In this paper, we analyze five of the most popular models proposed to represent conditional heteroscedasticity with leverage effect, namely, GQARCH, TGARCH, GJR, EGARCH, and APARCH. We show that when the parameters satisfy the positivity, stationarity, and finite kurtosis conditions, the...

In this paper, we propose a long memory asymmetric volatility model, which captures more flexible asymmetric patterns as compared with several existing models. We extend the new specification to realized volatility (RV) by taking account of measurement errors and use the Efficient Importance...

This paper proposes a new intraday volatility forecasting model, particularly suitable for modeling a large number of assets. We decompose volatility of high-frequency returns into components that may be easily interpreted and estimated. The conditional variance is a product of daily, diurnal,...

We propose an affine term structure model which accommodates nonlinearities in the drift and volatility function of the short-term interest rate. Such nonlinearities are a consequence of discrete beta-distributed regime shifts constructed on multiple thresholds. We derive iterative closed-form...

Maximum-likelihood estimates of the parameters of stochastic differential equations are consistent and asymptotically efficient, but unfortunately difficult to obtain if a closed-form expression for the transitional probability density function of the process is not available. As a result, a...

An aggregated method of nonparametric estimators based on time-domain and state-domain estimators is proposed and studied. To attenuate the curse of dimensionality, we propose a factor modeling strategy. We first investigate the asymptotic behavior of nonparametric estimators of the volatility...

This article proposes a bias-adjusted estimator for use in cointegrated panel regressions when the errors are cross-sectionally correlated through an unknown common factor structure. The asymptotic distribution of the new estimator is derived and is examined in small samples using Monte Carlo...

We use the pattern recognition algorithm of Lo, Mamaysky, and Wang (2000) with some modifications to determine whether 'head-and-shoulders' (HS) price patterns have predictive power for future stock returns. The modifications include the use of filters based on typical price patterns identified...

The well-known ARCH/GARCH models for financial time series have been criticized of late for their poor performance in volatility prediction, that is, prediction of squared returns.-super-1 Focusing on three representative data series, namely a foreign exchange series (Yen vs. Dollar), a stock...

We use a Bayesian time-varying coefficient model to measure contagion and interdependence, and we apply it to the Chilean FX market during the 2001 Argentine crisis. The proposed framework works in the joint presence of heteroskedasticity and omitted variables, without knowledge of the crisis...