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doi: 10.3934/jimo.2020042

A lattice method for option evaluation with regime-switching asset correlation structure

Adam Smith Business School, University of Glasow, Glasgow G12 8QQ, UK

Received  January 2016 Revised  January 2017 Published  March 2020

Fund Project: The first authors are supported by the Adam Smith Business School of the University of Glasow

This paper develops a lattice method for option evaluation in the presence of regime shifts in the correlation structure of assets, aiming at investigating whether the option prices reflect such shifts. We try to investigate whether option prices reflect switches in the correlation between the underlying asset of an option and risk-free rates.We develop and test two models.In the first model we allow all the parameters to follow a regime-switching process while in the second model, in order to isolate the regime-switching correlation effect on the option prices, we allow only the correlation to follow a regime-switching process. We use pentanomial lattices to represent the evolution of the regime-switching underlying assets. This is then applied in our empirical analysis, which focuses on crude oil. We use grid- and patternsearch based techniques to fit our models. Our findings suggest that prices of market traded options reflect the regime-switches and that a model which considers these switches produces significantly more accurate results than a single-regime model. We demonstrate that there is an asymmetry between parameter values obtained from historical data (backward looking) and those that are implied by traded options (for- ward looking) by employing the Kim filter to estimate our model.

Citation: Christoforidou Amalia, Christian-Oliver Ewald. A lattice method for option evaluation with regime-switching asset correlation structure. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2020042
References:
[1]

N. P. B. Bollen, Valuing options in regime-switching models, Journal of Derivatives, 6 (1998), 38-49.   Google Scholar

[2]

N. P. B. BollenS. F. Gray and R. E. Whaley, Regime switching in foreign exchange rates: Evidence from currency option prices, Journal of Econometrics, 94 (2000), 239-276.   Google Scholar

[3]

P. P. Boyle, A Lattice Framework for Option Pricing with Two State variables, Journal of Financial and Quantitative Analysis, 1988. Google Scholar

[4]

J. C. CoxS. A. Ross and M. Rubinstein, Option pricing: A Simplified approach, Journal of Financial Economics, 7 (1979), 229-263.   Google Scholar

[5]

J. C. Duan, A GARCH option pricing model, Mathematical Finance, 5 (1995), 13-32.  doi: 10.1111/j.1467-9965.1995.tb00099.x.  Google Scholar

[6]

J. C. DuanI. Popova and P. Ritchken, Option pricing under regime switching, Quantitative Finance, 2 (2002), 116-132.  doi: 10.1088/1469-7688/2/2/303.  Google Scholar

[7]

S. F. Gray, Modeling the conditional distribution of interest rates as a regime-switching process, Journal of Financial Economics, 42 (1996), 27-62.   Google Scholar

[8]

J. D. Hamilton, Rational expectations econometric analysis of changes in regime: An investigation of the term structure of interest rates,, Journal og Econometric Dynamics and Control, 12 (1988), 385-423.  doi: 10.1016/0165-1889(88)90047-4.  Google Scholar

[9]

J. Hull and A. White, The pricing of options assets with stochastic volatilities, The Journal of Finance, 42 (1987), 281-300.   Google Scholar

[10]

C. J. Kim, Dynamic linear models with Markov-switching,, Journal of Econometrics, 60 (1994), 1-22.  doi: 10.1016/0304-4076(94)90036-1.  Google Scholar

[11]

C. J. Kim, Charles R.Nelson State-Space Models with Regime Switching. Classical and Gibbs-Sampling Approaches with Applications, Massachusetts Institution of Technology, United States of America, 1999. Google Scholar

[12]

V. Naik, Option valuation and hedging strategies with jumps in the volatility of asset returns, The Journal of Finance, 47 (1993), 1969-1984.   Google Scholar

[13]

G. W. Swhwert, Business cycles, financial crises, and risky asset volatility, Carnegie-Rochester Conference Series on Public Policy, 31 (1989), 83-126.   Google Scholar

[14]

C. M. TurnerR. Startz and C. R. Nelson, A markov model of heteroscedasticity, riskm and learning in the risky asset market, Journal of Financial Economics, 25 (1989), 3-22.   Google Scholar

[15]

D. D. Yao, Q. Zhang and X. Y. Zhou, A regime-switching model for european option pricing,, Stochastic Processes, Optimization, and Control Theory: Applications in Financial Engineering, Queueing Networks, and Manufacturing Systems, Springer US, 94 (2006), 281–300. doi: 10.1007/0-387-33815-2_14.  Google Scholar

show all references

References:
[1]

N. P. B. Bollen, Valuing options in regime-switching models, Journal of Derivatives, 6 (1998), 38-49.   Google Scholar

[2]

N. P. B. BollenS. F. Gray and R. E. Whaley, Regime switching in foreign exchange rates: Evidence from currency option prices, Journal of Econometrics, 94 (2000), 239-276.   Google Scholar

[3]

P. P. Boyle, A Lattice Framework for Option Pricing with Two State variables, Journal of Financial and Quantitative Analysis, 1988. Google Scholar

[4]

J. C. CoxS. A. Ross and M. Rubinstein, Option pricing: A Simplified approach, Journal of Financial Economics, 7 (1979), 229-263.   Google Scholar

[5]

J. C. Duan, A GARCH option pricing model, Mathematical Finance, 5 (1995), 13-32.  doi: 10.1111/j.1467-9965.1995.tb00099.x.  Google Scholar

[6]

J. C. DuanI. Popova and P. Ritchken, Option pricing under regime switching, Quantitative Finance, 2 (2002), 116-132.  doi: 10.1088/1469-7688/2/2/303.  Google Scholar

[7]

S. F. Gray, Modeling the conditional distribution of interest rates as a regime-switching process, Journal of Financial Economics, 42 (1996), 27-62.   Google Scholar

[8]

J. D. Hamilton, Rational expectations econometric analysis of changes in regime: An investigation of the term structure of interest rates,, Journal og Econometric Dynamics and Control, 12 (1988), 385-423.  doi: 10.1016/0165-1889(88)90047-4.  Google Scholar

[9]

J. Hull and A. White, The pricing of options assets with stochastic volatilities, The Journal of Finance, 42 (1987), 281-300.   Google Scholar

[10]

C. J. Kim, Dynamic linear models with Markov-switching,, Journal of Econometrics, 60 (1994), 1-22.  doi: 10.1016/0304-4076(94)90036-1.  Google Scholar

[11]

C. J. Kim, Charles R.Nelson State-Space Models with Regime Switching. Classical and Gibbs-Sampling Approaches with Applications, Massachusetts Institution of Technology, United States of America, 1999. Google Scholar

[12]

V. Naik, Option valuation and hedging strategies with jumps in the volatility of asset returns, The Journal of Finance, 47 (1993), 1969-1984.   Google Scholar

[13]

G. W. Swhwert, Business cycles, financial crises, and risky asset volatility, Carnegie-Rochester Conference Series on Public Policy, 31 (1989), 83-126.   Google Scholar

[14]

C. M. TurnerR. Startz and C. R. Nelson, A markov model of heteroscedasticity, riskm and learning in the risky asset market, Journal of Financial Economics, 25 (1989), 3-22.   Google Scholar

[15]

D. D. Yao, Q. Zhang and X. Y. Zhou, A regime-switching model for european option pricing,, Stochastic Processes, Optimization, and Control Theory: Applications in Financial Engineering, Queueing Networks, and Manufacturing Systems, Springer US, 94 (2006), 281–300. doi: 10.1007/0-387-33815-2_14.  Google Scholar

Figure 1.  Binomial tree
Figure 2.  Pentanomial tree
Figure 3.  Sensitivity of Call Option value to Transition Probabilities
Figure 4.  Sensitivity of Call Option Value to the Regime Parameters
Figure 5.  Sensitivity of option price to the regime resistance of the two regimes
Figure 6.  Sensitivity of Option Price to the regime switching correlation
Table 1.   
Jump Probability Underlying Asset Price
Up $ \pi_{u} $ $ S_{u} $
Horizontal $\pi_{0} $ $ S $
Down $ \pi_{d} $ $ S_{d} $
Jump Probability Underlying Asset Price
Up $ \pi_{u} $ $ S_{u} $
Horizontal $\pi_{0} $ $ S $
Down $ \pi_{d} $ $ S_{d} $
Table 2.   
Event Probability Underlying Asset Price
Asset 1 Asset 2
$ E_{1} $ $ \pi_{1} $ $ S_{p}u_{p} $ $ S_{b}u_{b} $
$ E_{2} $ $ \pi_{2} $ $ S_{p}u_{p} $ $ S_{b}d_{b} $
$ E_{3} $ $ \pi_{3} $ $ S_{p}d_{p} $ $ S_{b}d_{b} $
$ E_{4} $ $ \pi_{4} $ $ S_{p}d_{p} $ $ S_{b}u_{b} $
$ E_{5} $ $ \pi_{5} $ $ S_{p} $ $ S_{b} $
Event Probability Underlying Asset Price
Asset 1 Asset 2
$ E_{1} $ $ \pi_{1} $ $ S_{p}u_{p} $ $ S_{b}u_{b} $
$ E_{2} $ $ \pi_{2} $ $ S_{p}u_{p} $ $ S_{b}d_{b} $
$ E_{3} $ $ \pi_{3} $ $ S_{p}d_{p} $ $ S_{b}d_{b} $
$ E_{4} $ $ \pi_{4} $ $ S_{p}d_{p} $ $ S_{b}u_{b} $
$ E_{5} $ $ \pi_{5} $ $ S_{p} $ $ S_{b} $
Table 3.   
Strike Price Market Price Predicted by Lattice Predicted by BS
16 4.9 3.881616 3.9936
17 2.85 3.127282 3.0613
18 2.35 2.373772 2.2162
19 1.65 1.629733 1.5029
20 0.95 0.949999 0.9500
21 0.4 0.399999 0.5587
22 0.2 0.200003 0.3059
23 0.2 0.136097 0.1565
24 0.1 0.105053 0.0750
25 0.15 0.088113 0.0339
Sum of absolute differences 1.110163 2.573241
Strike Price Market Price Predicted by Lattice Predicted by BS
16 4.9 3.881616 3.9936
17 2.85 3.127282 3.0613
18 2.35 2.373772 2.2162
19 1.65 1.629733 1.5029
20 0.95 0.949999 0.9500
21 0.4 0.399999 0.5587
22 0.2 0.200003 0.3059
23 0.2 0.136097 0.1565
24 0.1 0.105053 0.0750
25 0.15 0.088113 0.0339
Sum of absolute differences 1.110163 2.573241
Table 4.   
Strike Price Market Price Predicted by Lattice Predicted by BS
16 5 4.4153631 4.3208036
17 3.8 3.7998973 3.5464351
18 2.45 2.3996588 2.8585724
19 2.25 1.9270998 2.2636265
20 1.6 1.5997621 1.7622281
21 1.35 1.3087541 1.3499542
22 0.95 1.0205032 1.0186664
23 1.05 1.1713058 0.7580323
24 0.6 0.6000609 0.5569055
25 0.5 0.4960518 0.4043912
Sum of absolute differences 0.4890502 1.1145081
Strike Price Market Price Predicted by Lattice Predicted by BS
16 5 4.4153631 4.3208036
17 3.8 3.7998973 3.5464351
18 2.45 2.3996588 2.8585724
19 2.25 1.9270998 2.2636265
20 1.6 1.5997621 1.7622281
21 1.35 1.3087541 1.3499542
22 0.95 1.0205032 1.0186664
23 1.05 1.1713058 0.7580323
24 0.6 0.6000609 0.5569055
25 0.5 0.4960518 0.4043912
Sum of absolute differences 0.4890502 1.1145081
Table 5.   
Strike Price Market Price Predicted by Lattice Predicted by BS
16 5 4.4153631 4.3208036
17 3.8 3.7998973 3.5464351
18 2.45 2.3996588 2.8585724
19 2.25 1.9270998 2.2636265
20 1.6 1.5997621 1.7622281
21 1.35 1.3087541 1.3499542
22 0.95 1.0205032 1.0186664
23 1.05 1.1713058 0.7580323
24 0.6 0.6000609 0.5569055
25 0.5 0.4960518 0.4043912
Sum of absolute differences 0.4890502 1.1145081
Strike Price Market Price Predicted by Lattice Predicted by BS
16 5 4.4153631 4.3208036
17 3.8 3.7998973 3.5464351
18 2.45 2.3996588 2.8585724
19 2.25 1.9270998 2.2636265
20 1.6 1.5997621 1.7622281
21 1.35 1.3087541 1.3499542
22 0.95 1.0205032 1.0186664
23 1.05 1.1713058 0.7580323
24 0.6 0.6000609 0.5569055
25 0.5 0.4960518 0.4043912
Sum of absolute differences 0.4890502 1.1145081
Table 6.   
Strike Price Predicted
16 4.9 1.625144372
17 2.85 1.269327353
18 2.35 0.940833993
19 1.65 0.626509398
20 0.95 0.420399871
21 0.4 0.245524212
22 0.2 0.158808617
23 0.2 0.079890983
24 0.1 0.045583587
25 0.15 0.023566284
Strike Price Predicted
16 4.9 1.625144372
17 2.85 1.269327353
18 2.35 0.940833993
19 1.65 0.626509398
20 0.95 0.420399871
21 0.4 0.245524212
22 0.2 0.158808617
23 0.2 0.079890983
24 0.1 0.045583587
25 0.15 0.023566284
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