scholarly journals Variance and Dimension Reduction Monte Carlo Method for Pricing European Multi-Asset Options with Stochastic Volatilities

2019 ◽  
Vol 11 (3) ◽  
pp. 815 ◽  
Author(s):  
Yijuan Liang ◽  
Xiuchuan Xu
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Convergence Rates ◽  
Option Price ◽  
Correlation Coefficients ◽  
Control Variate ◽  
Stochastic Volatilities ◽  
Conditional Monte Carlo ◽  
Mc Simulation ◽  
Martingale Representation Theorem

Pricing multi-asset options has always been one of the key problems in financial engineering because of their high dimensionality and the low convergence rates of pricing algorithms. This paper studies a method to accelerate Monte Carlo (MC) simulations for pricing multi-asset options with stochastic volatilities. First, a conditional Monte Carlo (CMC) pricing formula is constructed to reduce the dimension and variance of the MC simulation. Then, an efficient martingale control variate (CV), based on the martingale representation theorem, is designed by selecting volatility parameters in the approximated option price for further variance reduction. Numerical tests illustrated the sensitivity of the CMC method to correlation coefficients and the effectiveness and robustness of our martingale CV method. The idea in this paper is also applicable for the valuation of other derivatives with stochastic volatility.

scholarly journals PENENTUAN HARGA OPSI BELI TIPE ASIA DENGAN METODE MONTE CARLO-CONTROL VARIATE

2017 ◽  
Vol 6 (1) ◽  
pp. 29
Author(s):  
NI NYOMAN AYU ARTANADI ◽  
KOMANG DHARMAWAN ◽  
KETUT JAYANEGARA
Keyword(s):  
Monte Carlo ◽  
Standard Error ◽  
Asset Price ◽  
Option Price ◽  
Asian Option ◽  
Underlying Asset ◽  
Average Value ◽  
Control Variate ◽  
Exercise Price ◽  
Maturity Date

Option is a contract between the writer and the holder which entitles the holder to buy or sell an underlying asset at the maturity date for a specified price known as an exercise price. Asian option is a type of financial derivatives which the payoff taking the average value over the time series of the asset price. The aim of the study is to present the Monte Carlo-Control Variate as an extension of Standard Monte Carlo applied on the calculation of the Asian option price. Standard Monte Carlo simulations 10.000.000 generate standard error 0.06 and the option price convergent at Rp.160.00 while Monte Carlo-Control Variate simulations 100.000 generate standard error 0.01 and the option price convergent at Rp.152.00. This shows the Monte Carlo-Control Variate achieve faster option price toward convergent of the Monte Carlo Standar.

scholarly journals On Multilevel and Control Variate Monte Carlo Methods for Option Pricing under the Rough Heston Model

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2930
Author(s):  
Siow Woon Jeng ◽  
Adem Kiliçman
Keyword(s):  
Monte Carlo ◽  
Stock Price ◽  
Variance Reduction ◽  
Volterra Equation ◽  
Price Volatility ◽  
Multilevel Methods ◽  
Heston Model ◽  
Control Variate ◽  
Stochastic Volterra Equation ◽  
And Control

The rough Heston model is a form of a stochastic Volterra equation, which was proposed to model stock price volatility. It captures some important qualities that can be observed in the financial market—highly endogenous, statistical arbitrages prevention, liquidity asymmetry, and metaorders. Unlike stochastic differential equation, the stochastic Volterra equation is extremely computationally expensive to simulate. In other words, it is difficult to compute option prices under the rough Heston model by conventional Monte Carlo simulation. In this paper, we prove that Euler’s discretization method for the stochastic Volterra equation with non-Lipschitz diffusion coefficient error[|Vt−Vtn|p] is finitely bounded by an exponential function of t. Furthermore, the weak error |error[Vt−Vtn]| and convergence for the stochastic Volterra equation are proven at the rate of O(n−H). In addition, we propose a mixed Monte Carlo method, using the control variate and multilevel methods. The numerical experiments indicate that the proposed method is capable of achieving a substantial cost-adjusted variance reduction up to 17 times, and it is better than its predecessor individual methods in terms of cost-adjusted performance. Due to the cost-adjusted basis for our numerical experiment, the result also indicates a high possibility of potential use in practice.

scholarly journals Examining the Efficiency of American Put Option Pricing by Monte Carlo Methods with Variance Reduction

2018 ◽  
Vol 10 (2) ◽  
pp. 10
Author(s):  
George Chang
Keyword(s):  
Monte Carlo ◽  
Option Pricing ◽  
Variance Reduction ◽  
American Options ◽  
Balance Sheet ◽  
Put Options ◽  
Control Variate ◽  
American Put Option ◽  
Put Option ◽  
American Put

We apply the Monte Carlo simulation algorithm developed by Broadie and Glasserman (1997) and the control variate technique first introduced to asset pricing via simulation by Boyle (1977) to examine the efficiency of American put option pricing via this combined method. The importance and effectiveness of variance reduction is clearly demonstrated in our simulation results. We also found that the control variates technique does not work as well for deep-in-the-money American put options. This is because deep-in-the-money American options are more likely to be exercised early, thus the value of the American options are less in line (or less correlated) with those of their European counterparts. the same FPESS can also be observed when investigators partition large datasets into smaller datasets to address a variety of auditing questions. In this study, we fill the empirical gap in the literature by investigating the sensitivity of the FPESS to partitioned datasets. We randomly selected 16 balance-sheet datasets from: China Stock Market Financial Statements Database™, that tested to be Benford Conforming noted as RBCD. We then explore how partitioning these datasets affects the FPESS by repeated randomly sampling: first 10% of the RBCD and then selecting 250 observations from the RBCD. This created two partitioned groups of 160 datasets each. The Statistical profile observed was: For the RBCD there were no indications of Non-Conformity; for the 10%-Sample there were no overall indications that Extended Procedures would be warranted; and for the 250-Sample there were a number of indications that the dataset was Non-Conforming. This demonstrated clearly that small datasets are indeed likely to create the FPESS. We offer a discussion of these results with implications for audits in the Big-Data context where the audit In-charge would find it necessary to partition the datasets of the client. 

scholarly journals PERBANDINGAN KEKONVERGENAN METODE CONDITIONAL MONTE CARLO DAN ANTITHETIC VARIATE DALAM MENENTUKAN HARGA OPSI CALL TIPE BARRIER

2018 ◽  
Vol 7 (3) ◽  
pp. 271
Author(s):  
NI LUH PUTU KARTIKA WATI ◽  
KOMANG DHARMAWAN ◽  
KARTIKA SARI
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Stock Price ◽  
Option Price ◽  
Life Time ◽  
Barrier Option ◽  
Strike Price ◽  
Stock Volatility ◽  
Volatility Risk ◽  
Conditional Monte Carlo

Barrier option is an option where the payoff price depends  on whether or not the stock price passes the barrier during its life time. The aim of the research is to compare the convergence between conditional Monte Carlo and antithetic variate methods in determining the call barrier option  price. The call barrier option price  is influenced by several factors: initial stock price, stock volatility, risk-free interest rate, maturity, strike price and barrier. The calculation of call barrier option price is obtained by simulating stock price movements with different simulation number. Based on the simulation result, it is obtained that the calculation of call barrier option price with conditional Monte Carlo method converge faster than the antithetic variate method.

Green Simulation with Database Monte Carlo

2021 ◽  
Vol 31 (1) ◽  
pp. 1-26
Author(s):  
Mingbin Feng ◽  
Jeremy Staum
Keyword(s):  
Monte Carlo ◽  
Computational Efficiency ◽  
Numerical Experiments ◽  
Variance Reduction ◽  
Idle Time ◽  
General Strategy ◽  
Control Variate ◽  
Simulation Procedure ◽  
Computational Budget ◽  
Over Time

In a setting in which experiments are performed repeatedly with the same simulation model, green simulation means reusing outputs from previous experiments to answer the question currently being asked of the model. In this article, we address the setting in which experiments are run to answer questions quickly, with a time limit providing a fixed computational budget, and then idle time is available for further experimentation before the next question is asked. The general strategy is database Monte Carlo for green simulation: the output of experiments is stored in a database and used to improve the computational efficiency of future experiments. In this article, the database provides a quasi-control variate, which reduces the variance of the estimated mean response in a future experiment that has a fixed computational budget. We propose a particular green simulation procedure using quasi-control variates, addressing practical issues such as experiment design, and analyze its theoretical properties. We show that, under some conditions, the variance of the estimated mean response in an experiment with a fixed computational budget drops to zero over a sequence of repeated experiments, as more and more idle time is invested in creating databases. Our numerical experiments on the procedure show that using idle time to create databases of simulation output provides variance reduction immediately, and that the variance reduction grows over time in a way that is consistent with the convergence analysis.

scholarly journals Penentuan Harga Opsi Asia dengan Metode Monte Carlo

2017 ◽  
Vol 3 (1) ◽  
pp. 44-48
Author(s):  
Surya Amami Pramuditya
Keyword(s):  
Monte Carlo ◽  
Confidence Interval ◽  
Stock Price ◽  
Variance Reduction ◽  
Option Price ◽  
Numerical Procedure ◽  
Reduction Technique ◽  
Asian Options ◽  
Call Option ◽  
Strike Price

An option is a contract between a holder and a writer in which the writer grants the rights (not obligations) to the holder to buy or sell the assets of the writer at a certain price (strike price) at maturity time. Asian options are included in the dependent path option. This means that Asia's payoff option depends not only on the stock price at maturity time, but it is the average stock price during its maturity and symbolized A (average). Monte Carlo is basically used as a numerical procedure to estimate the expected value of pricing product derivatives. The techniques used are the standard Monte Carlo and variance reduction. The result obtained the Asia call option price and put for both techniques with 95% confidence interval. The variance reduction technique looks faster reducing 95% confidence interval than standard method.

CONDITIONAL MONTE CARLO SCHEME FOR STABLE GREEKS OF WORST-OF AUTOCALLABLE NOTES

2019 ◽  
Vol 22 (06) ◽  
pp. 1950028
Author(s):  
FIRUZ RAKHMONOV ◽  
PARVIZ RAKHMONOV
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Variance Reduction ◽  
Computational Effort ◽  
Early Termination ◽  
Special Structure ◽  
Monte Carlo Algorithm ◽  
Suggested Algorithm ◽  
Conditional Monte Carlo

It is well known that the application of Monte Carlo method in pricing of products with early termination feature results in a high Monte Carlo error and unstable greeks; see Fries & Joshi (2011). We develop a Monte Carlo scheme that utilizes a special structure of worst-of autocallable notes and produces stable greeks. This scheme clearly demonstrates the variance reduction in Monte Carlo scheme and can be used in pricing of multi-asset worst-of autocallable notes with any number of underlying assets. We suggest an algorithm and analyze its performance for an autocallable note on four assets. The suggested algorithm allows one to calculate stable greeks (delta, gamma, vega and others) and substantially reduce the computational effort to achieve the desired accuracy in comparison to standard Monte Carlo algorithm.

scholarly journals MENENTUKAN HARGA OPSI DENGAN METODE MONTE CARLO BERSYARAT MENGGUNAKAN BARISAN KUASI ACAK FAURE

2021 ◽  
Vol 10 (3) ◽  
pp. 141
Author(s):  
PUTU WIDYA ASTUTI ◽  
KOMANG DHARMAWAN ◽  
KARTIKA SARI
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Random Sequence ◽  
Random Number Generator ◽  
Option Price ◽  
Call Option ◽  
Standard Distribution ◽  
Real Value ◽  
Conditional Monte Carlo ◽  
The Right

An option contract is a contract that gives the owner the right to sell or even to buy an asset at the predetermined price and period time. The conditional Monte Carlo is one of the several methods that is used to determine the option price which in the process uses random numbers with normal standard distribution. At the same time, the random number generator can be substituted by using a quasi-random sequence, as in Faure's quasi-random sequence. The aim of this study is to determine the contract price of the call option with the European type by applying the conditional Monte Carlo method. This method used the Faure quasi-random sequence and compared it with the method of Monte Carlo standard, Monte Carlo standard in using the quasi-random sequence of Faure, and conditional Monte Carlo. The results of this study showed that the call option calculated using the conditional Monte Carlo method using the quasi-random Faure sequence began to stabilize at the 5000th simulation for K = 32575 and K = 34725 and in the 10000th simulation for K = 33000 and K = 33950. Research also show that with the conditional Monte Carlo in using the quasi-random sequence of Faure is more stable. Therefore, it is obtained its real value faster than the Monte Carlo standard, Monte Carlo standard in using the quasi-random sequence of Faure, and conditional Monte Carlo. The MAPE value of conditional Monte Carlo in using the quasi-random sequences of Faure and the Monte Carlo standard is smaller than the Monte Carlo standard in using the quasi-random sequence of Faure, and conditional Monte Carlo. Therefore, it can be said to be more accurate when calculating the European type call option price at BBCA.JK stocks.

EXACT SIMULATION OF THE 3/2 MODEL

2012 ◽  
Vol 15 (05) ◽  
pp. 1250032 ◽  
Author(s):  
JAN BALDEAUX
Keyword(s):  
Monte Carlo ◽  
Stock Price ◽  
Variance Reduction ◽  
Lie Symmetry Analysis ◽  
Exact Simulation ◽  
Quasi Monte Carlo ◽  
Monte Carlo Techniques ◽  
Price Process ◽  
Reduction Techniques ◽  
Conditional Monte Carlo

This paper discusses the exact simulation of the stock price process underlying the 3/2 model. Using a result derived by Craddock and Lennox using Lie Symmetry Analysis, we adapt the Broadie-Kaya algorithm for the simulation of affine processes to the 3/2 model. We also discuss variance reduction techniques and find that conditional Monte Carlo techniques combined with quasi-Monte Carlo point sets result in significant variance reductions.

scholarly journals Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games Using Baselines

2019 ◽  
Vol 33 ◽  
pp. 2157-2164
Author(s):  
Martin Schmid ◽  
Neil Burch ◽  
Marc Lanctot ◽  
Matej Moravcik ◽  
Rudolf Kadlec ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Learning Strategies ◽  
Imperfect Information ◽  
Variance Reduction ◽  
Convergence Rates ◽  
Regret Minimization ◽  
State Action ◽  
Policy Gradient ◽  
Order Of Magnitude ◽  
New Formulation

Learning strategies for imperfect information games from samples of interaction is a challenging problem. A common method for this setting, Monte Carlo Counterfactual Regret Minimization (MCCFR), can have slow long-term convergence rates due to high variance. In this paper, we introduce a variance reduction technique (VR-MCCFR) that applies to any sampling variant of MCCFR. Using this technique, periteration estimated values and updates are reformulated as a function of sampled values and state-action baselines, similar to their use in policy gradient reinforcement learning. The new formulation allows estimates to be bootstrapped from other estimates within the same episode, propagating the benefits of baselines along the sampled trajectory; the estimates remain unbiased even when bootstrapping from other estimates. Finally, we show that given a perfect baseline, the variance of the value estimates can be reduced to zero. Experimental evaluation shows that VR-MCCFR brings an order of magnitude speedup, while the empirical variance decreases by three orders of magnitude. The decreased variance allows for the first time CFR+ to be used with sampling, increasing the speedup to two orders of magnitude.

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