index returns. The proposed models capture key stylized facts of such returns, namely heavy tails, asymmetry, volatility … asymmetric. Volatility is modeled parametrically. The new model is applied to the daily returns of the S\&P 500, FTSE 100, and … EUROSTOXX 50 indices and is compared to GARCH, Stochastic Volatility, and other Bayesian semi-parametric models …

This paper examines a wide variety of popular volatility models for stock index return, including Random Walk model … volatility clustering, alike to findings in previous studies. Each market contains different GARCH models, which fit well. From … the estimation, we find that the volatility of the return and the jump probability were significantly higher after 27 …

nonlinearity and asymmetry in the drift, and incorporates the level effect and stochastic volatility in the diffusion function is … asymmetric drift of the short rate, and the presence of nonlinearity, GARCH, and level effects in its volatility. The empirical … volatility of interest rate changes …

forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve …

This paper investigates the relevance of skewed Student-t distributions in capturing long memory volatility properties … selection in the context of long memory volatility and enhance the performance of risk management in Japanese financial markets …

Volatility is a key measure of risk in financial analysis. The high volatility of one financial asset today could … affect the volatility of another asset tomorrow. These lagged effects among volatilities - which we call volatility … freedom. Moreover, we study volatility spillovers among a large number of assets. To this end, we use penalized estimation of …