Quadratic Variation (Stochastic Processes and Brownian Motion)

In summary, the quadratic variation in Brownian motion is not zero, which indicates that the process is random and not predictable. This is important in the study of stochastic calculus and has implications in financial models, particularly in understanding volatility in stock prices.
  • #1
spenghali
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Homework Statement


No specific problem to solve, just looking for a better explanation of the implications of the quadratic variation not being zero in Brownian motion. Why is this so important in the study of stochastic calculus and Brownian motion? I understand that quadratic variation in usually zero in regular calculus. Also, what does the quadratic variation actually tell us in layman terms in both stochastic and regular calculus? I think the side-by-side comparison should help. Thanks for any responses/help.

Homework Equations


The Attempt at a Solution



P.s. does this lead to volatility in stock price models?
 
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  • #2
In regular calculus, the quadratic variation of a function tells us how much the function changes over a period of time. In stochastic calculus and Brownian motion, the quadratic variation is not necessarily zero. This is important because it implies that the process is not a deterministic one, but rather a random one. In other words, the quadratic variation tells us how much the process changes over a period of time, but it also tells us that the changes are not predictable or consistent. This has implications in financial models, such as stock price models, where volatility is an important factor. The quadratic variation helps us to understand the magnitude of the volatility, and therefore gives us some insight into the behavior of the stock prices.
 

FAQ: Quadratic Variation (Stochastic Processes and Brownian Motion)

What is quadratic variation in stochastic processes?

Quadratic variation refers to the measure of the amount of change or variability in a stochastic process over time. It is calculated by taking the sum of the squared differences between consecutive values of the process.

How is quadratic variation related to Brownian motion?

Brownian motion is a type of stochastic process that exhibits a random and continuous movement. Quadratic variation is used to measure the amount of irregularity or randomness in Brownian motion, making it a key concept in understanding and analyzing this type of process.

Can quadratic variation be negative?

No, quadratic variation is always a non-negative value. Since it represents the sum of squared differences, it cannot be negative. However, it can be equal to zero if the stochastic process is constant.

What is the significance of quadratic variation in finance?

Quadratic variation is an important concept in finance, particularly in the study of financial markets and asset prices. It is used to measure the volatility, or degree of fluctuation, in asset prices and is a key component in models such as the Black-Scholes model for option pricing.

How is quadratic variation calculated?

To calculate quadratic variation, the differences between consecutive values of a stochastic process are squared and then summed. This value is then divided by the length of the time interval over which the process is observed. Alternatively, it can also be calculated using integral calculus by taking the limit of the sum of squared differences as the time interval approaches zero.

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