Questions tagged [brownian-motion]
In mathematics, Brownian motion is described by the Wiener process; a continuous-time stochastic process named in honor of Norbert Wiener.
495 questions
1 vote
2 answers
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Girsanov Theorem: how to implement it with code?
I'm trying to visualize the path transformations coming from the application of the Girsanov Theorem in a Monte Carlo Simulation. Below the (Python) code where I'm trying to "adjust" the ...
1 vote
0 answers
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Joint distribution correlated Brownian motions weighted by stopping probabilities
I want to find the joint distribution of 2 correlated Brownian motions, $X$ and $Y$, at time $T$ after crossing boundaries $a<0$ and $b<0$ respectively with initial values $X_0=Y_0=0$, drifts $\...
0 votes
0 answers
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Adjusting Riemann correction by Hurst Exponent for Barrier Option Pricing
I am simulating barrier option pricing by implementing Fractional Brownian Motion (Cholesky decomposition approach). Is the adjustment of the Riemann correction of the barrier by the Hurst exponent ...
0 votes
0 answers
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Cointegrated log normal processes
Playing around with the idea of controlling the dynamics between two processes. There are some papers around this building on the SDE $\frac{dS}{S} = \alpha (\mu - \ln S)dt + \sigma dW_{t}$ (Schwartz) ...
1 vote
0 answers
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Can Black-Scholes-style modeling help with CapEx forecasting? Does it make sense to apply Black-Scholes-related concepts this way?
I've been learning about quantitative finance for the past few months, though I’m still far from an expert. I’ve read about applications of Black-Scholes concepts outside traditional financial options....
0 votes
0 answers
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Implementation of standard stretched Brownian motion in Python
I am trying to implement a standard stretched Brownian motion 1,2in Python. This requires finding a fixed point of the equation $\mathcal{A}:CDF \to CDF$, $\mathcal{A}F = F_\mu \circ(\phi*(Q_\nu \circ(...
0 votes
0 answers
132 views
Generation of two correlated Brownian Motions using two different approaches in numpy Python
Consider two correlated Brownian Motions $W_{1,t}$ and $W_{2,t}$ for which it holds: $$dW_{1,t}\sim N(0, \sqrt{dt})$$ $$dW_{2,t}\sim N(0, \sqrt{dt})$$ $$Cov(dW_{1,t},dW_{2,t}) = E[dW_{1,t}dW_{2,t}] = \...
1 vote
0 answers
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Initial price of contingent claim in terms of Black-Scholes call option initial price with new volatility parameter
In this problem we assume the interest rate $r=0$. I have a European contingent claim with maturity $T$ and payout $$Y = S_T 1_{\{S_T \leq K\}} - K1_{\{S_T > K\}}$$ I need to show that the initial ...
1 vote
0 answers
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Conceptual questions on Brownian Bridge
Brownian Bridge, denoted $B_t$ can be described as a Brownian motion pinned at zero and $T$, with the following mathematical definition: $$B_t=W_t-\frac{t}{T}W_T$$ From the above, we can deduce that $\...
0 votes
1 answer
264 views
Trying to solve an SDE with a power function
I want to solve an SDE: $$ dX_t=X^3_t\,dt+X^2_t\,dB_t, $$ with $X_0$=1. In my understanding (I'm currently using J. Michael Steele's textbook "Stochastic calculus and financial applications")...
0 votes
0 answers
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Should Geometric Brownian Motion Prediction Use Rolling Window or Train-Test Split?
I'm currently working on a stock market prediction project using Geometric Brownian Motion (GBM). My goal is to showcase that GBM, as a stochastic model, does not require a large dataset compared to ...
2 votes
1 answer
106 views
Trying to derive some properties of geometric brownian motion
I am trying to derive some properties of geometric brownian motion: $dS_t = \mu S_t dt + \sigma S_t dW_t$ I am interested in analyzing paths that 'survive' a lower boundary $X$ i.e. always stay above $...
1 vote
1 answer
140 views
Estimate of GBM Return Variance
I typically see people define realized variance as the squared difference in log returns, i.e. $$RVar = \frac{1}{T} \sum_{n=1}^N \log \left( \frac{S_{n}}{S_{n-1}} \right)^2$$ where $t_n - t_{n-1} = \...
4 votes
4 answers
586 views
Show that $E [ \int_s^t W_u \, du \,|\, \mathcal{F}_s ] = (t - s) W_s$
Show that $$E \left[ \int_s^t W_u \, du \,|\, \mathcal{F}_s \right] = (t - s) W_s$$ where $W_u$ is a standard Brownian motion and $\mathcal{F}_s$ is the filtration up to time $s $.
0 votes
1 answer
106 views
ABM Crossing Times
Suppose I have a process that follows an arithmetic brownian motion $dX_t = \sigma dW_t$ How do I calculate, within a certain interval $\Delta t$ , the expected number of times that the process will &...