The random walk model is widely used in the area of finance. The stock prices or exchange rates (Asset prices) follow a random walk. A common and serious departure from random behavior is called a random walk (non-stationary) since today’s stock price is equal to yesterday’s stock price plus a random shock.
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Types of Random Walk Model
There are two types of random walks
- Random walk without drift (no constant or intercept)
- Random walk with drift (with a constant term)
Definition
A time series is said to follow a random walk if the first differences (difference from one observation to the next observation) are random.
Note that in a random walk model, the time series itself is not random, however, the first differences in time series are random (the differences change from one period to the next).
A random walk model for a time series $X_t$ can be written as
[X_t=X_{t-1}+e_t, , ,]
where $X_t$ is the value in time period $t$, $X_{t-1}$ is the value in time period $t-1$ plus a random shock $e_t$ (value of error term in time period $t$).
Since the random walk is defined in terms of first differences, therefore, it is easier to see the model as
[X_t-X_{t-1}=e_t, , ,]
where the original time series is changed to a first difference time series, that is the time series is transformed.
The transformed time series:
- Forecast the future trends to aid in decision-making
- If the time series follows a random walk, the original series offers little or no insights
- May need to analyze the first differenced time series
Real World Example
Consider a real-world example of the daily US-dollar-to-Euro exchange rate. A plot of the entire history (of daily US-dollar-to-Euro exchange rate) from January 1, 1999, to December 5, 2014, looks like
The historical pattern from the above plot looks quite interesting, with many peaks and valleys. The plot of the daily changes (first difference) would look like
The volatility (variance) has not been constant over time, but the day-to-day changes are almost completely random.
Key Characteristics of a Random Walk
- No Pattern: The path taken by a random walk is unpredictable.
- Independence: Each step is independent of the previous one.
- Probability distribution: The size and direction of each step can be defined by a probability distribution.
Applications of Random Walk Models
Beyond finance, random walk models have applications in:
- Physics: Brownian motion and diffusion processes
- Biology: Population dynamics and genetic drift
- Computer science: Algorithms and simulations
Remember that, random walk patterns are also widely found elsewhere in nature, for example, in the phenomenon of Brownian Motion that was first explained by Einstein.
