scholarly journals Modeling and estimation on long memory stochastic volatility for index prices of FTSE Bursa Malaysia KLCI

2017 ◽  
Vol 13 (4-1) ◽  
pp. 315-324
Author(s):  
Kho Chia Chen ◽  
Arifah Bahar ◽  
Chee-Ming Ting ◽  
Haliza Abd Rahman
Keyword(s):  
Stochastic Volatility ◽  
Long Memory ◽  
Financial Time Series ◽  
Least Square ◽  
Estimation Methods ◽  
Fluctuation Analysis ◽  
Least Square Estimator ◽  
Persistence Properties ◽  
Persistence Property ◽  
Mean Square Errors

Long memory and volatility have been used to measure risks associated with persistence in financial data sets. However, the persistence in volatility cannot be easily captured because some mathematical models are not able to detect these properties. To overcome this shortfall, this study develops a procedure to construct long-memory stochastic volatility (LMSV) model by using fractional Ornstein-Uhlenbeck (fOU) process in financial time series to evaluate the degree of persistence property of the data. Procedures for constructing the LMSV model and the estimation methods were applied to the real daily index prices of FTSE Bursa Malaysia KLCI over a period of 20 years. The least square estimator (LSE) and quadratic generalised variations (QGV) methods were used to estimate the drift and diffusion coefficient of the volatility process respectively. The long memory parameter was estimated by the detrended fluctuation analysis (DFA) method. The findings show that the volatility of the index prices exhibited a long memory process but the returns of the index prices did not show strong persistence properties. The root mean square errors (RMSE) obtained from various methods indicated that the performances of the model and estimators in describing returns of the index prices were good.

SIGNAL EXTRACTION IN LONG MEMORY STOCHASTIC VOLATILITY

2014 ◽  
Vol 31 (6) ◽  
pp. 1382-1402 ◽  
Author(s):  
Josu Arteche
Keyword(s):  
Stochastic Volatility ◽  
Long Memory ◽  
Financial Time Series ◽  
Signal Extraction ◽  
Typical Characteristic ◽  
Nonstationary Signals ◽  
Financial Time ◽  
Daily Series ◽  
Parametric Specification ◽  
A New Technique

Long memory in stochastic volatility (LMSV) models are flexible tools for the modeling of persistent dynamic volatility, which is a typical characteristic of financial time series. However, their empirical applicability is limited because of the complications inherent in the estimation of the model and in the extraction of the volatility component. This paper proposes a new technique for volatility extraction, based on a semiparametric version of the optimal Wiener–Kolmogorov filter in the frequency domain. Its main characteristics are its simplicity and generality, because no parametric specification is needed for the volatility component and it remains valid for both stationary and nonstationary signals. The applicability of the proposal is shown in a Monte Carlo and in a daily series of returns from the Dow Jones Industrial index.

scholarly journals Generalized Mixtures of Exponential Distribution and Associated Inference

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1371
Author(s):  
Yaoting Yang ◽  
Weizhong Tian ◽  
Tingting Tong
Keyword(s):  
Exponential Distribution ◽  
Hazard Function ◽  
Least Square ◽  
Estimation Methods ◽  
Mean Deviation ◽  
Simulation Studies ◽  
Least Square Estimator ◽  
Real World Applications ◽  
New Distribution ◽  
Better Than

A new generalization of the exponential distribution, namely the generalized mixture of exponential distribution, is introduced. Some of its basic properties, such as hazard function, moments, order statistics, mean deviation, measures of uncertainly, and reliability probability, are studied. Three different estimation methods are investigated by the maximum likelihood estimator, least-square estimator, and weighted least-square estimator. The performances of the estimators are assessed by simulation studies. Real-world applications of the proposed distribution are explored, and data fitting results show that the new distribution performs better than its competitors.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

Fuzzy C-Regression Models

2013 ◽  
Vol 278-280 ◽  
pp. 1323-1326
Author(s):  
Yan Hua Yu ◽  
Li Xia Song ◽  
Kun Lun Zhang
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Linear Models ◽  
Least Squares Method ◽  
Least Square Method ◽  
Least Square ◽  
Estimation Methods ◽  
Statistical Regression ◽  
Fuzzy Linear Regression ◽  
Fuzzy Degree

Fuzzy linear regression has been extensively studied since its inception symbolized by the work of Tanaka et al. in 1982. As one of the main estimation methods, fuzzy least squares approach is appealing because it corresponds, to some extent, to the well known statistical regression analysis. In this article, a restricted least squares method is proposed to fit fuzzy linear models with crisp inputs and symmetric fuzzy output. The paper puts forward a kind of fuzzy linear regression model based on structured element, This model has precise input data and fuzzy output data, Gives the regression coefficient and the fuzzy degree function determination method by using the least square method, studies the imitation degree question between the observed value and the forecast value.

scholarly journals High Precision Position Measurement Method for Laguerre-Gaussian Beams Using a Quadrant Detector

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4007 ◽  
Author(s):  
Qian Li ◽  
Jiabin Wu ◽  
Yunshan Chen ◽  
Jingyuan Wang ◽  
Shijie Gao ◽  
...  
Keyword(s):  
Maximum Error ◽  
Least Square ◽  
New Method ◽  
Beam Radius ◽  
Engineering Practice ◽  
Gaussian Beams ◽  
Good Prospect ◽  
Position Measurement ◽  
Beam Position ◽  
Mean Square Errors

In this paper, we propose a new method to improve the position measurement accuracy for Laguerre-Gaussian beams on a quadrant detector (QD). First, the error effects of the detector diameter and the gap size are taken into account, and the position error compensation factor is introduced into the conventional formula. Then, in order to reduce the number of parameters, the concept of effective radius is proposed. Thus, a new analytical expression is obtained with a best fit using the least square method. It is verified by simulation that this approach can reduce the maximum error by 97.4% when the beam radius is 0.95 mm; meanwhile, the root mean square errors under different radii are all less than 0.004 mm. The results of simulation show that the new method could effectively improve the accuracy of the QD measurement for different radii. Therefore, the new method would have a good prospect in the engineering practice of beam position measurements.

Sign in / Sign up

Export Citation Format

Share Document