in this paper, in order to optimize the portfolio consisting of selected industrial stocks of petroleum products, automobiles and parts, electrical industry and extraction of minerals from tehran stock exchange member, first, time – varying conditional covariance matrix has been estimated based on the following multivariate garch models: diagonal-vech (1,1), ccc (1,1) and diagonal -bekk (1,1). ...

The Covid-19 pandemic increased uncertainty in the Indonesian stock market. This paper aims to investigate foreign and domestic investors' behavior market, especially during pandemic. method used was E-GARCH DCC-GARCH. results showed that impact of shocks from bad good news greater on return volatility had a positive increase spillover effect NFI's short long term before pandemic, but this did ...

Wind power is the fastest growing renewable energy source in the United States. Existing empirical studies on the economic impact of wind power development have primarily relied on case studies and input-output models, which have limitations. This study instead uses a linear fixed effects model to estimate the county level effects of wind power in the Great Plains region of the United States fr...

Abstract This paper investigates the linkage of returns and volatilities between United States Chinese stock markets from January 2010 to March 2020. We use dynamic conditional correlation (DCC) asymmetric Baba–Engle–Kraft–Kroner (BEKK) GARCH models calculate time-varying correlations these two examine return volatility spillover effects markets. The empirical results show that there are only u...

Modeling the dependency between stock market returns is a difficult task when returns follow a complicated dynamics. It is not easy to specify the multivariate distribution relating two or more return series. In this paper, a methodology based on fitting ARIMA, GARCH and ARMA-GARCH models and copula functions is applied. In such methodology, the dependency parameter can easily be rendered condi...

Financial returns exhibit common behavior described at best by factor models, but also fat tails, which may be captured by α-stable distributions. This paper concentrates on estimating factor models with multivariate α-stable distributed and independent factors and idiosyncratic noises under the assumption of time constant distribution (static factor models) or time-varying conditional distribu...

Volatility is a key parameter used in many financial applications, from derivatives valuation to asset management and risk management. Volatility measures the size of the errors made in modeling returns and other financial variables. It was discovered that, for vast classes of models, the average size of volatility is not constant but changes with time and is predictable. Autoregressive conditi...

We test the importance of multivariate information for modelling and forecasting inflation’s conditional mean and variance. In the literature, the existence of inflation’s conditional heteroskedasticity has been debated for years, as it seemed to appear only in some datasets and for some lag lengths. This phenomenon might be due to the fact that inflation depends on a linear combination of econ...