Search
Search Results
-
Stochastic Volatility and Realized Stochastic Volatility Models
This treatise delves into the latest advancements in stochastic volatility models, highlighting the utilization of Markov chain Monte Carlo...
-
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...
-
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...
-
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...
-
-
Multiple Measures Realized GARCH Models
Realized volatility has become the most popular empirical measure in fitting and forecasting volatility. However, as the properties of this class of...
-
Interpolation of missing swaption volatility data using variational autoencoders
Albeit of crucial interest for financial researchers, market-implied volatility data of European swaptions often exhibit large portions of missing...
-
Sparse vector heterogeneous autoregressive modeling for realized volatility
We propose a sparse vector heterogeneous autoregressive (VHAR) model for realized volatility forecasting. As a multivariate extension of a...
-
Efficient parameter estimation for parabolic SPDEs based on a log-linear model for realized volatilities
We construct estimators for the parameters of a parabolic SPDE with one spatial dimension based on discrete observations of a solution in time and...
-
Long and Short–Run Dynamics in Realized Covariance Matrices: A Robust MIDAS Approach
A recent stream of the econometric literature is devoted to modelize unobservable short and long–run components in volatility and time–varying...
-
Variance Swaps Under Multiscale Stochastic Volatility of Volatility
Many hedge funds and retail investors demand volatility and variance derivatives in order to manage their exposure to volatility and...
-
Stochastic Volatility Model with Generalized Hyperbolic Skew Student’s t Error
In this chapter, we extend the ASV model introduced in Chap. 3 , by applying a skew Student’s t...
-
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,...
-
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....
-
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...
-
Forecasting realized volatility: A review
Forecast methods for realized volatilities are reviewed. Basic theoretical and empirical features of realized volatilities as well as versions of...
-
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...
-
Multivariate Volatility Modeling
The models of volatility in Chap. 8 are univariate, i.e., they model the volatility quite independently on other time series. It may be a drawback...
-
Dynamic tail inference with log-Laplace volatility
We present a stochastic volatility modeling method that enables flexible and computationally efficient estimation of time-varying extreme event...
-
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...
