Method and System for High Speed Options Pricing

• US 20070294157A1
• Filed: 06/08/2007
• Published: 12/20/2007
• Est. Priority Date: 06/19/2006
• Status: Active Grant

• Current Assignee: Exegy Incorporated
• Original Assignee: Exegy Incorporated
• Inventors: Singla, Naveen, Parsons, Scott, Taylor, David, Franklin, Mark
• Granted Patent: US 7,840,482 B2
• Time in Patent Office: Days
• US Class Current: 705/36.00R
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A high speed technique for options pricing in the financial industry is disclosed that can provide both high throughput and low latency. A parallel/pipelined architecture is disclosed for computing an implied volatility in connection with an option. Parallel/pipelined architectures are also disclosed for computing an option'"'"'s theoretical fair price. Preferably these parallel/pipelined architectures are deployed in hardware, and more preferably reconfigurable logic such as Field Programmable Gate Arrays (FPGAs) to accelerate the options pricing operations relative to conventional software-based options pricing operations.

Claims

1. A device for processing financial market data, the device comprising:

a reconfigurable logic device that is configured to (1) receive a data stream comprising financial market data and (2) perform an options pricing operation on at least a portion of the received data stream.

2. The device of claim 1 wherein the options pricing operation comprises a computation of an implied volatility for an option on an underlying financial instrument.

3. The device of claim 2 wherein the options pricing operation further comprises a computation of at least one theoretical fair market price for the option.

4. The device of claim 3 wherein the reconfigurable logic device is further configured to compute the at least one theoretical fair market price by applying an option pricing model to at least a portion of the received data stream.

5. The device of claim 4 wherein the option pricing model comprises an option pricing model based on a Cox-Ross-Rubinstein (CRR) pricing model.

6. The device of claim 2 wherein the reconfigurable logic device is configured with a firmware application module pipeline, the firmware application module pipeline being configured to perform the options pricing operation.

7. The device of claim 2 wherein the implied volatility computation comprises an iterative computation of the implied volatility.

8. The device of claim 1 wherein the options pricing operation comprises a computation of a theoretical fair market price for an option on an underlying financial instrument.

9. The device of claim 8 wherein the reconfigurable logic device is configured to perform the options pricing operation using a lookup table indexed by a volatility for the option, the lookup table being configured to store a plurality of precomputed parameters for a plurality of different volatility values, the precomputed parameters for use by the reconfigurable logic device in the computation of the theoretical fair market price.

10. The device of claim 9 wherein the lookup table is external to the reconfigurable logic device.

11. The device of claim 8 wherein the reconfigurable logic device is further configured to directly compute a plurality of constituent terms used in the theoretical fair market price computation via a plurality of pipelined stages, wherein a plurality of the pipelined stages comprise a plurality of parallel computational units.

12. The device of claim 8 wherein the reconfigurable logic device is further configured to perform computation of the theoretical fair market price based on a binomial option pricing model.

13. The device of claim 1 wherein the reconfigurable logic device comprises a field programmable gate array (FPGA).

14. A method for processing financial market data, the method comprising:

streaming financial market data through a reconfigurable logic device; and

performing an options pricing operation with the reconfigurable logic device on at least a portion of the financial market data as the financial market data streams therethrough.

15. The method of claim 14 wherein the performing step comprises computing an implied volatility for an option on an underlying financial instrument.

16. The method of claim 15 wherein the performing step further comprises computing at least one theoretical fair market price for the option.

17. The method of claim 16 wherein the theoretical price computing step comprises computing the at least one theoretical fair market price by applying an option pricing model to at least a portion of the streaming financial market data.

18. The method of claim 17 wherein the option pricing model comprises an option pricing model based on a Cox-Ross-Rubinstein (CRR) pricing model.

19. The method of claim 15 wherein the computing step comprises computing the implied volatility with a firmware application module pipeline.

20. The method of claim 15 further comprising:

feeding the computed implied volatility to a black box trading algorithm; and

automatically executing a trade on the option via the black box trading algorithm based at least in part upon the computed implied volatility.

21. The method of claim 15 further comprising:

automatically generating an option quote with an auto-quote server based at least in part upon the computed implied volatility.

22. The method of claim 14 wherein the performing step comprises computing a theoretical fair market price for an option on an underlying financial instrument.

23. The method of claim 22 further comprising:

storing a plurality of precomputed parameters for a plurality of different volatility values in a lookup table indexed by a volatility index; and

retrieving a precomputed parameter for use in the computing step from the lookup table using a volatility value for the option as the volatility index into the lookup table; and

wherein the computing step comprises computing the theoretical fair market price based at least in part upon the retrieved precomputed parameter.

24. The method of claim 22 further comprising:

feeding the computed theoretical fair purchase price to a black box trading algorithm; and

automatically executing a trade on the option via the black box trading algorithm based at least in part upon the computed theoretical fair purchase price.

25. The method of claim 22 further comprising:

automatically generating an option quote with an auto-quote server based at least in part upon the computed theoretical fair purchase price.

26. The method of claim 22 wherein the computing step comprises directly computing a plurality of constituent terms used in the theoretical fair market price computation via a plurality of pipelined stages, wherein a plurality of the pipelined stages comprise a plurality of parallel computational units.

27. A device for computing an implied volatility for an option on an underlying financial instrument, the device comprising:

a plurality of computational modules arranged in a pipeline, each computational module being configured to (1) receive a plurality of option characteristic values for the option, (2) compute a plurality of theoretical fair market prices for the option from the option characteristic values and a plurality of different volatility values, (3) determine whether a condition has been satisfied, and (4) in response to a determination that the condition has been satisfied, determine the implied volatility for the option based at least in part on the computed theoretical fair market prices; and

wherein each computational module in the pipeline is configured to successively operate on a progressively narrower band of volatility values for the option.

28. The device of claim 27 wherein each computational module is configured to compute the plurality of theoretical fair market prices in parallel.

29. The device of claim 28 wherein the pipeline is configured to process a data stream comprising a plurality of option messages, each option message corresponding to a different option and comprising a plurality of option characteristic values therefor, and wherein successive computational modules in the pipeline are configured to operate sequentially on the same option message such that each computational module in the pipeline is configured to operate in parallel on different option messages to thereby determine the implied volatilities for the different option messages.

30. The device of claim 29 wherein the condition corresponds to a theoretical fair market price convergence property.

31. The device of claim 30 wherein each computational module is further configured to (1) determine whether the theoretical fair market price convergence property condition is satisfied by comparing each theoretical fair market price computed thereby with an actual purchase price for the option and determining that the theoretical fair market price convergence property condition is satisfied if one of the theoretical fair market prices computed thereby differs from the actual purchase price by less than the theoretical fair market price convergence property, and (2) in response to a determination that the theoretical fair market price convergence property condition has been satisfied, determine the implied volatility for the option as the volatility value for which the theoretical fair market price computed therefrom differs from the actual purchase price by less than the theoretical fair market price convergence property.

32. The device of claim 29 wherein the condition corresponds to a volatility convergence property.

33. The device of claim 32 wherein each computational module is further configured to identify a band of volatility values within which the implied volatility resides based at least in part upon the plurality of theoretical fair market prices computed by that computational module, wherein the identified band has a width that is less than a full range of the volatility values for which that computational module computed the plurality of theoretical fair market prices, wherein each computational module is further configured to (1) determine whether the volatility convergence property condition is satisfied by comparing the width of the identified band with the volatility convergence property, and (2) in response to a determination that the volatility convergence property condition has been satisfied, determine the implied volatility for the option as a value within the identified band.

34. The device of claim 29 wherein each computational module is further configured to determine whether at least one of at least two conditions has been satisfied, and, in response to a determination that at least one of the conditions has been satisfied, determine the implied volatility for the option based at least in part on the computed theoretical fair market prices, wherein the at least two conditions comprise a condition corresponding to a theoretical fair market price convergence property and a condition corresponding to a volatility convergence property.

35. The device of claim 29 wherein each computational module is further configured to identify a band of volatility values within which the implied volatility resides based at least in part upon the plurality of theoretical fair market prices computed by that computational module, wherein the identified band has a width that is less than a full range of the volatility values for which that computational module computed the plurality of theoretical fair market prices, wherein each next successive computational module in the pipeline is configured to operate on a plurality of volatility values within the band identified by the previous computational module in the pipeline.

36. The device of claim 29 wherein each computational module is configured to compute the theoretical fair market prices based on a Cox-Ross-Rubenstein (CRR) option pricing model.

37. The device of claim 36 wherein each computational module is further configured to compute the theoretical fair market prices using at least one look-up table of precomputed terms for the CRR option pricing model, the look-up table being indexed by at least one of the option characteristic values.

38. The device of claim 27 further comprising at least one reconfigurable logic device on which the pipeline of computational modules is deployed.

39. A computer-implemented method for determining an implied volatility for an option on an underlying financial instrument, the method comprising:

computing a plurality of theoretical fair market prices for the option, each theoretical fair market price being computed based at least in part upon a volatility value for the underlying financial instrument;

identifying a band of volatility values within which the theoretical fair market price for the option differs from an actual purchase price for the option by less than a predetermined threshold;

iteratively narrowing the band of volatility values and repeating the computing step for a plurality of volatility values within the narrowed band until one of the computed theoretical fair market prices differs from the actual purchase price by less than the predetermined threshold; and

defining the implied volatility for the option as the volatility value for which the computed theoretical fair market price differs from the actual purchase price by less than the predetermined threshold.

40. The method of claim 39 further comprising:

for a first iteration, defining a plurality of volatility values such that a range of volatility values for the underlying financial instrument is subdivided into a plurality m of bands; and

wherein the computing step for the first iteration comprises computing the theoretical fair market prices for each of the defined volatility values.

41. The method of claim 40 wherein the band identifying step comprises:

determining which one of the m bands has a computed theoretical fair market price at a first boundary and a computed theoretical fair market price at a second boundary such that the actual purchase price falls between the computed theoretical fair market price at the first boundary and the computed theoretical fair market price at the second boundary; and

selecting the determined band as the identified band.

42. The method of claim 41 wherein the iteratively narrowing and repeating step comprises

for each iteration, (1) defining a plurality of volatility values within the selected band such that the selected band is subdivided into a plurality m of bands, (2) computing a plurality of theoretical fair market prices for the defined volatility values within the selected band, and (3) performing the band identifying step for the m bands of the selected band.

43. The method of claim 42 wherein the value of m for all iterations is 4.

44. The method of claim 42 wherein the computing step comprises computing the plurality of theoretical fair market prices in parallel.

45. The method of claim 44 wherein the computing step comprises computing the theoretical fair market prices based on a Cox-Ross-Rubinstein (CRR) option pricing model.

46. The method of claim 45 wherein the computing step further comprises computing the plurality of theoretical fair market prices in parallel based on the CRR option pricing model.

47. The method of claim 45 wherein the computing step further comprises computing each theoretical fair market price using a binomial tree having a plurality of CRR computational nodes, the binomial tree being of depth n, wherein the computational nodes of the same depth are configured to operate in parallel.

48. The method of claim 44 further comprising:

performing the computing step with reconfigurable logic.

49. The method of claim 42 further comprising:

performing at least the computing step with reconfigurable logic.

50. The method of claim 42 further comprising:

performing all of the method steps with reconfigurable logic.

51. The method of claim 39 further comprising:

performing at least the computing step with reconfigurable logic.

52. The method of claim 39 further comprising:

performing all of the method steps with reconfigurable logic.

53. The method of claim 39 further comprising:

feeding the implied volatility to a black box trading algorithm; and

automatically determining through the black box trading algorithm whether to execute a trade on the option based at least in part upon the implied volatility fed thereto.

54. The method of claim 39 further comprising:

automatically generating an option quote with an auto-quote server based at least in part upon the defined implied volatility.

55. The method of claim 39 further comprising:

receiving data describing the option within a financial market data stream, the option data comprising the actual purchase price for the option, a current price of the financial instrument underlying the option, a strike price for the option, and a time to maturity for the option.

56. A computer-implemented method for determining an implied volatility for an option on an underlying financial instrument, the method comprising:

computing a plurality of theoretical fair market prices for the option, each of said computed theoretical fair market prices corresponding to an assumed volatility value for the underlying financial instrument;

comparing the computed theoretical fair market prices with an actual purchase price for the option to determine whether at least one of the computed theoretical fair market prices satisfies a predetermined condition and,

if the pre-determined condition is satisfied, then declaring the volatility value for which the computed theoretical fair market price approximates the offer price as the option'"'"'s implied volatility, and,

if the predetermined condition is not satisfied, identifying at least one computed theoretical fair market price in relative close proximity to the actual purchase price and selecting a band of volatility values within which the actual purchase price is expected to be found based on the identified at least one computed theoretical fair market price, and within the selected band, computing a second plurality of theoretical fair market prices and repeating the comparing step.

57. The method of claim 56 wherein the step of selecting a band of volatility values includes selecting the band between two computed theoretical fair market prices surrounding the actual purchase price.

58. A device comprising:

an option pricing engine that is configured to process a financial market data stream to compute an implied volatility for an option on an underlying financial instrument, wherein the option pricing engine is configured to iteratively subdivide a range of volatility values for the underlying financial instrument into a plurality m of bands and, for each band compute at least two theoretical fair market prices for the option based on at least two volatility values for the band;

wherein the option pricing engine is further configured to continue the iterative subdivision and the corresponding theoretical fair market computations until an implied volatility for the option is found as determined by at least one condition.

59. The device of claim 58 wherein the option pricing engine comprises a pipeline of computational modules, each computational module being configured to compute a plurality of the theoretical fair market prices for a different iterative subdivision.

60. The device of claim 58 wherein the option pricing engine is further configured to feed data describing the option into the first computational module in the pipeline and initialize the first computational module with a plurality of plurality of parameters that control how the theoretical fair market prices will be computed.

61. The device of claim 60 wherein the option data comprises the actual purchase price for the option, a strike price for the option, a price for the underlying financial instrument, and a time to maturity for the option, and wherein the parameters comprise a plurality of volatility values for computing the theoretical fair market prices of the first iteration.

62. The device of claim 61 wherein the parameters further comprise an interest rate related to the option.

63. The device of claim 61 wherein the option pricing engine is further configured to compute the plurality of volatility values for use in computing the theoretical fair market prices within each band for each iteration.

64. The device of claim 61 wherein each computational module is configured to compute the plurality of theoretical fair market prices for the different volatility values in parallel.

65. The device of claim 64 wherein the option pricing engine is further configured to identify which one of the m bands that the implied volatility resides in by determining the band for which the actual purchase price falls between the computed theoretical fair market prices for that band.

66. The device of claim 65 wherein each computational module is configured to compute the plurality of theoretical fair market prices based on a Cox-Ross-Rubinstein (CRR) option pricing model.

67. The device of claim 66 wherein each computational module further comprises a plurality of parallel CRR option pricing model computational units, each parallel CRR option pricing model computational unit being configured to receive a volatility value as an input parameter and compute a theoretical fair market price for the option based at least in part upon the received volatility value.

68. The device of claim 67 wherein each parallel CRR option pricing model computational unit comprises a binomial tree of CRR computational nodes, the binomial tree comprising a plurality n of pipelined stages, wherein the computational nodes sharing a common pipelined stage in the tree are configured to operate in parallel, and wherein the pipelined stages are arranged such that the computational nodes at a lower level of the tree feed their outputs to the computational nodes at a next higher level of the tree, and wherein the computational node at a root of the tree is configured to output the computed theoretical fair market price for the input volatility value.

69. The device of claim 67 wherein the plurality of parallel CRR option pricing units are deployed on at least one reconfigurable logic device.

70. The device of claim 61 wherein the computational modules are deployed on at least one reconfigurable logic device.

71. The device of claim 61 wherein the plurality of volatility values used to initialize the first computational module in the pipeline comprise a plurality of volatility values within a predetermined range of volatility values.

72. The device of claim 61 wherein the option pricing engine is further configured to select the plurality of volatility values used to initialize the first computational module in the pipeline based on a plurality of historical volatility values for the financial instrument underlying the option.

73. The device of claim 58 wherein the at least one condition comprises a theoretical fair market price convergence property.

74. The device of claim 58 wherein the at least one condition comprises a volatility convergence property.

75. The device of claim 58 wherein the at least one condition comprises an iteration maximum.

76. The device of claim 58 wherein the at least one condition comprises an adjustable condition.

77. The device of claim 58 wherein the option pricing engine is deployed on at least one reconfigurable logic device.

78. The device of claim 58 wherein the option pricing engine is deployed across a plurality of parallel co-processors.

79. A computer-implemented method for computing an implied volatility for an option on an underlying financial instrument, the method comprising:

receiving data describing an option on an underlying financial instrument; and

performing an iterative m-ary search within a range of volatility values for the underlying financial instrument using the option data to thereby compute the implied volatility for the option.

80. The method of claim 79 further comprising:

performing the iterative m-ary search with reconfigurable logic.

81. A computer-implemented method for determining an implied volatility for an option on an underlying financial instrument, the method comprising:

computing a plurality of theoretical fair market prices for a plurality of different volatility values;

choosing at least one computed theoretical fair market price relatively close to an actual purchase price for the option;

iteratively computing a plurality of theoretical fair market prices in correspondingly narrowing bands of volatility values until a predetermined condition is satisfied; and

in response to satisfaction of the predetermined condition, defining the implied volatility for the option based at least in part on a volatility value used to compute a theoretical fair market price when the predetermined condition was satisfied.

82. The method of claim 81 further comprising:

performing the method steps with reconfigurable logic.

83. A computer-implemented method for computing a value of interest in relation to a financial instrument, the method comprising:

receiving financial market data that is descriptive of a financial instrument, wherein the financial instrument is associated with at least a first parameter and a second parameter, wherein the first parameter is a monotonically increasing or decreasing function of the second parameter;

iteratively computing a value of interest for the financial instrument by;

(1) defining a plurality of second parameter values, wherein the plurality of second parameter values together define a range of second parameter values that has been subdivided into a plurality m of bands;

(2) for each defined second parameter value, computing a value for the first parameter; and

(3) for each computed first parameter value, computing a value that is indicative of how closely the first parameter value and the received financial market data value match each other;

(4) for each computed indicative value, comparing the computed indicative value with a predetermined threshold to thereby determine whether any additional iterations are needed;

(5) responsive to a determination that no additional iterations are needed, outputting as the value of interest the first parameter value used to compute the second parameter value for which the comparing step resulted in a determination that no further iterations are needed, and responsive to a determination that an additional iteration is needed, repeating steps (1)-(5) for a next iteration, wherein for the next iteration the range of second parameter values in the defining step is the band for which the received financial market data value falls between the two first parameter values computed for that band.

84. The method of claim 83 wherein the range for a first iteration is a full range of second parameter values for the function.

85. The method of claim 84 wherein the financial instrument comprises a derivative financial instrument.

86. The method of claim 85 wherein the derivative financial instrument comprises an option on an underlying financial instrument.

87. The method of claim 86 wherein the received financial market data value comprises a price for the option, wherein the first parameter comprises a theoretical fair purchase price for the option, wherein the second parameter comprises a volatility for the underlying financial instrument, and wherein the value of interest comprises an implied volatility for the option.

88. The method of claim 83 further comprising performing the iteratively computing step with reconfigurable logic.

89. The method of claim 83 wherein the first parameter is a monotonically strictly increasing or strictly decreasing function of the second parameter.

90. A device for computing a price for an option, the device comprising:

an options pricing engine configured to process data describing an option through an option pricing model to thereby compute a theoretical fair market price for the option;

wherein the options pricing engine comprises a plurality of pipelined stages, the plurality of pipelined stages comprising a plurality of parallel computational nodes, each of said nodes being configured to compute a step of a stepwise option pricing model, and wherein said computational nodes are cascaded such that each successive pipelined stage is configured to receive as its input the output from at least two computational nodes in the immediately preceding pipelined stage, wherein the output of the last pipelined stage comprises the computed theoretical fair market price for the option.

91. The device of claim 90 wherein the option pricing model comprises a Cox, Ross and Rubinstein (CRR) option pricing model.

92. The device of claim 91 wherein the pipelined stages are arranged in a binomial tree of computational nodes, wherein the tree is of depth n.

93. The device of claim 92 wherein the options pricing engine is configured to partition the binomial tree across a plurality of processing resources.

94. The device of claim 93 wherein the plurality of processing resources comprise processing resources of different types.

95. The device of claim 94 wherein at least one of the processing resources comprises a reconfigurable logic device.

96. The device of claim 91 wherein the pipelined stages are configured to compute a plurality of terminal financial instrument prices for the option pricing model based on an upward multiplicative factor and a downward multiplicative factor.

97. The device of claim 96 wherein the options pricing engine further comprises a second set of the pipelined stages, wherein the second set of pipelined stages are configured to compute a plurality of probability terms for the option pricing model that are used in the theoretical fair market price computation.

98. The device of claim 97 wherein the two sets of pipelined stages are arranged in parallel with each other.

99. The device of claim 97 wherein the two sets of pipelined stages feed their respective outputs of terminal financial instrument prices and probability terms to a combinatorial logic stage that is configured to compute the theoretical fair market price based at least in part upon the terminal financial instrument prices and the probability terms.

100. The device of claim 90 wherein the options pricing engine is further configured to perform at least a portion of the theoretical fair market price computation with reconfigurable logic.

101. A computer-implemented method comprising:

receiving data describing an option on an underlying financial instrument; and

computing a theoretical fair market price for the option using a plurality of pipelined stages, the plurality of pipelined stages comprising a plurality of parallel computational nodes, each of said nodes being configured to compute a step of a stepwise option pricing model, and wherein said computational nodes are cascaded such that each successive pipelined stage receives as its input the output from at least two computational nodes in the immediately preceding pipelined stage, wherein the output of the last pipelined stage comprises the computed theoretical fair market price for the option.

102. The method of claim 101 wherein the option pricing model comprises a Cox, Ross and Rubinstein (CRR) option pricing model.

103. The method of claim 102 wherein the computing step further comprises providing a binomial tree of computational units, wherein the tree is of depth n.

104. The method of claim 103 further comprising partitioning the binomial tree across a plurality of processing resources.

105. The method of claim 104 wherein the plurality of processing resources comprise processing resources of different types.

106. The method of claim 105 wherein at least one of the processing resources comprises a reconfigurable logic device.

107. The method of claim 102 wherein the computing step further comprises computing a plurality of terminal financial instrument prices for the option pricing model with the pipelined stages based on an upward multiplicative factor and a downward multiplicative factor.

108. The method of claim 107 wherein the computing step further comprises computing a plurality of probability terms for the option pricing model with the pipelined stages.

109. The method of claim 108 wherein the computing step further comprises computing the terminal financial instrument prices and the probability terms in parallel.

110. The method of claim 108 wherein the computing step further comprises feeding the terminal financial instrument prices and the probability terms to a combinatorial logic stage that is configured to compute the theoretical fair market price based at least in part upon the terminal financial instrument prices and the probability terms.

111. The method of claim 101 further comprising performing the computing step in reconfigurable logic.

112. The method of claim 101 further comprising:

feeding the computed theoretical fair market price to a black box trading algorithm; and

automatically determining through the black box trading algorithm whether to execute a trade on the option based at least in part upon the theoretical fair market price fed thereto.

113. The method of claim 101 further comprising:

feeding the computed theoretical fair market price to an auto-quote server; and

automatically generating through the auto-quote server a price for an option quote based at least in part upon the theoretical fair market price fed thereto.

114. A device for computing a theoretical fair market price for a European option on an underlying financial instrument, the device comprising:

an options pricing engine that is configured to retrieve from a lookup table a plurality of precomputed terms for use in computing the theoretical fair market price, wherein the options pricing engine is configured to retrieve the plurality of precomputed terms using an index based at least in part upon a volatility for the underlying financial instrument.

115. The device of claim 114 wherein the options pricing engine comprises:

a first lookup table in which a plurality of volatility values for a plurality of different financial instruments are stored, wherein the first lookup table is indexed by financial instrument;

a second lookup table in which the plurality of precomputed terms are stored, wherein the second lookup table is indexed by a volatility for the underlying financial instrument and a time to maturity for the option;

a lookup unit that is configured to process data describing the option, access the first lookup table based on an identity of the underlying financial instrument for the option to thereby retrieve the volatility value for that underlying financial instrument, access the second lookup table based on the retrieved volatility value and a time to maturity for the option to thereby retrieve the plurality of precomputed terms applicable to that option; and

a combinatorial logic stage that is configured to compute the theoretical fair market price according to an option pricing model based at least in part upon the data describing the option, the retrieved volatility value and the retrieved precomputed terms.

116. The device of claim 115 wherein the option pricing model comprises a Cox, Ross and Rubinstein (CRR) option pricing model for computing a theoretical fair market price for a European option.

117. The device of claim 116 wherein at least the combinatorial logic stage is implemented in reconfigurable logic.

118. A computer-implemented method for computing a theoretical fair market price for a European option on an underlying financial instrument, the method comprising:

retrieving, based at least in part upon a volatility for the underlying financial instrument, a plurality of precomputed terms from a lookup table for use in computing the theoretical fair market price; and

computing the theoretical fair market price for the option based at least in part upon the retrieved precomputed terms.

119. The method of claim 118 further comprising:

receiving data describing the option, the data comprising an identity of the underlying financial instrument and a time to maturity for the financial instrument;

retrieving the volatility from another lookup table based on the identity of the underlying financial instrument, the another lookup table storing a plurality of volatility values for a plurality of different financial instruments, wherein the another lookup table is indexed by financial instrument; and

wherein the precomputed terms retrieving step further comprises retrieving the precomputed terms based at least in part upon the retrieved volatility and the time to maturity for the option.

120. The method of claim 119 wherein the option pricing model comprises a Cox, Ross and Rubinstein (CRR) option pricing model for European options.

121. The method of claim 120 further comprising performing at least the computing step with reconfigurable logic.

122. The method of claim 118 further comprising:

recomputing the precomputed terms according to at least one of the group consisting of a periodic basis and a triggered basis.

123. The method of claim 118 further comprising:

feeding the computed theoretical fair market price to a black box trading algorithm; and

automatically determining through the black box trading algorithm whether to execute a trade on the option based at least in part upon the theoretical fair market price fed thereto.

124. The method of claim 118 further comprising:

feeding the computed theoretical fair market price to an auto-quote server; and

automatically generating through the auto-quote server a price for an option quote based at least in part upon the theoretical fair market price fed thereto.