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We distinguish two types of stationarity: strict stationarity (or strong stationarity) and weak stationarity.

Strictly Stationary Process (strongly stationary process) is a stochastic process whose finite-dimensional distributions do not change with time. Formally speaking, a stochastic process X(t) is strictly stationary if, for any moments of time t_{1}, ..., t_{k}, positive shift h and values x_{1}, ..., x_{k},

P(X(t_{1}+h) ≤ x_{1}, ... , X(t_{k}+h) ≤ x_{k}) = P(X(t_{1}) ≤ x_{1}, ... , X(t_{k}) ≤ x_{k}).

Stationary Process (weakly stationary process) is a stochastic process whose expectations and covariances do not change with time. Formally speaking, a stochastic process X(t) is weakly stationary if, for any moments of time s < t and positive shift h,

1] E[X(t+h)] = E[X(t)],

2] Cov[X(s+h), X(t+h)] = Cov[X(s+h), X(t+h)].

Strict stationarity implies weak stationarity.

**STATIONARY PROCESS REFERENCES
**

Lawler, G. F. (1995). Introduction to Stochastic Processes. New York: Chapman and Hall/CRC.

Ross, S. M. (1995). Stochastic Processes (2nd ed). New York: Wiley.

Karlin, S., & Taylor, H. M. (1975). A First Course in Stochastic Processes (2nd ed). New York: Academic Press.

Gikhman, I. I., & Skorokhod, A. V. (2004). The Theory of Stochastic Processes I. Springer-Verlag Berlin Heidelberg.

Gikhman, I. I., & Skorokhod, A. V. (2004). The Theory of Stochastic Processes II. Springer-Verlag Berlin Heidelberg.

Gikhman, I. I., & Skorokhod, A. V. (2007). The Theory of Stochastic Processes III. Springer-Verlag Berlin Heidelberg.

Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.

Brockwell, P.J., & Davis, R. A. (1991). Time Series: Theory and Methods (2nd ed). New York: Springer.

Tsay, R. S. (2005). Analysis of Financial Time Series. New Jersey: Wiley-Interscience.

Greene, W. H. (2011). Econometric Analysis (7th ed). Upper Saddle River, NJ: Prentice Hall.

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