Introduction
The Monte Carlo Option Model method was really established by Stanislaw Ulam and John von Neumann while working on the Manhattan Project. The Manhattan undertaking is the undertaking that develops the first atomic arm during World War II. The Monte Carlo method helps to work out a job by exciting straight to the physical procedure, and is non necessary to compose down the differential equations that describe the behavior of the system.B. Ricky Rambharat_and Anthony Brockwell ( 2006 ) mentioned that the Monte Carlo method is a really general procedure and is a valid attack in scientific countries such as natural philosophies, chemical science and computing machine scientific discipline. In mathematical finance, a Monte Carlo option theoretical account uses Monte Carlo methods to cipher the value of an option with multiple beginnings of uncertainness or with complicated characteristics.
The Monte Carlo Option Model can be used to cipher some type of option. For illustration, the option that relate to assorted beginnings of uncertainness and ciphering their values with other theoretical account that is hard. In add-on, Claudia Ribeiro and Nick Webber ( 2003 ) indicated that the Monte Carlo Option Model besides can be used to cipher the option when that exist in the market but have really complicated characteristics. Besides, Michael B. Giles ( 2009 ) highlighted that other option is when the arbitrage-free rating of a definite derived function that consists of a big figure of dimensions.
BACKGROUND OF MONTE CARLO OPTION MODEL
In twelvemonth 1930, an Italian physict who name is Enrico Fermi ; he is the first laminitis of the Monte Carlo Option Model. Boyle ( 1977 ) stated that in twelvemonth 1946, an American mathematician who name is Stanislaw Ulam had besides found the Monte Carlo Option Model. A Grecian American Physicist, Nicholas Metropolis had given the name of the theoretical account as “ Monte Carlo ” . Herath and Park ( 2001 ) explained that the theoretical account was intended to calculate the value of peculiar option invented the technique and work outing certain types of differential equation utilizing probabilistic methods by utilizing the Monte Carlo Methods. Phelim Boyle is the first applier of utilizing this option pricing which was applies to cipher the value of European option in twelvemonth 1977. M. Broadie and P. Glasserman demo how to treat and monetary value the Asiatic securities by utilizing the Monte Carlo Option Model. In twelvemonth 2001, E.S.Schwartz and F.A.Longstaff discovered the procedure and the theoretical account were applied for finding the values of American option.
Jamison ( 1999 ) indicated that the Monte Carlo simulation is a method for measuring a deterministic theoretical account utilizing sets of random figure as inputs. This method is used when the theoretical account is complex, nonlinear or involves more than a twosome of unsure parametric quantities. In the yesteryear, Schwartz ( 1977 ) suggested that the simulation merely practically is utilizing ace computing machines. Mark S. Joshi ( 2006 ) indicated that the Monte Carlo simulation methods are particularly utile in imitating assorted types of uncertainness in inputs and with complicated characteristics which would do them hard to value. Chitro Majumdar ( 2005 ) discovered that specific countries of application include physical scientific disciplines, design and visuals, finance and concern, telecommunication and games. The theoretical account besides used on the Manhattan Project, towards the development of atomic arms in the yesteryear.
In theory, Monte Carlo rating rely on hazard impersonal rating where the monetary value of the option in its discounted value. In add-on, M R Samis, G A Davis and D G Laughton ( 2007 ) mentioned that in order to cipher the associated exercising value of the option for illustration the final payment, it had to use the theoretical account to bring forth several possible monetary values via simulation. The final payment are so averaged and discounted. The consequence is the value of the option. Besides, some of the theory allows the increasing of complexness. For illustration, Barraquand and Martineau ( 1995 ) developed the option of equity is one of the attacks that may pattern with one beginning of uncertainness.
Wim Schoutens and Stijn Symens ( 2002 ) highlighted that in mathematical finance field, the Monte Carlo Option Model uses the Monte Carlo methods to cipher the value of an option with multiple beginnings of uncertainness, to value and analyze instruments, portfolio and investing by imitating assorted types of uncertainness or with complicated characteristics through the straightforward Black-Scholes procedure.
Harmonizing to Aniela Karina Iancu, M.S. ( 2004 ) The Monte Carlo Option Method is used to cipher the options that relate to assorted beginning of uncertainness and ciphering their values with other theoretical accounts is hard like the Monte Carlo simulation which can be used to value options where the final payment depends on the value of multiple underlying assets such as the Basket option or the Rainbow option. Options that exist in the market but have really complicated characteristics and arbitrage-free rating of a definite derived function that consists of immense figure of dimensions.
Sergey A. Maidanov ( 2000 ) indicated that the Brownian gesture is one of the signifiers of
Markov processes used as a theoretical account for stock monetary value motions as far back as in 1900 by L. Bachelier. The Brownian gesture theoretical account states that the value S of a security follows the Stochastic procedure
darmstadtium = I?Sdt +I?SdW
I? = the impetus rate
I? = the volatility
W ( T ) =variable is a standard Brownian gesture
dt = clip increase
At t = 0, the value S ( 0 ) is S0
Literature Review
Marius Holtan ( 2002 ) highlighted that the progress of engineering had made the Monte Carlo simulation fast going the pick for measuring and analysing assets, be it pure fiscal derived functions or investings in existent assets. Sibel Kaplan ( 2008 ) mentioned that the Monte Carlo method is a technique that involves utilizing random Numberss and chance to work out jobs. Harmonizing to the John R.Birge ( 1995 ) , Monte Carlo can be used to stand for many random phenomena that can act upon the option monetary value. Nowadays, sensible random Numberss generators can be found in every scheduling linguistic communication. Aniela Karina Iancu ( 2004 ) stated that the Monte-Carlo simulation is besides an attack that can suit complex final payments, stochastic volatility and variable involvement rates. John R.Birge ( 1995 ) highlighted that the Monte Carlo method has its basic in statistical theory, in peculiar, the cardinal bound theorem that allows the computation of assurance intervals about the average value of a random variable based on a sequence of independent random observations. Other than that, Victor Podlozhnyuk & A ; Mark Harris ( 2007 ) , indicated that the Monte Carlo option pricing is “ embarrassingly parallel ” , because the pricing of each option is independent of all others. Barry R. Cobb & A ; John M. Charnes ( 2007 ) surveies describes the usage of Monte Carlo simulation and stochastic optimisations for the rating of existent options arise from the abilities of directors to act upon the hard currency flows of the undertakings under their control. Harmonizing to Chitro Majumdar ( 2005 ) , the Monte Carlo simulation in calculating theoretical account is based on choosing a random value for each of the undertaking, based on the scope of estimations.
Restrictions of Monte Carlo Simulation
Susana Alonso Bonis ( 2006 ) , indicated that the restriction of the Monte Carlo attack is that it can be used merely for European manner derived functions securities and Monte Carlo simulation can non manage early exercising since there is no manner of cognizing whether early exercising is optimum when a peculiar monetary value is reached at a peculiar clip. Susana Alonso Bonis ( 2006 ) besides stated that the Monte Carlo simulation is a forward initiation process, which generates future values of the variable from its old value and therefore is non suited for valuing assets bring forthing hard currency flows contingent on future events, such as is the instance of American-type options. However, Aniela Karina Iancu, M.S. ( 2004 ) mentioned that even we being given high velocity computing machines, the method is clip devouring, as both n and N have to be really big to give good estimations for the option monetary value. In add-on, John R.Birge ( 1995 ) mentioned that the Monte Carlo simulation ‘s cogency of a pseudo-random sequence for statistical entropy is slightly questionable.
Advantages of Monte Carlo Simulation
Marius Holtan ( 2002 ) highlighted that the two of the chief virtuousnesss of simulation are flexibleness and simpleness. Marius Holtan ( 2002 ) the simulation is besides easy to implement and theoretical accounts can easy be constructed in spreadsheet bundles. Michael J. Sanislo P.E ( 2003 ) indicated that the Monte Carlo simulation become proficient at acknowledging chances with high market hazard and governable unique hazard. Michael J. Sanislo, P.E. ( 2003 ) mentioned that the Monte Carlo simulation can assist to divide the unique hazard and market hazard. Besides, John R.Birge ( 1995 ) mentioned that even we presuming the entropy, the mistake from cardinal bound statements decrease easy, reciprocally relative to the square root of the figure of observations. Hongbin Zhang ( 2009 ) indicated that the disadvantage of utilizing Monte Carlo methods for way dependant options is the big figure of computations that are necessary to update the way dependant variables throughout the simulation. N Bolia and S Juneja ( 2005 ) stated that even the Monte Carlo techniques can be rather slow as the problem-size additions, actuating research in discrepancy decrease techniques to increase the efficiency of the simulations. Xiang Tian, Khaled Benkrid, and Xiaochen Gu ( 2008 ) highlighted that the computational clip of the Monte Carlo simulations increases about linearly with figure of variables, whereas in most other methods, computational clip additions exponentially with the figure of variables. In add-on, the multiple independent waies are computed by utilizing the correspondence which is one of the most of import features of the Monte Carlo simulation.
Uses of Monte Carlo Simulation
The Fieldss that can utilize the Monte Carlo simulation are spread outing production capacity, constructing new workss, existent investing job or investing in IT. Yongzheng Lai & A ; Jerome Spanier ( 2003 ) indicated that Monte Carlo simulation is besides a method used to understand the impact of hazard and the uncertainness in fiscal, undertaking direction, cost, and other prediction theoretical accounts. Xiang Tian, Khaled Benkrid, and Xiaochen Gu ( 2008 ) indicated that Monte Carlo simulation besides used to calculate a broad scope of events and scenarios, such as the conditions, merchandise gross revenues and consumer demand.Claudia Ribeiro ( 2003 ) surveies shown that Monte Carlo option can used in determine uncertainness prediction theoretical account which the prediction theoretical account is the theoretical account that plan in front for the hereafter.There are some premises that can find utilizing Monte Carlo simulation in the prediction theoretical account. For illustration the investings return on a portfolio, the cost of a building undertaking or the length of period that the undertaking will be completed.
Examples of Monte Carlo Simulation
For illustration of the instance: To gauge the entire clip completion of the period of the undertaking. In this instance, it ‘s a building undertaking, with three parts. The parts have to be done one after the other, so the entire clip for the undertaking will be the amount of the three parts. All the times are in months.
Undertaking
Time Estimate
Job 1
5 Calendar months
Job 2
4 Calendar months
Job 3
5 Calendar months
Entire
14 Calendar months
Table1: Basic Forecasting Model
Undertaking
Minimum
Most Likely
Maximum
Job 1
4 Calendar months
5 Calendar months
7 Calendar months
Job 2
3 Calendar months
4 Calendar months
6 Calendar months
Job 3
4 Calendar months
5 Calendar months
6 Calendar months
Entire
11 Calendar months
14 Calendar months
19 Calendar months
Table 2: Prediction Model Using Range Estimates
Time
Number of Times ( Out of 500 )
Percentage of Total ( Rounded )
12 Calendar months
1
0 %
13 Calendar months
31
6 %
14 Calendar months
171
34 %
15 Calendar months
394
79 %
16 Calendar months
482
96 %
17 Calendar months
499
100 %
18 Calendar months
500
100 %
Table 3: Consequences of a Monte Carlo Simulation
The original estimation for the “ most likely ” , or expected instance, was 14 months. From Monte Carlo simulation, nevertheless, we can see that out of 500 tests utilizing random values, the entire clip was 14 months or less in merely 34 % of the instances.
Decision: In the simulation there is merely a 34 % opportunity, it about 1 out of 3 which that any single test will ensue in a entire clip of 14 months or less. On the other manus, there is a 79 % opportunity that the undertaking will be completed during 15 months. Further, the theoretical account demonstrates that it is highly improbable, in the simulation, which we will of all time fall at the absolute lower limit or maximal entire values. This demonstrates the hazard in the theoretical account. Based on this information, we might do different picks when be aftering the undertaking.
Related Theory
Black Box Testing Model
In the Monte Carlo option, the related theory that related to the theoretical account is the Black Box Testing Model. Harmonizing to Umar Saeed and Ansur Mahmood Amjad ( 2009 ) , it is the computing machine plan which user enters the information and the system utilized the logic to organize an end product to the users. Umar Saeed and Ansur Mahmood Amjad ( 2009 ) mentioned when the logic is form, the part of the system creates expressions and computations for the user to utilize the system. Regard to Umar Saeed and Ansur Mahmood Amjad ( 2009 ) the system frequently used to find optimum trading and patterns that generate many different types of information including bargain and sell signal.
Harmonizing to Umar Saeed and Ansur Mahmood Amjad ( 2009 ) the black box theoretical account is recommended by International Software Testing Qualification Board ( ISTQB ) and it is of import and uses to find the defects determination, edification, defect guesswork, attempt and quality of trial instances. Regard to Umar Saeed and Ansur Mahmood Amjad ( 2009 ) , the black box proving schemes and techniques are of import in equality breakdown, boundary value analysis ( BVA ) , determination tabular array based testing, categorization tree method, instances based testing and province diagram based proving. Umar Saeed and Ansur Mahmood Amjad ( 2009 ) said that there are all indispensable and often used black box proving schemes ( BBTS ) in package forming presents. Umar Saeed and Ansur Mahmood Amjad ( 2009 ) mentioned the boundary value analysis is a black box proving techniques to place trial instances. It is use to rectify the critical mistake in the input or end product of boundaries by mechanics computation or manipulated the information with an aim. Umar Saeed and Ansur Mahmood Amjad ( 2009 ) stated the system developed that cause bugs and the bugs catch the mistake in the system, the two variables x1, x2 of map input and their scope are as follow: .
E.g. F ( x1, x2 ) = 2×1+x22
For the trial instance choice, Umar Saeed and Ansur Mahmood Amjad ( 2009 ) stated that there are individual versus multiple scope look intoing mistake which to determined the figure of instances, for the individual valuable, the boundary-adjacent values are helpful to exert the plan checking logic which, may utilize “ & lt ; ” , “ & gt ; ” or “ & lt ; = ” on most keyboard layout. Umar Saeed and Ansur Mahmood Amjad ( 2009 ) mentioned the set of trial instances selected at the lower limit would be the value with the baseline where x+ is value greater than x and y- is value less than Y and the omega is nominal value which lies between x+ and y- .
E.g. [ x- , x, x+ , omega, y- , Y, y+ ]
Umar Saeed and Ansur Mahmood Amjad ( 2009 ) highlighted that there is besides holding the multiple sub-ranges for individual variable which will be the brotherhood of two set of trial instances as given by the followers:
Lbaseline = { a, a+ , vitamin E, b- , B } U { B, b+ , degree Fahrenheit, d- , 500 } = { a, a+ , vitamin E, b- , B, b+ , degree Fahrenheit, d- , 500 }
For multiple variables, Umar Saeed and Ansur Mahmood Amjad ( 2009 ) stated that there will be a individual mistake theoretical account, which has two inputs X and Y, is failure due to the low chance of mistakes happening at the same time and the baseline individual variable trial instance are as follow:
Xbaseline = { a, a+ , B, c- , degree Celsius }
And
Ybaseline = { vitamin D, d+ , vitamin E, f- , f }
Umar Saeed and Ansur Mahmood Amjad ( 2009 ) stated the advantages of black box which utilizing several methods as follow:
It is easy to larn, simple and the most faulty technique.
Some of the methods have quality of edification and simpleness which generates to cover discovers mistakes of the boundaries.
It is various in commanding the alterations by controlled on the base of individual and multiple mistake premises.
It can besides ease the trial instance to bring forth and assist placing any job before the coding start moreover it besides helps full for proving intent.
It is a dependability theory by utilizing baseline or robust to find the instances and the BVA in the black box theoretical account is a utile tools in finding the quality and completeness of the plan which make the trial easy to use.
Umar Saeed and Ansur Mahmood Amjad ( 2009 ) mentioned the disadvantages of the black box as below:
The quality of the trial depend on the investing of clip and attempt and any confusion understate in the plan is easy to pretermit and diminish the opportunities of detecting the mistakes.
The black box theoretical account does non incorporate any input standards hence it depend on the choice of equality category types, although it depend on the choice but it does non guarantee best trial instances.
Some of the black box method is loath and the trial suite will hold different consequence as it used by different people so it will impact the accurately of the consequence.
Besides that wash uping in utilizing the theoretical account will ensue in impractical in add-on the trial instances may be impracticable and finally it will be removed from the plan.
Black Scholes Model
The Black Scholes Model is another related theory that can be connected to the Monte Carlo Model. Simon Benninga and Zvi Wiener ( 1997 ) said that the theoretical account is introduced by Robert Merton and Myron Scholes in twelvemonth 1973 which used in binomial pricing option expression. The Black Scholes model involves two underlying assets which are risk-free plus Cash Bond and hazardous plus Stock. Harmonizing to Peter Denteneer ( 2009 ) , the portion monetary value St of the hazardous plus Stock at clip T is assumed to follow a stochastic differential equation of the signifier ; it is besides the Brownian gesture:
dSt = I?tSt dt + I?St dWt
where { Wt } t a‰? 0, I?t is a nonrandom map of T and I? & gt ; 0 is a changeless volatility of the stock. Besides that, Simon Benninga and Zvi Wiener ( 1997 ) shows that the value of the Call at clip T is ( ST – K ) . Harmonizing to the Fundamental Theorem of Arbitrage Pricing, the monetary value of the plus Call at clip T = 0 must be the discounted outlook under the risk-neutral step which is:
C = SN ( d1 ) – Xe-rT N ( d2 ) aˆs where d1 = lnS/X ) + ( r + I?2/2 ) T and d2=d1-I? ( T ) 1/2
I? ( T ) 1/2
Harmonizing to Peter Denteneer ( 2009 ) there are several premises for the Merton-Black-Scholes theoretical account to idealise fiscal market which are:
Trading takes topographic point continuously and the standard signifier of the capital market theoretical account holds at each blink of an eye.
The merchandising of the assets is possible at any clip.
There are no dealing costs and short merchandising is allowed, i.e. an investor can sell a security that he does non have.
All market participants with utilizing this theoretical account can impart and borrow money at a changeless involvement rate.
Assume there are no dividend payment between t=0 and t=T.
Basket Option Pricing
Harmonizing to Georges Dionne, Genevieve Gauthier, Nadia Ouertani and Nabil Tahani ( 2006 ) , the basket pricing option is utile in exposed to the currency hazard as every market in the state are really active and liquid, hence it is more of import to follow a portfolio attack as it allows the house to account for the correlativities between these difference fiscal market at the same time with different fiscal hazard. Georges Dionne, Genevieve Gauthier, Nadia Ouertani and Nabil Tahani ( 2006 ) describe that basket option is a type of alien option whose final payment is depend on the value of the basket and is normally cheaper than a portfolio of the standard option. In add-on, the basket options are traded and designed to run into the demands of the purchaser.
Georges Dionne, Genevieve Gauthier, Nadia Ouertani and Nabil Tahani ( 2006 ) surveies describe that as the pricing of the basket options have no expressed analytical solution hence it is more ambitious than the standard option. There are several attacks for the monetary value basket option: the numerical methods, upper and lower boundary and analytical estimates. The parts of the option are:
To plan a model that are stochastic
To calculate the heterogenous market under the T-forward step.
To demo that some of the bing analytical estimate may used in general scene.
To measure up the estimate mistakes.
Harmonizing to Georges Dionne, Genevieve Gauthier, Nadia Ouertani and Nabil Tahani ( 2006 ) , St denotes the clip value of trade good, I?t is its continuously-compounded convenience output at clip T, Ct is the value at clip T of one unit of foreign currency expressed in domestic currency, and therefore the equation is given by:
dSt = st [ ( I±s – I?t ) dt + I?sdWt ] ,
dI?t = I? ( I? – I?t ) dt + I?I?dWt,
dCt = Ct [ I±cdt + I?cdWt ] ,
GARCH Option Pricing Model
Steven L. Heston and Saikat Nandi ( 1997 ) said that the GARCH option pricing theoretical account is a really popular and effectual tools in patterning the volatility of the kineticss market which is efficient usage than the Black Scholes theoretical account in finding the volatility of the market. As the Black Scholes theoretical account is updated every period to find the market option pricing while the GARCH theoretical account is held changeless from the historical monetary value. Steven L. Heston and Saikat Nandi ( 1997 ) stated that the advantages of this theoretical account are:
Its analytical solution that is available in pricing.
Perform good even when the parametric quantity is non re-estimated for long period.
More easy to implement as the volatility is identifiable.
The volatility can be observed from the historical monetary value.
Able to bring forth in illiquid market where the information is non exist.
Ability to capture the correlativity of volatility with return.
Steven L. Heston and Saikat Nandi ( 1997 ) shows that there are two premises made for the theoretical account, the log-spot monetary value follow by a peculiar GARCH procedure and the value of call option prior to termination. The log-spot monetary value expression is as follow:
Log ( S ( T ) ) = log ( S ( t-a?† ) ) + R + ?›h ( T ) + a?sh ( T ) omega ( T ) ,
Where R is the continuously compounded involvement rate for the clip interval a?† and omega ( T ) is a standard normal perturbation, H ( T ) is the conditional discrepancy of the log return between t – a?† and t. Steven L. Heston and Saikat Nandi ( 1997 ) shows that the expression of 2nd premise is as follow:
Log ( S ( T ) ) = log ( S ( t – a?† ) ) + r – 1/2 H ( T ) + a?sh ( T ) z* ( T ) ,
Steven L. Heston and Saikat Nandi ( 1997 ) derive that the monetary values written as maps of the topographic point plus monetary value and since the map of S ( T ) and H ( t + a?† ) can be written as map of S ( T ) . Since the 2nd premise is concerns with the pricing option hence the discrepancy of the theoretical account is non stochastic and we assume the Black Scholes expression clasp.
Heath-Jarrow-Morton Model ( HJM )
Carl Hiarella and Oh Kang Kwon ( 1999 ) indicate that the Heath Jarrow Morton model provides a really general involvement model and capable to integrate most of the market features to let the measureable of the mistakes from the appraisal of informations. Carl Hiarella and Oh Kang Kwon ( 1999 ) mentioned that it is utile to supply an look of price reduction bond monetary value in term of appropriate variable and let us to monetary value most of the European type term construction. Carl Hiarella and Oh Kang Kwon ( 1999 ) shows that the heath Jarrow Model is frequently clip devouring than the Monte Carlo simulation. In order to get the better of this job, many writers have considered many ways to transform the HJM theoretical account to Markovian system.
Harmonizing to Andrew Jeffrey, Oliver Linton, Thong Nguyen and Peter ( 2001 ) , there are two factor theoretical accounts in HJM, the first factor is harmonizing to the short term involvement rate meanwhile the 2nd factor are including the long rate, short rate, rising prices, cardinal inclination and volatility. For our information, Andrew Jeffrey, Oliver Linton, Thong Nguyen and Peter ( 2001 ) shows that the survey determines the issues that analyze the HJM theoretical account factor is in a strict manner.
Andrew Jeffrey, Oliver Linton, Thong Nguyen and Peter ( 2001 ) stated the one factor nonparametric HJM theoretical account determines the term construction in a forward rate which is sensitive to the method adopted. Besides that, Andrew Jeffrey, Oliver Linton, Thong Nguyen and Peter ( 2001 ) highlighted that the theoretical account ‘s appraisal of outputs is less sensitive as the output is the mean rate. Below is the equation that introduces by the model that utilizing for the uncertainness of the bond market.
Dy ( T, T ) = I±y ( E? , T, T ) dt + I? ( E? , T, T ) dW ( T ) ,
Where W ( T ) is a Brownian gesture, E? indicates the possible dependance construction up to clip t. specifically E?IµO?t, where O?t bespeak the information before clip generated by information.
In decision, the above theoretical account are all can be use by the populace and company to find and cipher the mistake, hazard, volatility and involvement rate of a company bond, stock and many other dealing that related to the company with utilizing the map expression. The most appropriate theoretical account that uses to find the hazard of the market is the Black Scholes Model. The GARCH option pricing theoretical account is most utile in determine the volatility of the market comparison to others model as its ability to updated the information every period meanwhile it is easy to implement. For mensurating and rectifying the mistake, the Black Box theoretical account is the most utile theoretical account with comparison to others as its ability to transform the information into the logic end product. Among the several related theory that can be alternatively of the Monte Carlo Option, the most utile and efficient theoretical account that most of the company usage is the Black Scholes Model. It is being use by the company as it about same with the Black Box theoretical account which is easy and simple to larn by the users. It is able to bring forth and place the job faster. Besides that, the premise of the theoretical account had besides brought many benefits either in term of cost or payment of a dealing in the company.
Decision
In this paper, we have showed that the illustration of Monte Carlo simulation in the foretelling the completion period of the undertaking. From that, we conclude that the Monte Carlo methods can supply utile solutions to many jobs originating in finance. Besides, we besides agree with the theory that suggested by Marius Holtan ( 2002 ) which he highlighted that the two of the chief virtuousnesss of Monte Carlo simulation are flexibleness and simpleness. However, Monte Carlo simulation besides has their restriction which Susana Alonso Bonis ( 2006 ) indicated that Monte Carlo attack is that it can be used merely for European manner derived functions securities. Besides that, we besides conclude that there is several related theory that can be alternatively of the Monte Carlo Simulation. Among several theory we found that the Black-Box Model is the most suited theoretical account to replace the Monte Carlo theoretical account as it is easy and simple to larn by the users and it able to bring forth and place the job faster. But if possible it is better to utilize the Monte Carlo theoretical account as its ability to find and cipher the assorted type of job that occurs accurately.
Mention from Books/ Magazines
Billio, M. , Casarin, R. ( 2009, December 1 ) . Identifying Business Cycle Turning Points with Sequential Monte Carlo Methods: An Online and Real-Time Application to the Euro Area. Journal of Forecasting, Volume 29, Pages 145-167.
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Holtan, M. ( 2002, May 31 ) . Using simulation to cipher the NPV of a undertaking. Onward Inc.
Kaplan, S. ( 2008, June ) . Monte Carlo Methods For Option Pricing. Institute of Applied Mathematics ( IAM ) METU Term Project.
Majumdar, C. ( 2005 ) . Merton ‘s Option Pricing Model and Credit Risk Portfolio Analysis: Monte Carlo Simulation. Financial Modeling Workshop: University of Ulm, Germany.
Michael, C, F. , Jian Qiang, H. ( 1995 ) . Sensitivity Analysis for Monte Carlo Simulation of Option Pricing. Probability in the Engineering and Informational Sciences, Volume 9, Issue 3, Pages 417-446.
Michael, J. , Sanislo, P, E. ( 2003, March 24 ) . Real Options and Monte Carlo Modeling for New Product Development. High Energy Consulting.
Ribeiro, C. , Webber, N. ( 2003, February 18 ) . A Monte Carlo Method for the Normal Inverse Gaussian Option Valuation Model utilizing an Inverse Gaussian Bridge. City University: Case Business School.
Samis, M, R. , Davis, G, A. , Laughton, D, G. ( 2007, June 19 ) . Using Stochastic Discounted Cash Flow and Real Option Monte Carlo Simulation to Analyze the Impacts of Contingent Taxes on Mining Projects. Project Evaluation Conference.
Schoutens, W. , Symens, S. ( 2002, October 17 ) . The Pricing of Exotic Options by Monte Carlo Simulations in a Levy Market with Stochastic Volatility. University of Antwerp.
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Mention from web site
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Available: & lt ; hypertext transfer protocol: //www.ias.ac.in/sadhana/Pdf2005AprJun/Pe1300.pdf.html & gt ;
Bonis, S, A. , Palenzuela, V, A. , Herrero, G. ( 2006, February ) . Alternate Monte Carlo Simulation Models and the Growth Option with Jumps. University of Valladolid: Department of Financial Economics and Accounting.
Available: & lt ; hypertext transfer protocol: //realoptions.org/papers2006/Alonso_RO_NY_Alonso.pdf.html & gt ;
Boyle, P, P. , Joy, C. , Ken Seng, T. ( 1977 ) . Quasi-Monte Carlo Method in Numerical Finance. Institute for Operations Research and the Management Sciences.
Available: & lt ; hypertext transfer protocol: //new.soa.org/library/monographs/50thanniversary/investment-section/1999/january/m-as99-2-02.pdf.html & gt ;
Chib, S. , Nardari, F. , Shephard, N. ( 2002 ) . Markov concatenation Monte Carlo methods for stochastic volatility theoretical accounts. Journal of Economics, Volume 108, 281-316. Retrieved August 30, 2001.
Available: & lt ; hypertext transfer protocol: //www.sciencedirect.com/science? _ob=ArticleURL & A ; _udi=B6V8V4FP1GF71 & A ; _user=152310 & A ; _coverDate=04 % 2F01 % 2F2006 & A ; _alid=1390503334 & A ; _rdoc=89 & A ; _fmt=high & A ; _orig=search & A ; _cdi=5880 & A ; _sort=r & A ; _docanchor= & A ; view=c & A ; _ct=15627 & A ; _acct=C000012578 & A ; _version=1 & A ; _urlVersion=0 & A ; _userid=152310 & A ; md5=9993bb5627cb691276117e0ef6f4af90 # bib9.html & gt ;
Congdon, P. ( 2006, January 30 ) . Bayesian theoretical account pick based on Monte Carlo estimations of posterior theoretical account chances. Computatinal Statistics & A ; Data Analysis, Volume 50, Issue 2, Pages 346-357. Retrieved July 13, 2004.
Available: & lt ; hypertext transfer protocol: //www.sciencedirect.com/science? _ob=ArticleURL & A ; _udi=B6V8V4D677NW1 & A ; _user=152310 & A ; _coverDate=01 % 2F30 % 2F2006 & A ; _alid=1390468735 & A ; _rdoc=133 & A ; _fmt=high & A ; _orig=search & A ; _cdi=5880 & A ; _sort=r & A ; _st=13 & A ; _docanchor= & A ; view=c & A ; _ct=15627 & A ; _acct=C000012578 & A ; _version=1 & A ; _urlVersion=0 & A ; _userid=152310 & A ; md5=f4a18125e4bea7e7255afa1fd18ba434.html & gt ;
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Mention of Related Theory
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Dionne, G. , Gauthier, G. , Ouertani, N. , Tahani, N. ( 2006, February ) . Heterogeneous Basket Option Pricing Using Analytical Approximations. Canada Research Chair in Risk Management Working Paper 06-01.
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Heston, S, L. , Nandi, S. ( 1997, November ) . A Closed-Form GARCH Option Pricing Model. Federal Reserve Bank of Atlanta Working Paper 97-9.
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