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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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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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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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Nonparametric Bayesian volatility learning under microstructure noise
In this work, we study the problem of learning the volatility under market microstructure noise. Specifically, we consider noisy discrete time...
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The effect of intraday periodicity on realized volatility measures
We focus on estimating daily integrated volatility ( IV ) by realized measures based on intraday returns following a discrete-time stochastic model...
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Capturing Measurement Error Bias in Volatility Forecasting by Realized GARCH Models
This paper proposes generalisations of the Realized GARCH model, in three different directions. First, heteroskedasticity of the noise term in the...
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Robust Optimal Investment Strategies for Mean-Variance Asset-Liability Management Under 4/2 Stochastic Volatility Models
This paper considers a robust optimal investment problem for an ambiguity-averse asset-liability manager under the mean-variance criterion in the...
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Non-parametric seasonal unit root tests under periodic non-stationary volatility
This paper presents a new non-parametric seasonal unit root testing framework that is robust to periodic non-stationary volatility in innovation...
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Distribution-free specification test for volatility function based on high-frequency data with microstructure noise
In this paper, we propose a two-step test for parametric specification of volatility function based on high-frequency data with microstructure noise....
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Change point in variance of fractionally integrated noise
This paper studies the quasi-maximum likelihood estimator (quasi-MLE) of a change point in variance for the fractionally integrated noise with memory...
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Trend and cycle decomposition of Markov switching (co)integrated time series
In this paper we derive the Beveridge–Nelson (BN) decomposition and the state space representation for various multivariate (co)integrated time...
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Local SIML estimation of some Brownian and jump functionals under market micro-structure noise
This paper is a contribution to a special issue on Data Science: Present and Future , because the main topic has been and will be in an active area of...
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Estimation of Tempered Stable Lévy Models of Infinite Variation
Truncated realized quadratic variations (TRQV) are among the most widely used high-frequency-based nonparametric methods to estimate the volatility...
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Correcting spot power variation estimator via Edgeworth expansion
In this paper, we propose an estimator of power spot volatility of order p through Edgeworth expansion. We provide a precise description of how to...
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Flexible Bayesian Inference for Diffusion Processes using Splines
We introduce a flexible method to simultaneously infer both the drift and volatility functions of a discretely observed scalar diffusion. We...
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The SIML method without microstructure noise
The SIML (abbreviation of Separating Information Maximal Likelihood) method, has been introduced by N. Kunitomo and S. Sato and their collaborators...
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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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An integrated framework for visualizing and forecasting realized covariance matrices
This paper proposes an integrated framework for visualizing and forecasting realized covariance matrices to enable the efficient construction and...
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A Component Multiplicative Error Model for Realized Volatility Measures
We propose a component Multiplicative Error Model (MEM) for modelling and forecasting realized volatility measures. In contrast to conventional MEMs,...