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What is the difference between stochastic and random?
Wikipedia states that stochastic means random. But there are differences depending on the context. Stochastic is used as an adjective, as in stochastic process, stochastic model, or stochastic simulation, with the meaning that phenomena as analyzed has an element of uncertainty or chance (random element). If a system is not stochastic, it is deterministic. I may consider a phenomena is a random process and analyze it using a stochastic simulation model. When we generate numbers using a probability distribution, these are called random numbers, or pseudo random numbers. They can also be called random deviates. See related links.
What is the difference between 'stochastic' and 'deterministic'?
These words are used to describe ways of modeling or understanding the world. "Stochastic" means that some elements of the model or description are thought of as being random. (The word "Stochastic" is derived from an ancient Greek word for random.) A model or description that has no random factors, but conceivably could, is called "deterministic." For example, the equation Q = VC where Q = charge, V = voltage, and C = capacitance, is a deterministic physical model. One stochastic version of it would be Q = VC + e where e is a random variable introduced to account for or characterize the deviations between the actual charges and the values predicted by the deterministic model.
What is a GARCH model?
A GARCH model is employed to help predict volatility (i.e. of stocks, XE rates etc) based on historical values through model fitting. Recent data is given more significance than older data. Compare to the least squares approach, which weights all the data equally. Since volatility is not the same across the entire data set (periods of volatility cluster together), this assumption is not valid. The related link provides greater detail and an Excel spreadsheet
How do you calculate implied volatility using solver?
To calculate implied volatility using Solver, you need an options pricing model (such as Black-Scholes) and market data (including the option price, strike price, underlying asset price, risk-free rate, time to expiration, and any dividends). Build the pricing model in a spreadsheet, input the market data, and set the initial volatility value in Solver. Set the objective to match the calculated option price with the market price by changing the volatility cell. Run Solver to find the implied volatility that minimizes the difference between the calculated and market option prices.
What is the difference between the stochastic error term and the residual?
Ah, the stochastic error term and the residual are like happy little clouds in our painting. The stochastic error term represents the random variability in our data that we can't explain, while the residual is the difference between the observed value and the predicted value by our model. Both are important in understanding and improving our models, just like adding details to our beautiful landscape.
Did charlton heston work as a model?
Yes. At one point Charlton Heston worked as a male model.
What is the difference between stochastic and random?
Wikipedia states that stochastic means random. But there are differences depending on the context. Stochastic is used as an adjective, as in stochastic process, stochastic model, or stochastic simulation, with the meaning that phenomena as analyzed has an element of uncertainty or chance (random element). If a system is not stochastic, it is deterministic. I may consider a phenomena is a random process and analyze it using a stochastic simulation model. When we generate numbers using a probability distribution, these are called random numbers, or pseudo random numbers. They can also be called random deviates. See related links.
What is the major difference between a mathematical model and an econometric model?
Mathematical model is exact in nature.it has Beta zero and Beta one and no stochastic or disturbance variables. Econometric model represents omitted variable, error in measurement and stochastic variables.
What is stochastic calculus?
The mathematical theory of stochastic integrals, i.e. integrals where the integrator function is over the path of a stochastic, or random, process. Brownian motion is the classical example of a stochastic process. It is widely used to model the prices of financial assets and is at the basis of Black and Scholes' theory of option pricing.
What has the author Lode Li written?
Lode Li has written: 'A stochastic theory of the firm' -- subject(s): Accessible book 'Optimal operating policies for multi-plant stochastic manufacturing systems in a changing environment' 'A stochastic model of resource flexibility' -- subject(s): Accessible book
What is the difference between 'stochastic' and 'deterministic'?
These words are used to describe ways of modeling or understanding the world. "Stochastic" means that some elements of the model or description are thought of as being random. (The word "Stochastic" is derived from an ancient Greek word for random.) A model or description that has no random factors, but conceivably could, is called "deterministic." For example, the equation Q = VC where Q = charge, V = voltage, and C = capacitance, is a deterministic physical model. One stochastic version of it would be Q = VC + e where e is a random variable introduced to account for or characterize the deviations between the actual charges and the values predicted by the deterministic model.
What has the author Tuula Hakala written?
Tuula Hakala has written: 'A stochastic optimization model for multi-currency bond portfolio management' -- subject(s): Mathematical models, Interest rates, Risk, Stochastic programming
What has the author H M Scoging written?
H. M. Scoging has written: 'A stochastic model of daily rainfall simulation in a semi-arid environment' -- subject(s): Mathematical models, Rain and rainfall, Stochastic processes
What is stochastic disturbance term?
A stochastic disturbance term is a random variable included in a statistical model to account for unexplained variability or uncertainty in the data. It represents the effects of unobserved factors that are not explicitly modeled but can influence the outcome of an analysis. By incorporating this term, the model can better capture the randomness or unpredictability in the data.
What is stochastic error term?
A Stochastic error term is a term that is added to a regression equation to introduce all of the variation in Y that cannot be explained by the included Xs. It is, in effect, a symbol of the econometrician's ignorance or inability to model all the movements of the dependent variable.
What has the author Yanguang Shan written?
Yanguang Shan has written: 'A stochastic spray model for radio frequency inductively coupled plasmas'
Are stochastic methods classified under empirical models?
It really depends on what exactly you are referring to when you use the term "stochastic method". Stochastic implies randomness. A stochastic method could involve a random search for the correct interaction, a random search for a set of possible outcomes, or even guided guessing for a nearly exact or likely solution, just to list a few. The model used in a stochastic method could be first principles, or empirical. In a first principles model the interactions are governed by equations which have been determined by the most basic physics. Any approximations are justified and accounted for. In an empirical model, the interaction is either approximated, guessed at, or completely ignored with a simple input to output mapping. The approximations are unknown, and the errors must be accounted for by comparing the model to a real event, and then crossing ones fingers in the hopes that the errors hold true for all similar events. For examples: * Most weather models are a mix of first principles, empirical and stochastic methods. They use first principles to govern air and heat flow, but use empirical approximations to account for the surface of the Earth and the effects of rain fall. They are stochastic in that they use slightly randomized sets of data to reflect errors in the data gathering, and in the model itself. * Climate models are empirical and stochastic. The basic interactions have been either guessed at or ignored in favor of a simple input to output mapping. The "most likely" outcome is then guessed at by feeding the model a range of inputs. * Calculation of electron exchange and correlation potentials are first principles and stochastic. The interactions of the electrons are dictated strictly by quantum mechanics and electrostatics, but the ground states of many random configurations need to be investigated.
