Autocorrelation, also known as serial correlation, is a statistical concept that describes the degree of similarity between a given time series and a lagged version of itself over successive time intervals. In finance, it’s used to identify non-randomness in data, particularly in pricing models.
For instance, stock prices are often modeled using random walk hypothesis which assumes no autocorrelation; however, empirical studies have shown some level of positive autocorrelation at short lags, indicating predictability in returns. This can be exploited for profitable trading strategies.
Moreover, autocorrelation helps in detecting seasonality in sales or identifying cyclical patterns in economic indicators. It’s also crucial in model selection where residuals from an ideal model should exhibit zero autocorrelation.
In options pricing, autocorrelation of underlying asset returns affects option values. For example, Black-Scholes model assumes constant volatility, but if returns are autocorrelated, implied volatility will change, affecting option price.
