scholarly journals Variance reduction estimation for return models with jumps using gamma asymmetric kernels

2019 ◽  
Vol 23 (5) ◽  
Author(s):  
Yuping Song ◽  
Weijie Hou ◽  
Shengyi Zhou
Keyword(s):  
Simulation Study ◽  
Bias Correction ◽  
High Frequency ◽  
Variance Reduction ◽  
Stock Exchange ◽  
High Frequency Data ◽  
Weak Consistency ◽  
Frequency Data ◽  
Finite Sample ◽  
Nonparametric Estimators

Abstract This paper discusses Nadaraya-Watson estimators for the unknown coefficients in second-order diffusion model with jumps constructed with Gamma asymmetric kernels. Compared with existing nonparametric estimators constructed with Gaussian symmetric kernels, local constant smoothing using Gamma asymmetric kernels possesses some extra advantages such as boundary bias correction, variance reduction and resistance to sparse design points, which is validated through theoretical details and finite sample simulation study. Under the regular conditions, the weak consistency and the asymptotic normality of these estimators are presented. Finally, the statistical advantages of the nonparametric estimators are depicted through 5-minute high-frequency data from Shenzhen Stock Exchange in China.

scholarly journals Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data

Econometrics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 40
Author(s):  
Erhard Reschenhofer ◽  
Manveer K. Mangat
Keyword(s):  
Simulation Study ◽  
High Frequency ◽  
Mean Squared Error ◽  
Estimation Methods ◽  
High Frequency Data ◽  
Frequency Data ◽  
Periodic Patterns ◽  
Typical Sample ◽  
Squared Error ◽  
Proper Interpretation

For typical sample sizes occurring in economic and financial applications, the squared bias of estimators for the memory parameter is small relative to the variance. Smoothing is therefore a suitable way to improve the performance in terms of the mean squared error. However, in an analysis of financial high-frequency data, where the estimates are obtained separately for each day and then combined by averaging, the variance decreases with the sample size but the bias remains fixed. This paper proposes a method of smoothing that does not entail an increase in the bias. This method is based on the simultaneous examination of different partitions of the data. An extensive simulation study is carried out to compare it with conventional estimation methods. In this study, the new method outperforms its unsmoothed competitors with respect to the variance and its smoothed competitors with respect to the bias. Using the results of the simulation study for the proper interpretation of the empirical results obtained from a financial high-frequency dataset, we conclude that significant long-range dependencies are present only in the intraday volatility but not in the intraday returns. Finally, the robustness of these findings against daily and weekly periodic patterns is established.

FAST CONVERGENCE RATES IN ESTIMATING LARGE VOLATILITY MATRICES USING HIGH-FREQUENCY FINANCIAL DATA

2013 ◽  
Vol 29 (4) ◽  
pp. 838-856 ◽  
Author(s):  
Minjing Tao ◽  
Yazhen Wang ◽  
Xiaohong Chen
Keyword(s):  
Sample Size ◽  
High Frequency ◽  
Convergence Rates ◽  
Fast Convergence ◽  
High Frequency Data ◽  
Frequency Data ◽  
Microstructure Noise ◽  
Finite Sample ◽  
Integrated Volatility ◽  
The Matrix

Financial practices often need to estimate an integrated volatility matrix of a large number of assets using noisy high-frequency data. Many existing estimators of a volatility matrix of small dimensions become inconsistent when the size of the matrix is close to or larger than the sample size. This paper introduces a new type of large volatility matrix estimator based on nonsynchronized high-frequency data, allowing for the presence of microstructure noise. When both the number of assets and the sample size go to infinity, we show that our new estimator is consistent and achieves a fast convergence rate, where the rate is optimal with respect to the sample size. A simulation study is conducted to check the finite sample performance of the proposed estimator.

scholarly journals Daily LGARCH model estimation using high frequency data

2021 ◽  
Vol 1 (2) ◽  
pp. 165-179
Author(s):  
Xiaoling Chen ◽  
◽  
Xingfa Zhang ◽  
Yuan Li ◽  
Qiang Xiong
Keyword(s):  
High Frequency ◽  
Stock Exchange ◽  
Likelihood Estimation ◽  
High Frequency Data ◽  
Simulation Studies ◽  
Frequency Data ◽  
Asymptotic Results ◽  
Estimation Precision ◽  
Quasi Maximum Likelihood ◽  
Autoregressive Conditional Heteroscedasticity

<abstract> <p>In this paper, we introduce the intraday high frequency data to estimate the daily linear generalized autoregressive conditional heteroscedasticity (LGARCH) model. Based on the volatility proxies constructed from the intraday high frequency data, the quasi maximum likelihood estimation (QMLE) of the daily LGARCH model and its asymptotic distribution are studied under some regular assumptions. One criterion is also given to choose the optimal volatility proxy according to the asymptotic results. Simulation studies show that the QMLE of the parameters performs well. It is also found that introducing the intraday high frequency data can significantly improve the estimation precision. The proposed method is applied to analyze the SSE 50 Index, which consists of the 50 largest and most liquid A-share stocks listed on Shanghai Stock Exchange. Empirical results show the method is of potential application value.</p> </abstract>

scholarly journals Liquidity of Tunisian Stock Market: A Panel Var Estimation

2021 ◽  
Vol 10 (3) ◽  
pp. 93
Author(s):  
Wafa Chabeb ◽  
Adel Boubaker
Keyword(s):  
Response Function ◽  
High Frequency ◽  
Stock Exchange ◽  
Impulse Response Function ◽  
High Frequency Data ◽  
Var Model ◽  
Panel Var ◽  
Frequency Data ◽  
Dynamic Interactions ◽  
Dynamic Relationships

The purpose of this paper is to estimate the functions impulsions-response of liquidity on the Tunisian Stock Exchange (TSE). We will use the methodology proposed by Abrigo and Love (2016). Our study is done on an order-driven market. The data is composed of high frequency data of orders listed on the TSE for the period April 2014 to June 2014. Inspired of the study of Jarnecic and Snape (2014), we apply a panel VAR model to stocks traded in continuous in order to examine the dynamic interactions between spread, volatility, size and frequency of transactions. Then we study the liquidity of the TSE through the impulse response function of the Panel VAR model. Our findings show dynamic relationships between spread, volatility, size and frequency of trading. Some differences exist in the dynamics of liquidity when we take into account the trading intensity of the stock. Furthermore, we note that shocks are absorbed after three gaps of 45minutes.

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