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Mixtures of generalized normal distributions and EGARCH models to analyse returns and volatility of ESG and traditional investments
Environmental, social and governance (ESG) criteria are increasingly integrated into investment process to contribute to overcoming global...
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Natural language processing and financial markets: semi-supervised modelling of coronavirus and economic news
This paper investigates the reactions of US financial markets to press news from January 2019 to 1 May 2020. To this end, we deduce the content and...
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Iterative QML estimation for asymmetric stochastic volatility models
The paper illustrates a new procedure for estimating asymmetric stochastic volatility models. These models shape the asymmetric effect of negative...
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Understanding relationships with the Aggregate Zonal Imbalance using copulas
In the Italian electricity market, we analyze the Aggregate Zonal Imbalance, which is the algebraic sum, changed in sign, of the amount of energy...
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Realized Stochastic Volatility Model
In this chapter, we further extend the SV model by incorporating a model-free volatility estimator called realized volatility. The realized...
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Volatility forecasting using deep recurrent neural networks as GARCH models
Estimating and predicting volatility in time series is of great importance in different areas where it is required to quantify risk based on...
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Financial Time Series and Related Models
Financial time series analysis has been one of the hottest research topics in the recent decades. In this chapter, we illustrate the stylized facts...
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Introduction
This chapter provides a brief background on the developments in stochastic volatility models, including Markov chain Monte Carlo simulation for the...
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Asymmetric Stochastic Volatility Model
It has long been recognized in stock markets that there is a negative correlation between today’s return and tomorrow’s volatility [1, 3]. This...
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Bayesian inference of multiple structural change models with asymmetric GARCH errors
Structural change in any time series is practically unavoidable, and thus correctly detecting breakpoints plays a pivotal role in statistical...
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Estimation and decomposition of food price inflation risk
Ensuring aggregate food price stability requires a forward-looking assessment of the risk that unexpected deviations in individual food items’...
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Modelling Volatility in Finance and Economics: ARCH, GARCH and EGARCH Models
In time series analyses, just as in regression, it is assumed that the residuals (or errors) are homoscedastic. In a seminal article, Engel (1982)...
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Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid
Appropriate risk management is crucial to ensure the competitiveness of financial institutions and the stability of the economy. One widely used...
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Maximum Likelihood With a Time Varying Parameter
We consider the problem of tracking an unknown time varying parameter that characterizes the probabilistic evolution of a sequence of independent...
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Quantile forecasts for financial volatilities based on parametric and asymmetric models
For financial volatilities such as realized volatility and volatility index, a new parametric quantile forecast strategy is proposed, focusing on...
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Volatility of Financial Time Series
The models introduced in previous chapters can be mostly considered as linear models (e.g., the linear process from Sect. 6.2 is linear function...
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Goodness-of-fit tests for Log-GARCH and EGARCH models
This paper studies goodness-of-fit tests and specification tests for an extension of the Log-GARCH model, which is both asymmetric and stable by...
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Wavelet-L2E Stochastic Volatility Models: an Application to the Water-Energy Nexus
Forecasting commodity markets are difficult due to the time-varying nature and complexity of the financial return series representing these markets....
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Bayesian inference of multivariate-GARCH-BEKK models
The main aim of this paper is to present a Bayesian analysis of Multivariate GARCH( l , m ) (M-GARCH) models including estimation of the coefficient...
