic trading systems deteriorates during periods of information arrival. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:675,696, 2004 [source]