Abstract This study considers the long memory and fractional integration in range-based volatilities across 30 currencies against USD. Graphical analysis of autocorrelation function at lags pole near zero frequencies periodogram suggests existence integration. We apply semi-parametric methods to measure long-range dependence. find a decrease estimates with an increase bandwidth, which indicates...

We discovered that past changes in the market correlation structure are significantly related with future changes in the market volatility. By using correlationbased information filtering networks we device a new tool for forecasting the market volatility changes. In particular, we introduce a new measure, the “correlation structure persistence”, that quantifies the rate of change of the market...

Abstract We examine stock return autocorrelation at various quantiles of the returns' distribution and use it to forecast volatility. Our empirical results show that strength autoregression varies across in terms both magnitude persistence. Specifically, order coefficients is lower left tail comparison with right tail. Additionally, we quantile autoregressive (QAR) framework proposed this study...

We consider semiparametric estimation of the memory parameter in a model which includes as special cases both the long-memory stochastic volatility (LMSV) and fractionally integrated exponential GARCH (FIEGARCH) models. Under our general model the logarithms of the squared returns can be decomposed into the sum of a long-memory signal and a white noise. We consider periodogram-based estimators ...

Several authors, including Andersen and Bollerslev (1998), stress the importance of long-term volatility dependence for value-at-risk (VaR) prediction. The present paper addresses multiple-period market risk forecasts under long memory persistence in market volatility. To this aim, we propose volatility forecasts based on a combination of the GARCH(1,1)-model with potentially fat-tailed and ske...

High frequency financial data is irregularly spaced in time. The information content that determines the time between subsequent trades introduces is potentially related to volatility. We introduce a new continuous time model to jointly model stock return and duration between trades. This model include a bivariate Ornstein-Uhlenbeck process for two latent processes: log-volatility of stock retu...

The slump of the Canadian Dollar against the U.S. Dollar over the last years has led many observers to speculate that the Canadian Dollar is in secular decline, prompting demands for a U.S.-dollarization or even a common currency for North America. However, there is no robust empirical evidence to support the notion of a ‘secular’ decline. An econometric model of the nominal USD/CAD exchange ra...

Recent theoretical developments tend to suggest that rare disaster risks enhance the persistence of uncertainty. Given this, we analyse impact climate (temperature growth or its volatility), as proxies for such unusual events, on economic and policy-related uncertainty 50 US states in a panel data set-up, over monthly period 1984:03 2019:12. Using impulse response functions (IRFs) from regime-b...