scholarly journals The application of the improved option parity arbitrage model in SSE 50ETF option

2021 ◽  
Vol 233 ◽  
pp. 01169
Author(s):  
Liu Xu
Keyword(s):  
Transaction Costs ◽  
Stock Index ◽  
Options Market ◽  
Arbitrage Opportunities ◽  
Strategy Model ◽  
Effective Option ◽  
Arbitrage Strategy ◽  
Risk Free Rate ◽  
Index Option ◽  
Free Rate

The SSE 50ETF option is China's first stock index option product launched in 2015. For a number of reasons, the options market can sometimes create arbitrage opportunities. Based on the theory of option parity arbitrage and taking into account the transaction costs, this paper explores effective options arbitrage strategies and practices them. Based on the theory of option parity arbitrage and taking into account the transaction costs, this paper establishes an effective option arbitrage strategy model and puts it into practice. The results show that there are indeed arbitrage opportunities in the market that exceed the risk-free rate of return, but there are not many such opportunities, and there is not much arbitrage space under many opportunities. This is not only the embodiment of high market efficiency, but also the result of taking various transaction costs into full consideration in this paper to ensure the effectiveness of arbitrage.

CHANCE DISCOVERY IN STOCK INDEX OPTION AND FUTURES ARBITRAGE

2005 ◽  
Vol 01 (03) ◽  
pp. 435-447 ◽  
Author(s):  
EDWARD TSANG ◽  
SHERI MARKOSE ◽  
HAKAN ER
Keyword(s):  
Historical Data ◽  
Stock Index ◽  
Domain Experts ◽  
Programming Tool ◽  
Promising Tool ◽  
Arbitrage Opportunities ◽  
Out Of Sample ◽  
Computer Scientists ◽  
The One ◽  
Index Option

The prices of the option and futures of a stock both reflect the market's expectation of futures changes of the stock's price. Their prices normally align with each other within a limited window. When they do not, arbitrage opportunities arise: an investor who spots the misalignment will be able to buy (sell) options on the one hand, and sell (buy) futures on the other and make risk-free profits. Historical data suggest that option and futures prices on the LIFFE Market do not align occasionally. Arbitrage chances are rare. Besides, they last for seconds only before the market adjusts itself. The challenge is not only to discover such chances, but to discover them ahead of other arbitragers. In the past, we have introduced EDDIE as a genetic programming tool for forecasting. This paper describes EDDIE-ARB, a specialization of EDDIE, for forecasting arbitrage opportunities. As a tool, EDDIE-ARB was designed to enable economists and computer scientists to work together to identify relevant independent variables. Trained on historical data, EDDIE-ARB was capable of discovering rules with high precision. Tested on out-of-sample data, EDDIE-ARB out-performed a naive ex ante rule, which reacted only when misalignments were detected. This establishes EDDIE-ARB as a promising tool for arbitrage chances discovery. It also demonstrates how EDDIE brings domain experts and computer scientists together.

Robust Benchmarking of Indian Mutual Funds-A Partial Frontier Approach

2015 ◽  
Vol 5 (2) ◽  
Author(s):  
Ram Pratap Sinha
Keyword(s):  
Decision Making ◽  
Performance Evaluation ◽  
Mutual Funds ◽  
Mutual Fund ◽  
Multi Criteria Decision Making ◽  
Risk Framework ◽  
Programming Techniques ◽  
Risk Free Rate ◽  
Risk Ratios ◽  
Free Rate

Performance analysis of mutual funds is usually made on the basis of return-risk framework. Traditionally, excess return (over risk-free rate) to risk ratios were used for the purpose mutual fund evaluation. Subsequently, the application of non-parametric mathematical programming techniques in the context of performance evaluation facilitated multi-criteria decision making. However,the estimates of performance on the basis of conventional programming techniques like DEA and FDH are affected by the presence of outliers in the sample observations. The present, accordingly uses more robust benchmarking techniques for evaluating the performance od sectoral mutual fund schemes based on observations for the second half of 2010. The USP of the present study is that it uses two partial frontier techniques (Order-m and Order- a) which are less susceptible to the problem of extreme data.

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