This paper proposes a constrained nonlinear programming view of generalized autoregressive conditional heteroskedasticity (GARCH) volatility estimation models in financial econometrics. These models are usually presented to the reader as unconstrained optimization models with recursive terms in the literature, whereas they actually fall into the domain of nonconvex nonlinear programming. Our re...

Quasi maximum likelihood estimation and inference in multivariate volatility models remains a challenging computational task if, for example, the dimension is high. One of the reasons is that typically numerical procedures are used to compute the score and the Hessian, and often they are numerically unstable. We provide analytical formulae for the score and the Hessian and show in a simulation ...

when the past observations are correlated with future observations and their correlation is significant, the time series has long memory. in this paper the contagion effect of volatilities, with consideration of long-run effect, is investigated. the basic model is bekk (1, 1) and fbekk (1,d,1), model extended long-run memory parameter (d) is considered and estimated. furthermore in this paper p...

Com a utilização de dados diários do retorno das ações, da cotação Dólar e índice Ibovespa, durante o período 2010 até 2020, este estudo apresenta os resultados dos transbordamentos volatilidades no mercado ações Brasil, com correlações condicionais. A construção retornos representativos pela Análise Componentes Principais, tradicional robusto se mostraram mais adequadas em relação à Principais...

Duisenberg school of finance is a collaboration of the Dutch financial sector and universities, with the ambition to support innovative research and offer top quality academic education in core areas of finance. Abstract Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and ...

This study analyzes the volatility spillover effects in US stock market (S&P500) and cryptocurrency (BGCI) using intraday data during COVID-19 pandemic. As potential drivers of portfolio diversification, we measure asymmetric transmission on both markets. We apply MGARCH-BEKK algorithm-based GA2M machine learning model. The negative shocks to returns impact S&P500 more than positive als...

in this paper, first a great number of inverse problems which arise in instrumentation, in computer imaging systems and in computer vision are presented. then a common general forward modeling for them is given and the corresponding inversion problem is presented. then, after showing the inadequacy of the classical analytical and least square methods for these ill posed inverse problems, a baye...

This paper investigates the forecasting ability of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model. The paper also compares the forecasting ability of the non-GARCH model the Kalman method. Forecast errors based on twenty UK company weekly stock return (based on timevary beta) forecasts ...