We propose a new class of financial volatility models, which we call the REcurrent Conditional Heteroskedastic (RECH) to improve both in-sample analysis and out-of-sample forecast performance traditional conditional heteroskedastic models. In particular, incorporate auxiliary deterministic processes, governed by recurrent neural networks, into variance e.g. GARCH-type flexibly capture dynamics ...

The catastrophe that the world is now facing in form of COVID-19, has affected most economies and financial markets as a result lockdown, travelling restrictions, social distances. present study attempted to investigate effects COVID-19 on stock returns Pakistan Stock Exchange. data employed comprises daily prices Exchange, value exchange rate over period 01 January 2011 30 April 2021, dummy va...

In this paper we consider a nonparametric regression model in which the conditional variance function is assumed to vary smoothly with the predictor. We offer an easily implemented and fully Bayesian approach that involves the Markov chain Monte Carlo sampling of standard distributions. This method is based on a technique utilized by Kim, Shephard, and Chib (1998) for the stochastic volatility ...

Forecasting of the Renewable Energy plays a major role in optimal decision formula for government and industrial sector in Bangladesh. This research is based on time series modeling with special application to solar energy data for Dhaka city. Three families of time series models namely, the autoregressive integrated moving average models, Holt’s linear exponential smoothing, and the autoregres...

This study investigates the impact of oil price variations, exchange rates, macroeconomic factors and domestic stock volatility in South Asian countries. employs a general autoregressive heteroskedastic (GARCH) model using quarterly data from 2001 to 2021. The results reveal that there is significant relationship between credit factors. We find surprising for set countries; negative asset portf...