Internal Design of Asian Option Pricing Engine

Overview

The Asian option pricing engine uses Monte Carlo Simulation to estimate the value of the Asian option. Here, we assume the process of asset pricing applies to Black-Scholes process.

Asian Option is kind of exotic option. The payoff is path dependent and it is dependent on the average price of underlying asset over the settled period of time \(T\).

The payoff of Asian options is determined by the arithmetic or geometric average underlying price over some pre-set period of time. This is different from the case of usual European Option and American Option, where the payoff of the option depends on the price of the underlying at exercise. One advantage of Asian option is the relative cost of Asian option compared to American options. Because of the averaging feature, Asian options are typically cheaper than American options.

The average price of underlying asset could be used as strike price or the underlying settlement price at the expiry time. When the average price of underlying asset is used as the underlying settlement price at the expiry time, the payoff is calculated as follows:

payoff of put option = \(\max(0, K - A(T))\)

payoff of call option = \(\max(0, A(T) - K)\)

When the average price of underlying asset is used as the strike price at the expiry time, the payoff is calculated as follows:

payoff of put option = \(\max(0, A(T) - S_T)\)

payoff of call option = \(\max(0, S_T - A(T))\)

Where \(T\) is the time of maturity, \(A(T)\) is the average price of asset during time \(T\), \(K\) is the fixed strike, \(S_T\) is the price of underlying asset at maturity.

The average could be arithmetic or geometric, which is configurable. \(N\) is the number of discrete steps from \(0\) to \(T\).

Arithmetic average: \(A(T) = \frac{\sum_{i=0}^N S_i}{N}\)

Geometric average: \(A(T) = \sqrt[n]{\prod_{i=0}^N S_i}\)

MCAsianAPEngine

The pricing process of Asian Arithmetic Pricing engine is as follows:

  1. Generate independent stock paths by using Mersenne Twister Uniform MT19937 Random Number Generator (RNG) followed by Inverse Cumulative Normal Uniform Random Numbers.
  2. Start at \(t = 0\) and calculate the stock price of each path firstly in order to achieve initiation interval (II) = 1.
  3. Calculate arithmetic average and geometric average value of each path.
\[Price_{Arithmetic} = \frac{1}{M+1} * \sum_{i=0}^{M} (S(i\Delta t))\]
\[Price_{Geometric} = exp{( \frac{1}{M+1} * \sum_{i=0}^{M} (\log{S(i\Delta t)}) )}\]
  1. Calculate the payoff difference of arithmetic average price and geometric average price.
\[Payoff_{gap} = \max(0,Strike - Price_{Arithmetic}) - \max(0,Price_{Geometric} - Strike)\> for \> put \> options\]
\[Payoff_{gap} = \max(Strike - Price_{Arithmetic}, 0) - \max(Price_{Geometric} - Strike, 0)\> for \> call \> options\]
  1. Calculate the payoff off of geometric average price \(Payoff_{ref}\) based on analytical method.
  2. Payoff of average pricing is \(Payoff = Payoff_{ref} + Payoff_{gap}\)

The pricing architecture on FPGA can be shown as the following figure:

McAsianAPEngine pricing architecture on FPGA

MCAsianASEngine

The pricing process of Asian Arithmetic Strike engine is as follows:

  1. Generate independent stock paths by using Mersenne Twister Uniform MT19937 Random Number Generator (RNG) followed by Inverse Cumulative Normal Uniform Random Numbers.
  2. Start at \(t = 0\) and calculate the stock price of each path firstly in order to achieve II = 1.
  3. Accumulate the sum of lognormal stock price using the following analytical solution.
\[Price_{Arithmetic} = \frac{1}{M} * \sum_{i=0}^{M-1} (S_0*exp((i+1)*\]
\[(riskFreeRate - dividend - 0.5 * volatility^2)*dt + \sum_{j=0}^{i} volatility*\sqrt{dt}*dw_j))\]

where \(M\) is the total timesteps, \(dt\) is the time interval 4. Calculate the final payoff by taking the strike price \(Strike_t\) as the previous arithmetic average price \(\frac{\sum_{j=0}^i S_j}{M}\).

\[Payoff = \max(0,Strike_t - Price_t) \> for \> call \> options\]
\[Payoff = \max(0,Price_t - Strike_t) \> for \> put \> options\]

The pricing architecture on FPGA can be shown as the following figure:

McAsianASEngine pricing architecture on FPGA

MCAsianGPEngine

The pricing process of Asian Geometric Pricing engine is as follows:

  1. Generate independent stock paths by using Mersenne Twister Uniform MT19937 Random Number Generator (RNG) followed by Inverse Cumulative Normal Uniform Random Numbers.
  2. Start at \(t = 0\) and calculate the stock price of each path firstly in order to achieve II = 1.
  3. Transfer the geometric average of stock price to sum of lognormal stock price.
  4. Accumulate the sum of lognormal stock price using the following analytical solution.
\[Price_{Geometric} = S_0*exp((riskFreeRate - dividend - 0.5*volatility^2)dt)^\frac{n+1}{2} *\]
\[exp(\sum_{i=0}^{n-1} \frac{n-i}{n}*volatility*\sqrt{dt}*dw_i)\]
  1. Calculate the final payoff by using a fixed strike price.
\[Payoff = \max(0,Strike - Price_t) \> for \> call \> options\]
\[Payoff = \max(0,Price_t - Strike) \> for \> put \> options\]

The pricing architecture on FPGA can be shown as the following figure:

McAsianGPEngine pricing architecture on FPGA

Note

The 3 figures above shows the pricing part of McAsianAPEngine, McAsianASEngine and McAsianGPEngine respectively; the other parts, for example, PathGenerator, MCSimulation and other modules, are the same as in MCEuropeanEngine.

Profiling

The hardware resources are listed in Table 4. The Arithmetic and Geometric Asian Engines demand similar amount of resources.

Table 4 Hardware resources for single MCU
Engines BRAM DSP Register LUT Latency clock period(ns)
McAsianArithmeticAPEngine 12 59 24664 26826 53276 3.423
McAsianArithmeticASEngine 12 65 26683 29362 53196 3.423
McAsianGeometricAPEngine 10 61 24626 26657 53222 3.423

Table 5 shows the performance improvement in comparison with CPU-based Quantlib result (Tolerance = 0.02)

Table 5 Comparison between CPU and FPGA
Engines McAsianAPEngine McAsianASEngine McAsianGPEngine
SampNum 25951 33642 46805
CPU result 1.98441 3.10669 3.28924
CPU Execution time (us) 224911 310856 601068
FPGA result 1.89144 3.0866 3.26228
FPGA Kernel Execution time (us) 563.05 734.42 830.130
FPGA SampNum 28672 34816 49152
FPGA E2E Execution time (ms) 1 1 1
Number of MCM 2 2 4

Table 6 shows the max performance of McAsianEngines in one SLR of Xilinx xcu250-figd2104-2L-e (Vivado report).

Table 6 Hardware resources for max perforamnce
Engines BRAM DSP Register LUT Latency
Frequency
(MHz)
 Max Unroll Num
McAsianArithmeticAPEngine 192 749 246325 205626 54056 300 16
McAsianArithmeticASEngine 192 755 247524 207476 54109 300 16
McAsianGeometricAPEngine 160 781 242927 200106 54002 300 16