volatility, correlation, beta, quadratic variation, jump variation, and other functionals of an underlying continuous …

, focusing on the relationship between returns and conditional volatility. The conditional mean follows a GARCH-M model, while … PGARCH) were tested. We examine how accurately these GARCH models forecast volatility under various error distributions … the following: (i) the Macedonian stock returns time series display stylized facts such as volatility clustering, high …

In theory, the price of equity is determined by the dividend yields and growth potentials of the firms. There exists … established empirical proof of the impact of macroeconomic changes to the equity markets. With the advent of Islamic equities, and … initial empirical proof for further research on the impact of specific economic variables on the changes in Islamic equity …

prices. We empirically assess efficiency gains in volatility estimation when using range-based estimators as opposed to … simple daily ranges and explore the use of these more efficient volatility measures as predictors of daily ranges. The array … forecasts are produced by a realized range based HAR model with a GARCH volatility-of-volatility component. …

Markov-switching models, improves estimates and forecasts of stock return volatility over those of the more conventional …

/noncausal model is more representative of the data according to the Kullback-Leibler measure. Moreover, these estimation results …

financial assets with fat tails, asymmetry, periodic behaviors in the conditional variances, and volatility clustering. The gold …

One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC …

One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC …