Multivariate GARCH

The stock exchange, as a part of the capital market, in case of necessary conditions, can mobilize national capital and direct it towards economic growth. A secure environment where managers are working towards stockholders for investment and information asymmetry is considered to be the features of a good business. This research seeks to investigate whether information asymmetry of the financial market and macroeconomic growth can affect investors’ confidence ration. Our model is estimated using MGARCH for seasonal data of the stock market in Iran and the real GDP during the period from 1991-2016. The findings of the research have shown a significant effect of macroeconomic growth and information asymmetry of the financial market on the investors’ confidence. There is also a two-way gradient relationship between the information asymmetry and the confidence of investors in the short run. There is a cointegration relationship between these three variables.

Multiplicity of the companies experiencing financial distress in different countries and as a consequence, their bankruptcy and the impacts on other companies have necessitated conducting research on methods of prediction of such conditions and also their effects on other companies in the market. In this regard, this research has investigated the financial distress spillover in the automobile supply chain companies. For doing so, the methods of default probability time series KMV and the distance from default of four supply chain companies of Iran Khodro and four supply chain companies of SAIPA were calculated. Then, the financial distress spillover in these two major companies was measured in separated models using multivariate GARCH model. The results of the default probability of Iran Khodro companies showed that the default probability with pause of Khodro on the default probability of supply chain companies was significant and negative in 10% level. The results for SAIPA supply chain companies revealed that the default probability with pause of Khaspa had an impact on default probability of Kaspa, Pask and Khazin in significance level of 10%.

This paper examines the hedging effectiveness of gold futures for the stock market in minimizing variance and downside risks, including value at risk and expected shortfall using data from the Iran emerging capital market during four different sub-periods from December 2008 to August 2018. We employ dynamic conditional correlation models including VARMA-BGARCH (DCC, ADCC, BEKK, and ABEKK) and copula-GARCH with different copula functions to estimate volatilities and conditional correlations between Iran gold futures contract return and Tehran stock exchange main index return. The empirical results reveal that the dynamic conditional correlations switch between positive and near-zero values over the period under study. These correlations are high and positive during the major national currency devaluation and are low near to zero during other times. Out-of-sample one-step-ahead forecasts based on rolling window analysis show that DCC and ADCC multivariate GARCH models outperform other models for variance reduction, while a more interesting finding is that the copula-GARCH model outperforms other models for downside risks reduction.