This paper considers the use of ANN methodology for parameters estimation of the autoregressive conditional heteroscedastic (ARCH) processes. The paper provides heuristic approach of ARCH processes modelling. This approach is often employed to estimate the values of financial variables as rates of return, exchange rates, means and variances of inflation, stock market returns and price indexes a...

Various e m p i r i d studies have shown that the time-varying volatility of asset returns can be described by GARCH (generalized autoregressive conditional heteroskedasticity) models. The corresponding GARCH option pricing model of Duan (1995) is capable of depicting the "smile-effect" which often can be found in option prices. In some derivative markets, however, the slope of the smile is not...

In this Appendix, we provide the entire set of additional robustness checks mentioned throughout, but not included within, the original paper. The order in which the tables are presented corresponds to the order in which the results are mentioned in the original paper, with the exception of the table containing returns to various trading strategies, Table A10, which appears last and has its own...

The authors give easy-to-check sufficient conditions for the geometric ergodicity and the finiteness of the moments of a random process xt = φ(xt−1, . . . , xt−p)+ tσ(xt−1, . . . , xt−q) in which φ : IR → IR, σ : IR → IR and ( t) is a sequence of independent and identically distributed random variables. They deduce strong mixing properties for this class of nonlinear autoregressive models with ...

With high inflation economy, investors must find another way to minimize their risk, but also maximize return of the portfolio. Various instruments used for finding most suitable amount portfolio allocation. Single instruments, such as stocks, bonds, and time deposits is chosen by secure assets from inflation. The other chose mutual funds grow investments. One solutions allocation rebalance In ...

We discuss the existence and uniqueness of stationary ergodic nonlinear autoregressive processes when exogenous regressors are incorporated into dynamic. To this end, we consider convergence backward iterations dependent random maps. In particular, give a new result classical condition contraction on average is replaced with in conditional expectation. Under some conditions, also dependence pro...

A.1. Background on long memory models. As mentioned in the introduction, long-memory estimation is typically difficult both in theory and in practice; fundamental stochastic analysis research on this question is ongoing. In discrete time series, the long-memory autoregressive moving average (ARMA) and autoregressive conditional heteroskedasticity (ARCH) models that are popular in financial econ...

To capture the missed information in the standardized errors by parametric multivariate generalized autoregressive conditional heteroskedasticity (MV-GARCH) model, we propose a new semiparametric MV-GARCH (SM-GARCH) model. This SM-GARCH model is a twostep model: firstly estimating parametric MV-GARCH model, then using nonparametric skills to model the conditional covariance matrix of the standa...

Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH family started by Engle (1982). In ...