A fuzzy set approach for generalized CRR model: An empirical analysis of S&P 500 index options

被引：20
|
作者：
Lee C.F. [1 ,2 ]
Tzeng G.-H. [3 ,4 ]
Wang S.-Y. [5 ,6 ]
机构：
[1] Rutgers Business School, Piscataway, NJ 08854
[2] Institute of Financial Management, National Chiao Tung University, Hsinchu 300
[3] Department of Business Administration, Kainan University, Luchu Shiang, Taoyuan 338
[4] Institute of Management Technology, National Chiao Tung University, Hsinchu 300
[5] Institute of Management Science, National Chiao Tung University, Hsinchu 300
[6] Department of Finance, National Dong Hwa University, Shou-Feng, Hualien 974, 1, Sec. 2, Da-Hsueh Rd.
来源：
Review of Quantitative Finance and Accounting | 2005年 / 25卷 / 3期
关键词：
A generalized CRR model; Fuzzy binomial OPM; fuzzy set theory; Option pricing model (OPM); Portfolio strategy; Triangular fuzzy number;
D O I：
10.1007/s11156-005-4767-1
中图分类号：
学科分类号：
摘要：
This paper applies fuzzy set theory to the Cox, Ross and Rubinstein (CRR) model to set up the fuzzy binomial option pricing model (OPM). The model can provide reasonable ranges of option prices, which many investors can use it for arbitrage or hedge. Because of the CRR model can provide only theoretical reference values for a generalized CRR model in this article we use fuzzy volatility and fuzzy riskless interest rate to replace the corresponding crisp values. In the fuzzy binomial OPM, investors can correct their portfolio strategy according to the right and left value of triangular fuzzy number and they can interpret the optimal difference, according to their individual risk preferences. Finally, in this study an empirical analysis of S&P 500 index options is used to find that the fuzzy binomial OPM is much closer to the reality than the generalized CRR model. © 2005 Springer Science + Business Media, Inc.
引用
收藏
页码：255 / 275
页数：20
相关论文
共 50 条
  • [41] Forecasting with Deep Learning: S&P 500 index
    Kamalov, Firuz
    Smail, Linda
    Gurrib, Ikhlaas
    [J]. 2020 13TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2020), 2020, : 422 - 425
  • [42] Remarks on the Calculation of Earnings in the S&P 500 Index
    Keppler, Michael
    Xue, Xing-Hong
    [J]. JOURNAL OF INVESTING, 2011, 20 (03): : 15 - 17
  • [43] The competitive effects of S&P 500 Index revisions
    Chen, Sheng-Syan
    Lin, Yueh-Hsiang
    [J]. JOURNAL OF BUSINESS FINANCE & ACCOUNTING, 2018, 45 (7-8) : 997 - 1027
  • [44] A Level Set Analysis and A Nonparametric Regression on S&P 500 Daily Return
    Yang, Yipeng
    Tsoi, Allanus
    [J]. INTERNATIONAL JOURNAL OF FINANCIAL STUDIES, 2016, 4 (01):
  • [45] Determinants of S&P 500 index option returns
    Cao C.
    Huang J.-Z.
    [J]. Review of Derivatives Research, 2007, 10 (1) : 1 - 38
  • [46] S&P 500 Index revisions and credit spreads
    Baran, Lindsay
    Li, Ying
    Liu, Chang
    Liu, Zilong
    Pu, Xiaoling
    [J]. REVIEW OF FINANCIAL ECONOMICS, 2018, 36 (04) : 348 - 363
  • [47] How important is the correlation between returns and volatility in a stochastic volatility model? Empirical evidence from pricing and hedging in the S&P 500 index options market
    Nandi, S
    [J]. JOURNAL OF BANKING & FINANCE, 1998, 22 (05) : 589 - 610
  • [48] Stock price response to S&P 500 index inclusions: Do options listings and options trading volume matter?
    Chen, Yangyang
    Koutsantony, Constantine
    Truong, Cameron
    Veeraraghavan, Madhu
    [J]. JOURNAL OF INTERNATIONAL FINANCIAL MARKETS INSTITUTIONS & MONEY, 2013, 23 : 379 - 401
  • [49] Negative returns on addition to the S&P 500 index and positive returns on deletion? New evidence on the attractiveness of S&P 500 versus S&P 400 indexes
    Vijh, Anand M.
    Wang, Jiawei
    [J]. FINANCIAL MANAGEMENT, 2022, 51 (04) : 1127 - 1164
  • [50] A test of efficiency for the S&P 500 index option market using the generalized spectrum method
    Huang, Henry H.
    Wang, Kent
    Wang, Zhanglong
    [J]. JOURNAL OF BANKING & FINANCE, 2016, 64 : 52 - 70
12345