In this paper we examine the forecasting performance of five nonlinear GARCH(1,1) models. Four of these have recently been proposed in literature, while the fifth model is a new one. All five models allow for switching persistence of shocks, depending on the value and/or sign of recent returns....

In this paper we investigate the properties of the Lagrange Multiplier (LM) test for autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) in the presence of additive outliners (AO's). We show analytically that both the asymptotic size and power are adversely...

In this paper we test for (Generalized) AutoRegressive Conditional Heteroskedasticity [(G) ARCH] in daily and weekly data on 22 exchange rates and 13 stock market indices using the standard Lagrange Multiplier [LM] test for GARCH and a new LM test that is resistant to additive outliers. The data...

Using a standard 4-variable linear vector error correction model (VECM), we first show that the null hypothesis of linearity can be strongly rejected against the alternative of smooth transition autoregressive nonlinearity. An important result from this stage of the analysis is that the...

A closed fishing season is arguably the most important fisheries regulation measure implemented by the government of India in the new millennium. Applied mainly to the inshore trawl fishing fleet, the planners' intention was a safe-guarding of capture fisheries. This article, which is based on...

In this paper the issue of detecting and handling outliers in the GARCH(1,1) model is addressed. Simulation evidence shows that neglecting even a single outlier has a dramatic on parameter estimates. To detect and correct for outliers, we propose an adaptation of the iterative in Chen and Liu...

One of the stylized facts in financial and international economics is that of increasing predictability of variables such as exchange rates and stock returns at longer horizons. This fact is based upon applications of long horizon regressions, from which the typical findings are that the point...