Zili Zhu

Pricing barrier and American options under the SABR model on the graphics processing unit

In this paper, we presented our study on using the graphics processing unit (GPU) to accelerate the computation in pricing financial options. We first introduced the GPU programming and the SABR stochastic volatility model. We then discussed pricing options with quasi Monte Carlo techniques under the SABR model. In particular, we focused on pricing barrier options by quasi Monte Carlo and conditional probability correction methods and on pricing American options by the least squares Monte Carlo method. We then presented our GPU‐based implementation for pricing barrier options and hybrid CPU–GPU implementation for pricing American options. In addition, we described techniques for efficient use of GPU memory. We provided details of implementing these GPU numerical schemes for pricing options and compared performances of the GPU programs with their CPU counterparts. We found that GPU‐based computing schemes can achieve 134 times speedup for pricing barrier options, while maintaining satisfactory pricing accuracy. For pricing American options, we also reported that when the least squares Monte Carlo method is used, special techniques can be devised to use less GPU memory, resulting in 22 times speedup, instead of the original 10 times speedup. Copyright

Option pricing with the SABR model on the GPU

In this paper, we will present our research on the acceleration for option pricing using Monte Carlo techniques on the GPU. We first introduce some basic ideas of GPU programming and then the stochastic volatility SABR model. Under the SABR model, we discuss option pricing with Monte Carlo techniques. In particular, we focus on European option pricing using quasi-Monte Carlo with the Brownian bridge method and American option pricing using the least squares Monte Carlo method. Next, we will study a GPU-based program for pricing European options and a hybrid CPU-GPU program for pricing American options. Finally, we implement our GPU programs, and compare their performance with their CPU counterparts. From our numerical results, around 100× speedup in European option pricing and 10× speedup in American option pricing can be achieved by GPU computing while maintaining satisfactory pricing accuracy.

Calibrating and Pricing with a Stochastic-Local Volatility Model

In this paper, we present our study on using the hybrid stochastic-local volatility (SLV) model for option pricing. The SLV model contains a stochastic volatility component represented by a volatility process and a local volatility component represented by a so-called leverage function. The leverage function can be roughly seen as a ratio between local volatility and conditional expectation of stochastic volatility. The difficulty of implementing the SLV model lies in the calibration of the leverage function. In this paper, we provide detailed discussion on the implementation of the calibration and pricing procedures. The implemented SLV model is used for pricing exotic options in the FX market. Pricing results are presented for the SLV model in direct comparison with the pure local volatility and pure stochastic volatility models. The SLV model is shown to match market traded implied volatility surfaces very well and to improve the pricing accuracy for market traded barrier options.

Switching to Non-Affine Stochastic Volatility: A Closed-Form Expansion for the Inverse Gamma Model

This paper introduces the Inverse Gamma (IGa) stochastic volatility model with time-dependent parameters, defined by the volatility dynamics

A Hybrid Stochastic Volatility Model Incorporating Local Volatility

dV_{t}=\kappa_{t}\left(\theta_{t}-V_{t}\right)dt+\lambda_{t}V_{t}dB_{t}

Sharp Target Range Strategies with Application to Dynamic Portfolio Selection

. This non-affine model is much more realistic than classical affine models like the Heston stochastic volatility model, even though both are as parsimonious (only four stochastic parameters). Indeed, it provides more realistic volatility distribution and volatility paths, which translate in practice into more robust calibration and better hedging accuracy, explaining its popularity among practitioners. In order to price vanilla options with IGa volatility, we propose a closed-form volatility-of-volatility expansion. Specifically, the price of a European put option with IGa volatility is approximated by a Black-Scholes price plus a weighted combination of Black-Scholes greeks, where the weights depend only on the four time-dependent parameters of the model. This closed-form pricing method allows for very fast pricing and calibration to market data. The overall quality of the approximation is very good, as shown by several calibration tests on real-world market data where expansion prices are compared favorably with Monte Carlo simulation results. This paper shows that the IGa model is as simple, more realistic, easier to implement and faster to calibrate than classical transform-based affine models. We therefore hope that the present work will foster further research on non-affine models like the Inverse Gamma stochastic volatility model, all the more so as this robust model is of great interest to the industry.

Dynamic Portfolio Optimisation with Intermediate Costs: A Least-Squares Monte Carlo Simulation Approach

In this paper, we present our study on a hybrid stochastic volatility model incorporating local volatility for pricing options in the foreign exchange (FX) market. The hybrid stochastic-local volatility model (SLV) could match the implied volatility surface well and meanwhile shows the flexibility for pricing exotic options. The difficulty in implementing the SLV model lies in the calibration of the leverage function, which can be roughly seen as a ratio between the local volatility and the conditional expectation of stochastic volatility. We will illustrate our implementation of the SLV model and show the pricing performance for exotic options.

USING EXOTIC OPTION PRICES AS CONTROL VARIATES IN MONTE CARLO PRICING UNDER A LOCAL-STOCHASTIC VOLATILITY MODEL

A family of sharp target range strategies is presented for portfolio selection problems. Our proposed strategy maximizes the expected portfolio value within a target range, composed of a conservative lower target representing capital guarantee and a desired upper target representing investment goal. This strategy favorably shapes the entire probability distribution of return, as it simultaneously seeks a high expected return, cuts off downside risk, and implicitly caps volatility, skewness and other higher moments of the return distribution. To illustrate the effectiveness of our new investment strategies, we study a multi-period portfolio selection problem with transaction cost, where the results are generated by the Least-Squares Monte-Carlo algorithm. Our numerical tests show that the presented strategy produces a better efficient frontier, a better trade-off between return and downside risk, and a wider range of possible risk profiles than classical constant relative risk aversion utility. Finally, straightforward extensions of the sharp target range are presented, such as purely maximizing the probability of achieving the target range, adding an explicit target range for realized volatility, and defining the range bounds as excess return over a stochastic benchmark, for example, stock index or inflation rate. These practical extensions make the approach applicable to a wide array of investment funds, including pension funds, controlled-volatility funds, and index-tracking funds.In this paper, we propose a novel investment strategy for portfolio optimization problems. The proposed strategy maximizes the expected portfolio value bounded within a targeted range, composed of a conservative lower target representing a need for capital protection and a desired upper target representing an investment goal. This strategy favorably shapes the entire probability distribution of returns, as it simultaneously seeks a desired expected return, cuts off downside risk and implicitly caps volatility and higher moments. To illustrate the effectiveness of this investment strategy, we study a multiperiod portfolio optimization problem with transaction costs and develop a two-stage regression approach that improves the classical least squares Monte Carlo (LSMC) algorithm when dealing with difficult payoffs, such as highly concave, abruptly changing or discontinuous functions. Our numerical results show substantial improvements over the classical LSMC algorithm for both the constant relative risk-aversion (CRRA) utility approach and the proposed skewed target range strategy (STRS). Our numerical results illustrate the ability of the STRS to contain the portfolio value within the targeted range. When compared with the CRRA utility approach, the STRS achieves a similar mean–variance efficient frontier while delivering a better downside risk–return trade-off.

Pricing window barrier options with a hybrid stochastic-local volatility model

We present a simulation-and-regression method for solving dynamic portfolio optimization problems in the presence of general transaction costs, liquidity costs and market impact. This method extends the classical least squares Monte Carlo algorithm to incorporate switching costs, corresponding to transaction costs and transient liquidity costs, as well as multiple endogenous state variables, namely the portfolio value and the asset prices subject to permanent market impact. To handle endogenous state variables, we adapt a control randomization approach to portfolio optimization problems and further improve the numerical accuracy of this technique for the case of discrete controls. We validate our modified numerical method by solving a realistic cash-and-stock portfolio with a power-law liquidity model. We identify the certainty equivalent losses associated with ignoring liquidity effects, and illustrate how our dynamic optimization method protects the investors capital under illiquid market conditions. Lastly, we analyze, under different liquidity conditions, the sensitivities of certainty equivalent returns and optimal allocations with respect to trading volume, stock price volatility, initial investment amount, risk aversion level and investment horizon.

A scenario-based integrated approach for modeling carbon price risk

A Monte Carlo numerical method is developed for using a hybrid localstochastic volatility (LSV) model to price exotic options, in particular barrier options. We use market traded data on AUD/USD and AUD/JPY to benchmark the accuracy of the implemented method for pricing barrier options traded in the global foreign-exchange options market. We show that the implemented LSV model can accurately reproduce market implied volatilities for vanilla options. A PDE pricing engine for the LSV model has been used as the benchmark method to examine the performance of the Monte Carlo engine for which two different control variates are incorporated to reduce pricing variances in the Monte Carlo results. The numerical results shown in this paper suggest that the best improvement in accuracy is obtained when utilising market-traded exotic options as control variates to price other exotic options, whereas using vanilla options as conventional control variates is not as effective, because the exotic option control variate can provide additional market information to correct the LSV model error.