scholarly journals High-Dimensional Estimation of Quadratic Variation Based on Penalized Realized Variance

2021 ◽  
Author(s):  
Kim Christensen ◽  
Mikkel Slot Nielsen ◽  
Mark Podolskij
Keyword(s):  
Quadratic Variation ◽  
High Dimensional ◽  
Realized Variance

scholarly journals Estimating quadratic variation using realized variance

2002 ◽  
Vol 17 (5) ◽  
pp. 457-477 ◽  
Author(s):  
Ole E. Barndorff-Nielsen ◽  
Neil Shephard
Keyword(s):  
Quadratic Variation ◽  
Realized Variance

PRICING JOINT CLAIMS ON AN ASSET AND ITS REALIZED VARIANCE IN STOCHASTIC VOLATILITY MODELS

2013 ◽  
Vol 16 (01) ◽  
pp. 1350005 ◽  
Author(s):  
LORENZO TORRICELLI
Keyword(s):  
Stochastic Volatility ◽  
Realized Volatility ◽  
Numerical Results ◽  
Quadratic Variation ◽  
Stochastic Volatility Model ◽  
Realized Variance ◽  
Joint Function ◽  
Stochastic Volatility Models ◽  
Volatility Models ◽  
Volatility Model

In the setting of a stochastic volatility model, we find a general pricing equation for the class of payoffs depending on the terminal value of a market asset and its final quadratic variation. This provides a pricing tool for European-style claims paying off at maturity a joint function of the underlying and its realized volatility or variance. We study the solution under various specific stochastic volatility models, give a formula for the computation of the delta and gamma of these claims, and introduce some new interesting payoffs that can be valued by means of the general pricing equation. Numerical results are given and compared to those from plain vanilla derivatives.

INFERENCE FOR THE JUMP PART OF QUADRATIC VARIATION OF ITÔ SEMIMARTINGALES

2009 ◽  
Vol 26 (2) ◽  
pp. 331-368 ◽  
Author(s):  
Almut E.D. Veraart
Keyword(s):  
Asymptotic Theory ◽  
Asymptotic Variance ◽  
Quadratic Variation ◽  
Estimation Bias ◽  
Finite Sample ◽  
Realized Variance ◽  
Modeling Framework ◽  
Limit Theory ◽  
Monte Carlo Studies ◽  
The Difference

Recent research has focused on modeling asset prices by Itô semimartingales. In such a modeling framework, the quadratic variation consists of a continuous and a jump component. This paper is about inference on the jump part of the quadratic variation, which can be estimated by the difference of realized variance and realized multipower variation. The main contribution of this paper is twofold. First, it provides a bivariate asymptotic limit theory for realized variance and realized multipower variation in the presence of jumps. Second, this paper presents new, consistent estimators for the jump part of the asymptotic variance of the estimation bias. Eventually, this leads to a feasible asymptotic theory that is applicable in practice. Finally, Monte Carlo studies reveal a good finite sample performance of the proposed feasible limit theory.

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