# Random walk what is a random variable?

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## Top best answers to the question Ā«Random walk what is a random variableĀ»

A random walk of length k on a possibly infinite graph G with a root 0 is a stochastic process with random variables such that and is a vertex **chosen uniformly at random from the neighbors of** . Then the number is the probability that a random walk of length k starting at v ends at w.

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Those who are looking for an answer to the question Ā«Random walk what is a random variable?Ā» often ask the following questions:

### ā What is a random walk variable?

A random walk is defined as **a process where the current value of a variable is composed of the past value**. **plus an error term defined as a white noise** (a normal variable with zero mean and variance one).

### ā Does random walk random?

- A
**random walk is**a mathematical object, known as a stochastic or**random**process , that describes a path that consists of a succession of random steps on some mathematical space such as the integers.

### ā What is random walk?

A random walk describes the movement of an object along some mathematical space, or the different values generated by a random variable. Random walks have applications in Finance, Economics, Chemistry, Physics, and more. In this article, we'll introduce the idea of random walks and Random Walk Theory.

4 other answers

Suppose { X n, n ā„ 1 } are random variable on the probability space ( Ī©, B, P) and define the induced random walk by. S 0 = 0, S n = ā i = 1 n X i, n ā„ 1. Let. Ļ := inf { n > 0: S n > 0 } be the first upgoing ladder time. Prove that Ļ is a random variable. Assume we know Ļ ( Ļ) < ā for all Ļ ā Ī© . Prove S Ļ is a random variable.

The deļ¬nition of a random walk uses the concept of independent random variables whose technical aspects are reviewed in Chapter 1. For now let us just think of independent random variables as outcomes of a sequence of random experiments where the result of one experiment is not at all inļ¬uenced by the outcomes of the other experiments.

i; i ā„ 1} be a sequence of IID random variables, and let S n = X 1 + X 2 + Ā·Ā·Ā· + X n. The integer-time stochastic process {S n; n ā„ 1} is called a random walk, or, more precisely, the one-dimensional random walk based on {X i; i ā„ 1}. For any given n, S n is simply a sum of IID random variables, but here the behavior of the entire random walk process, {S

Suppose that U = (U1, U2, ā¦) is a sequence of independent random variables, each taking values 1 and ā 1 with probabilities p ā [0, 1] and 1 ā p respectively. Let X = (X0, X1, X2, ā¦) be the partial sum process associated with U, so that Xn = n ā i = 1Ui, n ā N The sequence X is the simple random walk with parameter p.

We've handpicked 24 related questions for you, similar to Ā«Random walk what is a random variable?Ā» so you can surely find the answer!

What is random walk with drift?Random walk with drift For a random walk with drift, the best forecast of tomorrow's price is today's price plus a drift term. One could think of the drift as measuring a trend in the price (perhaps reflecting long-term inflation). Given the drift is usually assumed to be constant.

What is the random walk equation?The random walk is simple if **Xk = Ā±1, with P(Xk = 1) = p and P(Xk = ā1) = 1āp = q**. Imagine a particle performing a random walk on the integer points of the real line, where it in each step moves to one of its neighboring points; see Figure 1. Remark 1. You can also study random walks in higher dimensions.

A random walk starting at any vertex will (assuming G is connected and [as Nate pointed out] gives an aperiodic walk) **converge to the stationary distribution**, which is given by the values of the left eigenvector associated with the first eigenvalue of the transition matrix.

Examples of non-ergodic random processes

An **unbiased random walk is non-ergodic**. Its expectation value is zero at all times, whereas its time average is a random variable with divergent variance.

**Random Walk** and Stationarityā¦ In fact, all random walk processes are non-stationary. Note that not all non-stationary time series **are random walks**. Additionally, a non-stationary time series does not have a consistent mean and/or variance over time.

Random walk theory suggests that **changes in stock prices have the same distribution and are independent of each other**. Therefore, it assumes the past movement or trend of a stock price or market cannot be used to predict its future movement.

#### What is random walk theory in finance?

**Random walk**theory infers that the past movement or trend of**a**stock price or market cannot be used to predict its future movement. Random walk theory believes it's impossible to outperform the market without assuming additional risk.

Random walk, in probability theory, **a process for determining the probable location of a point subject to random motions, given the** probabilities (the same at each step) of moving some distance in some direction. Random walks are an example of Markov processes, in which future behaviour is independent of past history.

A random walk is **a sequence of discrete, fixed-length steps in random directions**. Random walks may be 1-dimensional, 2-dimensional, or n-dimensional for any n. A random walk can also be confined to a lattice.

(Think of an inebriated person who steps randomly to the left or right at the same time as he steps forward: the path he traces will be a **random walk**.) ... If the constant term (alpha) in the random walk model is zero, it is a **random walk without drift**.

Biased- (Not random) Unbiased-(Random) Example: (ubbiased) Woman takes random people to take a survey.

What is random walk ask me about?I told him no and he walked away.. then walked back and asked me to go to dinner with him. Lets say I was single.. no way in hell am I going anywhere with a random guy in the middle of the night to "go get some dinner". If you walk up and just ask for a number, you're going to get turned down everytime.

What is random walk theory in finance?**Random walk**theory infers that the past movement or trend of**a**stock price or market cannot be used to predict its future movement. Random walk theory believes it's impossible to outperform the market without assuming additional risk.

A **random walk** is unpredictable; it cannot reasonably be predicted.

4.5.2 **Random Walk**

The model has the same form as AR(1) process, but since Ļ = 1, it is not stationary. Such process is called Random Walk.

The random walk (RW) model is **a special case of the autoregressive (AR) model**, in which the slope parameter is equal to 1 ā¦ The AR model exhibits higher persistence when its slope parameter is closer to 1, but the process reverts to its mean fairly quickly.

The idea that stock prices revert to a long term level. The key difference between a mean-reverting process and a random-walk is that **after the shock, the random-walk price process does not return to the old level**ā¦

Random walk theory suggests that changes in stock prices have the same distribution and **are independent of each other**ā¦ Random walk theory believes it's impossible to outperform the market without assuming additional risk.

- Basic Assumptions of the
**Random Walk**Theory. The**Random Walk**Theory assumes that the price of each security in the stock market follows a**random walk**. - Brief History of the
**Random Walk**Theoryā¦ - Implications of the
**Random Walk**Theoryā¦ - Random Walk Theory in Practiceā¦
- A Non-Random Walkā¦
- Conclusionā¦

Random Walk Assumptions:

**The price movements under Random Walk Theory are randomly distributed, in such a way that the present steps are independent of past steps** and in view of such random movements entry into the market any time gives same returns for the same risk to the investors.

: a process (such as Brownian motion or genetic drift) consisting of a sequence of steps (such as movements or changes in gene frequency) **each of whose characteristics (such as magnitude and direction) is determined by chance**.

- Deļ¬nition 2.1 [Random walk]Suppose that X 1, X 2,. . . is a sequence ofRd-valued independent and identically distributed random variables. A random walk started at z 2Rdis the sequence (Sn) n\\u00150where S 0= z and Sn= S n 1+ Xn, n \\u00151.

- The length of a
**walk**is number of edges in the path, equivalently it is equal to k. 2.**Random**Walks on Graphs Let G be a**graph**or digraph with the additional assumption that if G is a digraph, then deg+(v) > 0 for every vertex v. Now consider an object placed at vertex**v**j. At each stage the object must move to an adjacent vertex.

The variance of a random variable X is defined as **var[X] = E[(X āE[X])2]**. In other words, on average, what is the square of your distance to the expectation.