A Model Confidence Set approach to the combination of multivariate volatility forecasts

Amendola, Alessandra; Braione, Manuela; Candila, Vincenzo; Storti, Giuseppe

doi:10.1016/j.ijforecast.2019.10.001

Cited by 19 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Future research may compare our estimators with other relevant estimators, either parametric or nonparametric, for stochastic volatility. Finally, another idea for future research would be to expand our estimators in a multivariate framework (see Amendola et al 2020) or extend them to allow for jumps in the equation driving the observable variable.…”

Section: Discussionmentioning

confidence: 99%

Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility

Alghalith

Floros

Γκίλλας

2020

Risks

View full text Add to dashboard Cite

We propose novel nonparametric estimators for stochastic volatility and the volatility of volatility. In doing so, we relax the assumption of a constant volatility of volatility and therefore, we allow the volatility of volatility to vary over time. Our methods are exceedingly simple and far simpler than the existing ones. Using intraday prices for the Standard & Poor’s 500 equity index, the estimates revealed strong evidence that both volatility and the volatility of volatility are stochastic. We also proceeded in a Monte Carlo simulation analysis and found that the estimates were reasonably accurate. Such evidence implies that the stochastic volatility models proposed in the literature with constant volatility of volatility may fail to approximate the discrete-time short rate dynamics.

show abstract

Section: Discussionmentioning

confidence: 99%

Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility

Alghalith

Floros

Γκίλλας

2020

Risks

View full text Add to dashboard Cite

show abstract

“…In particular, the contribution of this work is twofold. First, we analyze the interdependencies among seven cryptocurrencies for the period 2017-2020 by using a set of popular DCC specifications-that is, the corrected DCC (cDCC) of Aielli (2013), the DCC-MIDAS of Colacito et al (2011), and the Dynamic Equicorrelation (DECO) of Engle and Kelly (2012), as well as a popular non-parametric specification, the RiskMetrics (RM) model, which is also used in Amendola et al (2020). The DCC class of models is a standard tool for investigating the dynamic interdependencies between financial assets (Hemche et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Cryptocurrencies

Candila

2021

Econometrics

Self Cite

View full text Add to dashboard Cite

Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are more than 8525 cryptocurrencies with a market value of approximately USD 1676 billion. These particular assets can be used to diversify the portfolio as well as for speculative actions. For this reason, investigating the daily volatility and co-volatility of cryptocurrencies is crucial for investors and portfolio managers. In this work, the interdependencies among a panel of the most traded digital currencies are explored and evaluated from statistical and economic points of view. Taking advantage of the monthly Google queries (which appear to be the factors driving the price dynamics) on cryptocurrencies, we adopted a mixed-frequency approach within the Dynamic Conditional Correlation (DCC) model. In particular, we introduced the Double Asymmetric GARCH–MIDAS model in the DCC framework.

show abstract

“…Due to the correlation structure of the models, we consider three robust loss functions (Laurent et al 2013) based on the distance between the predicted conditional covariance matrix and its proxy, built as the cross-products of the daily log-returns. The same loss functions have also been used in Amendola et al (2020), among others.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach

Andreani

Candila

Morelli

et al. 2021

Risks

Self Cite

View full text Add to dashboard Cite

This paper shows the effects of the COVID-19 pandemic on energy markets. We estimate daily volatilities and correlations among energy commodities relying on a mixed-frequency approach that exploits information from the number of weekly deaths related to COVID-19 in the United States. The mixed-frequency approach takes advantage of the MIxing-Data Sampling (MIDAS) methods. We compare our results to those obtained by employing two well-known models that do not account for the COVID-19 low-frequency variable, namely the Dynamic EquiCorrelation (DECO) and corrected Dynamic Conditional Correlation (cDCC). Moreover, we consider four possible specifications of the volatility: GARCH, GJR, GARCH-MIDAS, and Double-Asymmetric GARCH-MIDAS. The empirical results show that our approach is statistically superior to other models and represents a valuable methodology that can be used for risk managers, investors, and policy makers to assess the effects of the pandemic on spillovers effects in energy markets.

show abstract

A Model Confidence Set approach to the combination of multivariate volatility forecasts

Cited by 19 publications

References 43 publications

Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility

Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility

Multivariate Analysis of Cryptocurrencies

Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach

Contact Info

Product

Resources

About