Stochastic Correlation in Commodity Spread Options

2014 ◽  
Author(s):  
Ilya Kolpakov
Keyword(s):  
Spread Options ◽  
Stochastic Correlation

Pricing of spread options on stochastically correlated underlyings

2009 ◽  
Vol 12 (3) ◽  
pp. 31-61 ◽  
Author(s):  
Marcos Escobar ◽  
Barbara Götz ◽  
Luis Seco ◽  
Rudi Zagst
Keyword(s):  
Spread Options

CMS spread options

2021 ◽  
pp. 1-16
Author(s):  
Patrick S. Hagan ◽  
Andrew S. Lesniewski ◽  
G. E. Skoufis ◽  
Diana E. Woodward
Keyword(s):  
Spread Options

Pricing spread options by generalized bivariate edgeworth expansion

2017 ◽  
Vol 04 (02n03) ◽  
pp. 1750017
Author(s):  
Edward P. C. Kao ◽  
Weiwei Xie
Keyword(s):  
Edgeworth Expansion ◽  
Price Difference ◽  
Empirical Estimation ◽  
Strike Price ◽  
Closed Form Solutions ◽  
Spread Options ◽  
Valuation Process ◽  
The Difference ◽  
Spread Option ◽  
Underlying Processes

A spread option is a contingent claim whose underlying is the price difference between two assets. For a call, the holder of the option receives the difference, if positive, between the price difference and the strike price. Otherwise, the holder receives nothing. Spread options trade in large volume in financial, fixed-income, commodity, and energy industries. It is well known that pricing of spread options does not admit closed-form solutions even under a geometric Brownian motion paradigm. When price dynamics experience stochastic volatilities and/or jumps, the valuation process becomes more challenging. Following the seminal work of Jarrow and Judd, we propose the use of Edgeworth expansion to approximate the call price. In the spirit of Pearson, we reduce the cumbersome computation inherent in Edgeworth expansion to single numerical integrations. For an arbitrary bivariate price process, we show that once its product cumulants are available, either by virtue of the structural properties of the underlying processes or by empirical estimation using market data, the approach enables analysts to approximate the call price easily. Specifically, the call prices so estimated capture the correlation, skewness, and kurtosis of the two underlying price processes. As such, the approach is useful for approximate valuations based on Lévy-based models.

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