Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML) estimation with variance targeting, which reduces the degree of parameterization and facilitates estimation. We compare the two approaches and investigate, via simulations, how non-normality features of the...

We provide empirical evidence of volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Using recently-developed methodologies to detect jumps from high frequency price data, we estimate the size of positive and negative jumps and...

The objective of this paper is to analyze what are the main determinants of the exchange rate risk premium (ERP). The empirical case is conducted for the daily Mexican peso-USD exchange rate for a sample period from 2007 until 2015. According to the results the ERP is influenced by several...

In this paper we investigate the statistical properties of cryptocurrencies by using alpha-stable distributions. We also study the benefits of the Metcalfe's law (the value of a network is proportional to the square of the number of connected users of the system) for the evaluation of...

This paper proposes a generalization of the class of realized semivariance and semicovariance measures introduced by Barndorff-Nielsen, Kinnebrock and Shephard (2010) and Bollerslev, Li, Patton and Quaedvlieg (2020a) to allow for a finer decomposition of realized (co)variances. The new "realized...

This paper examines exchange-rate volatility with GARCH models using monthly exchange-rate return series from 1985:1 to 2011:7 for Naira/US dollar return and from 2004:1 to 2011:7 for Naira/British Pounds and Naira/Euro returns. The study compare estimates of variants of GARCH models with break...

We introduce the class of FloGARCH models in this paper. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the...

Volatility proxies like Realised Volatility (RV) are extensively used to assess the forecasts of squared financial return produced by Autoregressive Conditional Heteroscedasticity (ARCH) models. But are volatility proxies identified as expectations of the squared return? If not, then the results...

This paper develops a novel approach to modeling and forecasting realized volatility (RV) measures based on copula functions. Copula-based time series models can capture relevant characteristics of volatility such as nonlinear dynamics and long-memory type behavior in a flexible yet parsimonious...