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Improving the CARR model using extreme range estimators

Miralles Marcelo, José Luis; Miralles-Quirós, José Luis - In: Applied financial economics 23 (2013) 19/21, pp. 1635-1647

Persistent link: https://www.econbiz.de/10010259753

Forecasting volatility with component conditional autoregressive range model

Wu, Xinyu; Hou, Xinmeng - In: The North American journal of economics and finance : a … 51 (2020), pp. 1-13

Persistent link: https://www.econbiz.de/10012659660

Measuring and comparing the value-at-risk using GARCH and CARR models for CSI 300 index

Wu, Chunchou - In: Theoretical economics letters 8 (2018) 6, pp. 1179-1187

Persistent link: https://www.econbiz.de/10011888169

Forecasting sectoral trade growth under flexible exchange rates

Herwartz, Helmut; Weber, Henning - 2003

A huge body of empirical and theoretical literature has emerged on the relationship between exchange rate uncertainty and international trade. In empirical studies the estimated impacts of exchange rate uncertainty on trade figures are at most weak and often ambiguous with respect to their...

Persistent link: https://www.econbiz.de/10010296440

Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts

Benavides, Guillermo; Capistrán, Carlos - 2009

This paper provides empirical evidence that combinations of option implied and time series volatility forecasts that are conditional on current information are statistically superior to individual models, unconditional combinations, and hybrid forecasts. Superior forecasting performance is...

Persistent link: https://www.econbiz.de/10010322599

Stock Index Volatility Forecasting with High Frequency Data

Hol, Eugenie; Koopman, Siem Jan - 2002

Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … outperforms the GARCH model. …

Persistent link: https://www.econbiz.de/10010324972

A multivariate perspective for modelling and forecasting inflation's conditional mean and variance

Barigozzi, Matteo; Capasso, Marco - 2007

Dynamic Factor GARCH (DF-GARCH), is used here to exploit the relations between inflation and the other macroeconomic variables … for inflation forecasting purposes. The DF-GARCH is a dynamic factor model with the additional assumption of conditionally … heteroskedastic dynamic factors. When comparing the Dynamic Factor GARCH with univariate models and with the traditional dynamic …

Persistent link: https://www.econbiz.de/10010328579

Generalized dynamic factor model + GARCH exploiting multivariate information for univariate prediction

Alessi, Lucia; Barigozzi, Matteo; Capasso, Marco - 2007

GARCH model. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the … a GARCH.We compare GDFM+GARCH and standard GARCH performance on two samples up to 171 series, providing one …-step-ahead volatility predictions of returns. The GDFM+GARCH model outperforms the standard GARCH in most cases. These results are robust …

Persistent link: https://www.econbiz.de/10010328627

Comparing and evaluating Bayesian predictive distributions of assets returns

Geweke, John; Amisano, Gianni - 2008

Bayesian inference in a time series model provides exact, out-of-sample predictive distributions that fully and coherently incorporate parameter uncertainty. This study compares and evaluates Bayesian predictive distributions from alternative models, using as an illustration five alternative...

Persistent link: https://www.econbiz.de/10011605015

Optimal Prediction Pools

Geweke, John; Amisano, Gianni - 2009

A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave...

Persistent link: https://www.econbiz.de/10011605063

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