Effect of Markets Temperature on Stock-Price: Monte Carlo Simulation on Spin Model

Monte Carlo Simulation depends on random behaviour of events. When a variable takes values at random and becomes highly unpredictable due to its nature of randomness, the property of random numbers is made use of for predicting the future values that the variable may take. This property can be made use of for predicting share price movements, when the past share prices exhibit random behaviour, without exhibiting high fluctuations. This article explains the methodology of using Monte Carlo Simulation for predicting share price movements and explains the process with the help of an illustration taking the monthly share price data of ITC Limited for a period of 36 months, where the share prices have moved within a narrow band. Findings of the analysis show that it works well and that the method of prediction is reasonably accurate, showing only a minor deviation from the actual prices.

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VARIANCE GAMMA PROCESS WITH MONTE CARLO SIMULATION AND CLOSED FORM APPROACH FOR EUROPEAN CALL OPTION PRICE DETERMINATION

MEDIA STATISTIKA ◽

10.14710/medstat.14.2.183-193 ◽

2021 ◽

Vol 14 (2) ◽

pp. 183-193

Author(s):

Abdul Hoyyi ◽

Abdurakhman Abdurakhman ◽

Dedi Rosadi

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Closed Form ◽

Stock Price ◽

Option Price ◽

Secondary Data ◽

Exponential Model ◽

Variance Gamma ◽

Variance Gamma Process ◽

Normally Distributed

The Option is widely applied in the financial sector. The Black-Scholes-Merton model is often used in calculating option prices on a stock price movement. The model uses geometric Brownian motion which assumes that the data is normally distributed. However, in reality, stock price movements can cause sharp spikes in data, resulting in nonnormal data distribution. So we need a stock price model that is not normally distributed. One of the fastest growing stock price models today is the process exponential model. The process has the ability to model data that has excess kurtosis and a longer tail (heavy tail) compared to the normal distribution. One of the members of the process is the Variance Gamma (VG) process. The VG process has three parameters which each of them, to control volatility, kurtosis and skewness. In this research, the secondary data samples of options and stocks of two companies were used, namely zoom video communications, Inc. (ZM) and Nokia Corporation (NOK). The price of call options is determined by using closed form equations and Monte Carlo simulation. The Simulation was carried out for various values until convergent result was obtained.

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THE PRICING OF EXOTIC OPTIONS BY MONTE–CARLO SIMULATIONS IN A LÉVY MARKET WITH STOCHASTIC VOLATILITY

International Journal of Theoretical and Applied Finance ◽

10.1142/s0219024903002249 ◽

2003 ◽

Vol 06 (08) ◽

pp. 839-864 ◽

Cited By ~ 16

Author(s):

WIM SCHOUTENS ◽

STIJN SYMENS

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Stochastic Volatility ◽

Stock Price ◽

Poisson Approximation ◽

Exotic Options ◽

Option Prices ◽

Compound Poisson Approximation ◽

Black Scholes ◽

Cliquet Options

Recently, stock price models based on Lévy processes with stochastic volatility were introduced. The resulting vanilla option prices can be calibrated almost perfectly to empirical prices. Under this model, we will price exotic options, like barrier, lookback and cliquet options, by Monte–Carlo simulation. The sampling of paths is based on a compound Poisson approximation of the Lévy process involved. The precise choice of the terms in the approximation is crucial and investigated in detail. In order to reduce the standard error of the Monte–Carlo simulation, we make use of the technique of control variates. It turns out that there are significant differences with the classical Black–Scholes prices.

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Stock Price Simulation Using Bootstrap and Monte Carlo

Scientific Annals of Economics and Business ◽

10.1515/saeb-2017-0010 ◽

2017 ◽

Vol 64 (2) ◽

pp. 155-170 ◽

Cited By ~ 1

Author(s):

Martin Pažický

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Stock Price ◽

Error Term ◽

Monte Carlo Experiment ◽

Asian Option ◽

Heteroscedastic Error ◽

Maturity Date ◽

The Future ◽

The Difference

Abstract In this paper, an attempt is made to assessment and comparison of bootstrap experiment and Monte Carlo experiment for stock price simulation. Since the stock price evolution in the future is extremely important for the investors, there is the attempt to find the best method how to determine the future stock price of BNP Paribas′ bank. The aim of the paper is define the value of the European and Asian option on BNP Paribas′ stock at the maturity date. There are employed four different methods for the simulation. First method is bootstrap experiment with homoscedastic error term, second method is blocked bootstrap experiment with heteroscedastic error term, third method is Monte Carlo simulation with heteroscedastic error term and the last method is Monte Carlo simulation with homoscedastic error term. In the last method there is necessary to model the volatility using econometric GARCH model. The main purpose of the paper is to compare the mentioned methods and select the most reliable. The difference between classical European option and exotic Asian option based on the experiment results is the next aim of tis paper.

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