1. SIMULATION IN THE FINANCE INDUSTRY BY HARESH JANI
A STUDY ON
SIMULATION IN THE FINANCE INDUSTRY BY HARESH JANI

2. SIMULATION IN THE FINANCE INDUSTRY
SIMULATION MEANING:
Simulation is the imitation of the operation of a real-world process or system over time. The act of simulating something first requires that a model be developed; this model represents the key characteristics, behaviors and functions of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system over time.

3. USES: for performance optimization, safety engineering, testing,
training, 
education,
video games,
to show the eventual real effects of alternative conditions and courses of action.
Simulation is also used when the real system cannot be engaged, because it may not be accessible, or it may be dangerous or unacceptable to engage, or it is being designed but not yet built, or it may simply not exist.

4. KEY ISSUES IN SIMULATION:
Acquisition of valid source information about the relevant selection of key characteristics and behaviours,
Use of simplifying approximations and assumptions within the simulation, and fidelity and validity of the simulation outcomes.
Procedures and protocols for model verification and validation are an ongoing field of academic study, refinement, research and development in simulations technology or practice, particularly in the field of computer simulation.

5. WHY USE SIMULATION MODELING?
Solves real-world problems safely, economically and efficiently.
It is an important method of analysis which is easily verified, communicated, and understood.
Provides valuable solutions by giving clear insights into complex systems.
Simulation modeling is experimentation on a valid digital representation of a system.
It is dynamic and can be analyzed while it is running, even viewed in 2D or 3D.
Used in business when conducting experiments on a real system is impossible or impractical, often because of cost or time.

6. Advantages of Simulation:
Study the behavior of a system without building it.
Results are accurate in general, compared to analytical model.
Help to find un-expected phenomenon, behavior of the system.
Easy to perform ``What-If'' analysis.
They are able to provide users with practical feedback when designing real world systems.
They permit system designers to study a problem at several different levels of abstraction.
Effective means for teaching or demonstrating concepts to students

7. Overcome the limitations of spreadsheets.
ADVANTAGES CONT…
Computer simulations for multiphysics systems can be run from anywhere, are independent of changing environments and can be scaled to almost any requirement: from microscopically small to very large.
Overcome the limitations of spreadsheets.
Run variation calculations for different product configurations.
Risk assessment.
System simulation helps you increase performance and energy efficiency.
Reduce energy consumption and save resources.
Increase speed, power and moments of your machinery.
Optimize accuracy and precision of movements and positioning

8. SIMULATION MODELS: Mathematical Model:
To determine the likelihood of a particular outcome.
Running simulations is important for analysts who, for example, wish to predict a security's future price movements.
It is mathematical exercise in which a model of a system is established, then the model’s variables are altered to determine the effects on other variables.
For example,
A financial analyst might construct a model for predicting a stock’s market price and then manipulate various determinants of the price including earning, interest rates, and the inflation rate to determine how each of these changes affects the market price.

9. STOCHASTIC INVESTMENT MODEL:
Forecast how returns and prices on different assets or asset classes, (e. g. equities or bonds) vary over time.
Are not applied for making point estimation rather interval estimation and they use different stochastic processes.
Often used for actuarial work and financial planning to allow optimization in asset allocation or asset-liability-management (ALM).

10. INDUSTRY SPECIFIC SIMULATION:
Vlerick Bank Simulation:
The Vlerick Bank Simulation depicts a realistic financial market in which banks take major financial management decisions in a competitive setting.
Participants in the Vlerick Bank Simulation will manage their simulated bank, gain insight into the decisions taken in banks, understand the impact of financial markets on business and financial performance, and understand a bank’s overall strategy and the interactions among different business areas.

11. MONTE CARLO SIMULATION:
Monte Carlo simulation is a computerized mathematical technique that allows people to account for risk in quantitative analysis and decision making.
The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil & gas, transportation, and the environment.
Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities that will occur for any choice of action.

12. SOUIRCE
Source Wikipedia dictionary.thefreedictionary.com/simulation