The aim of this study is to examine the equity premium puzzle in Iran for the quarterly period of 1993-2016. In this regard, the hybrid bivariate Garch model and also fuzzy dummy variables with consumption capital asset pricing model (C-CAPM) have been used. The results of study show that using C-CAPM within fuzzy dummy variables (CCAPM-F), the relative risk aversion coefficient of investor is ...

This paper empirically investigates the relationship between CPI inflation uncertainty, and private investment in the Iranian economy from 1988 to 2010 by using quarterly data. We employ a bivariate VAR(5)-GARCH(1,1)-in-mean with diagonal BEKK model to discover in a unified framework how are the interactions between the variables. In the model, conditional variance of inflation and private inve...

This paper proposes an efficient approach for computing the prices of American style options in the GARCH framework. Rubinstein’s (1998) Edgeworth tree idea is combined with the analytical formulas for moments of the cumulative return under GARCH developed in Duan et al. (1999, 2002) to yield a simple recombining binomial tree for option valuation in the GARCH context. Since the resulting tree ...

We propose a new model for volatility forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the GARCH model. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series of returns. In this financial analysis, both these components are modeled as a GARCH. We compare GDFM+GARCH and ...

Nowadays many researchers use GARCH models to generate volatility forecasts. However, it is well known that volatility persistence, as indicated by the sum of the two parameters G1 and A1[1], in GARCH models is usually too high. Since volatility forecasts in GARCH models are based on these two parameters, this may lead to poor volatility forecasts. It has long been argued that this high persist...

To assess the time-varying dynamics in value-at-risk (VaR) estimation, this study has employed an integrated approach of dynamic conditional correlation (DCC) and generalized autoregressive heteroscedasticity (GARCH) models on daily stock return emerging markets. A log-returns three leading indices such as KSE100, KSE30, KSE-ALL from Pakistan Stock Exchange SSE180, SSE50 SSE-Composite Shanghai ...

We analyze the systemic risk of Italian banks with ΔCoVaR from a bivariate normal GARCH model. The results show that it is good measure and applicable to ranking other systemically important institutions. Using an elastic-net approach, we identify balance sheet market variables explain banks. analysis confirms these are key determinants importance highlights how higher capitalization beneficial...

In this paper, we introduce a two−dimensional Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model for clutter modeling and anomaly detection. The one−dimensional GARCH model is widely used for modeling financial time series. Extending the one−dimensional GARCH model into two dimensions yields a novel clutter model which is capable of taking into account important characteris...

This paper develops a closed-form option pricing formula for a spot asset whose variance follows a GARCH process. The model allows for correlation between returns of the spot asset and variance and also admits multiple lags in the dynamics of the GARCH process. The single-factor (one-lag) version of this model contains Heston’s (1993) stochastic volatility model as a diffusion limit and therefo...