This paper applies six recently developed nonparametric tests of serial independence to monthly US stock returns. Findings of previous studies based on the BDS test are supported since most of the new tests also reject the random walk hypothesis. Furthermore, power properties of the new tests are compared with those of the BDS test. The latter has much power against ARCH and GARCH alternatives ...

The augmented GARCH model is a unification of numerous extensions of the popular and widely used ARCH process. It was introduced by Duan and besides ordinary (linear) GARCH processes, it contains exponential GARCH, power GARCH, threshold GARCH, asymmetric GARCH, etc. In this paper, we study the probabilistic structure of augmented GARCH(1,1) sequences and the asymptotic distribution of various ...

Autoregressive conditional heteroskedasticity (ARCH) models pioneered by Engle (1982) and their extended version have been proven to be very successful in modeling the volatility of financial time series; see Bollerslev et al. (1994). Bayesian inference on ARCH models has been implemented using the importance sampling technique proposed by Geweke (1989) and more recently using Markov chain Mont...

For a given time horizon ∆T , this article explores the relationship between the realized volatility (the volatility that will occur between t and t + ∆T ), the implied volatility (corresponding to at-the-money option with expiry at t+∆T ), and several forecasts for the volatility build from multi-scales linear ARCH processes. The forecasts are derived from the process equations, and the parame...

This paper performs an experiment to forecast stock market movement in India using Artificial Neural Network (ANN) and Genetic Algorithm (GA). This model is named Genetically optimized Neural Network (GNN). We have tested this newly created model against traditional ARCH/GARCH models using hypothesis testing (z-test).We have used different error metrics like Average Absolute Error (AAE), Mean A...

The role of price risk in sow farrowings is investigated by using bivariate ARCH-M and GARCH-M models and a nonparametric kernel estimator. To account for the relevant time horizon of irreversible supply decisions, predictions for mean price and conditional price variance are iterated forward. The empirical results vary markedly in terms of their implications for risk response in hog supply dec...