Stochastic Differential Equations

In summary, the conversation discusses a project on volatility and drift structure of various markets. The equation dr = u(r,t)dt + w(r,t)dX is mentioned, with a question about whether it is a partial differential equation or a differential equation. The conversation also includes a discussion on dependent and independent variables and the basics of partial derivatives. Some resources are suggested for further reading and a potential collaboration on the project is mentioned.
  • #1
courtrigrad
1,236
2
Hello all

I am doing a project concerning volatility and drift structure of various markets. If we have [tex] dr = u(r,t)dt + w(r,t)dX [/tex] is this a partial differntial equation or just a differential equation? [tex] r [/tex] is the spot rate [tex] t [/tex] is time and [tex] X [/tex] is a random variable.

Thanks :smile:
 
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  • #2
Partial differential equation.
 
  • #3
ok so in other words [tex] dr = \frac{\partial u}{\partial t} dt + \frac{\partial w}{\partial t} dX [/tex]?

Thanks
 
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  • #4
No.

[tex] dr = \frac{\partial r}{\partial t} dt + \frac{\partial r}{\partial X} dX [/tex]
 
  • #5
courtrigrad said:
Hello all

I am doing a project concerning volatility and drift structure of various markets. If we have [tex] dr = u(r,t)dt + w(r,t)dX [/tex] is this a partial differntial equation or just a differential equation? [tex] r [/tex] is the spot rate [tex] t [/tex] is time and [tex] X [/tex] is a random variable.

Thanks :smile:


Well, I must admit that looks confussing to me. Would you kindly explain what's the dependent variable and what are the independent variable?

As I see it, it looks like the following:

We wish to find the function r(t,X) such that:

[tex]\frac{\partial r}{\partial t}=\frac{\partial u}{\partial t}+\frac {\partial w}{\partial X} [/tex]

such that u(r,t) and w(r,t) are given functions of the "dependent" variable r(t,X) and t.

I still think this isn't right but maybe an improvement you can correct.
 
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  • #6
yes i think salty dog that is right. We have two functions u and t with parameters r and t. I am not sure, as I am just studying calculus!
 
  • #7
courtrigrad said:
yes i think salty dog that is right. We have two functions u and t with parameters r and t. I am not sure, as I am just studying calculus!

Noooooo dude. That's not quite right what you said: need to precisely define what the function is, the dependent variable, independent variables and what partials are involved. I'm kind and won't tell you perhaps PDEs are not something for you to be looking at if you're just into Calculus.
 
  • #8
saltydog said:
Noooooo dude. That's not quite right what you said: need to precisely define what the function is, the dependent variable, independent variables and what partials are involved. I'm kind and won't tell you perhaps PDEs are not something for you to be looking at if you're just into Calculus.

Wait a minute. I'm sorry. I mean it's ok to look at them and be in wonder about them but perhaps not expect to be able to solve them if you're just starting Calculus. I once had a Chem teacher who showed me a triple integral a long time ago before I knew what it was. He expressed utter wonder at the time and I didn't understand. I do now!
 
  • #9
just taking mathwonk's advice. i am reading Courant's calculus book in addition to studying finance. i am skipping around and I understand the basic concept that in a partial derivative you keep variables fixed.
 
  • #10
courtrigrad,

I've been looking on the arxives and found this, it has a lot to do with what you have been asking about and specifically markets, options, and stochastics:

http://xxx.lanl.gov/PS_cache/physics/pdf/0001/0001040.pdf

There are some pretty good references in the bibliography that you may want to look into for further reading. This paper was also published in Physica A which carries a lot of the financial/physics papers.

I also found this 'elementary'(HA!) introduction to stochastic calculus. Scroll down towards the bottom of the page and the notes are in a pdf format.

http://www.statslab.cam.ac.uk/~afrb2/

Good luck on your project and I hope this helps a bit. There truly is a lot to the subject and you have only begun to scratch the surface, so have fun and keep digging.

BTW, have you had a chance to consult with a teacher on narrowing down the topic for your project?
 
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  • #11
yea i am investigating the drift and volatility structure of current data/ Polyb, thanks al ot for your great help. :smile: Maybe we can discuss more about the project, and your ideas as well

Thanks
 

1. What is a Stochastic Differential Equation (SDE)?

A Stochastic Differential Equation is a mathematical equation that describes the evolution of a system over time, where the system is subject to both deterministic and random forces. It is a type of differential equation that is commonly used to model systems in physics, finance, engineering, and other fields.

2. How are SDEs different from ordinary differential equations (ODEs)?

The main difference between SDEs and ODEs is that SDEs incorporate a random noise term, while ODEs do not. This random noise term accounts for the unpredictable behavior of a system and allows for more realistic modeling of real-world phenomena. Additionally, the solutions to SDEs are usually stochastic processes, while ODE solutions are deterministic.

3. What is the importance of SDEs in science and engineering?

SDEs are important because they provide a powerful tool for modeling and analyzing complex systems that are subject to random fluctuations. They are used in a wide range of fields, including economics, biology, physics, and engineering, to understand and predict the behavior of these systems over time.

4. How are SDEs solved?

SDEs are typically solved using numerical methods, such as Euler-Maruyama or Milstein methods, as there are no known general analytical solutions for most SDEs. These methods involve discretizing the SDE and approximating the solution at each time step. Sophisticated algorithms have been developed to improve the accuracy and efficiency of these numerical methods.

5. What are the applications of SDEs?

SDEs have a wide range of applications in various fields. In finance, they are used to model stock prices and interest rates. In biology, they are used to model population dynamics and the spread of diseases. In physics, they are used to model Brownian motion and diffusion processes. Other applications include climate modeling, signal processing, and control systems.

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