Quasi-Maximum Likelihood Estimation of Volatility with High Frequency Data

2010 ◽  
Author(s):  
Dacheng Xiu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
High Frequency ◽  
Likelihood Estimation ◽  
High Frequency Data ◽  
Frequency Data ◽  
Quasi Maximum Likelihood

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 Identifying Stabilising Effects on Survey Based Life Satisfaction Using Quasi-maximum Likelihood Estimation

2021 ◽  
Author(s):  
Johannes Klement
Keyword(s):  
Life Satisfaction ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Negative Binomial ◽  
Measurement Instrument ◽  
Likelihood Estimation ◽  
Satisfaction Scale ◽  
Quasi Maximum Likelihood ◽  
The Stability

AbstractTo which extent do happiness correlates contribute to the stability of life satisfaction? Which method is appropriate to provide a conclusive answer to this question? Based on life satisfaction data of the German SOEP, we show that by Negative Binomial quasi-maximum likelihood estimation statements can be made as to how far correlates of happiness contribute to the stabilisation of life satisfaction. The results show that happiness correlates which are generally associated with a positive change in life satisfaction, also stabilise life satisfaction and destabilise dissatisfaction with life. In such as they lower the probability of leaving positive states of life satisfaction and increase the probability of leaving dissatisfied states. This in particular applies to regular exercise, volunteering and living in a marriage. We further conclude that both patterns in response behaviour and the quality of the measurement instrument, the life satisfaction scale, have a significant effect on the variation and stability of reported life satisfaction.

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