scholarly journals UTILIZATION OF STUDENT’S T DISTRIBUTION TO HANDLE OUTLIERS IN TECHNICAL EFFICIENCY MEASUREMENT

2021 ◽  
Vol 14 (1) ◽  
pp. 56-67
Author(s):  
Rizky Zulkarnain ◽  
Anik Djuraidah ◽  
I Made Sumertajaya ◽  
Indahwati Indahwati
Keyword(s):  
Normal Distribution ◽  
Technical Efficiency ◽  
Efficiency Measurement ◽  
Ratio Test ◽  
Noise Component ◽  
Frontier Analysis ◽  
Student’S T Distribution ◽  
Student’S T ◽  
Student's T Distribution ◽  
T Distribution

Stochastic frontier analysis (SFA) is the favorite method for measuring technical efficiency. SFA decomposes the error term into noise and inefficiency components. The noise component is generally assumed to have a normal distribution, while the inefficiency component is assumed to have half normal distribution. However, in the presence of outliers, the normality assumption of noise is not sufficient and can produce implausible technical efficiency scores. This paper aims to explore the use of Student’s t distribution for handling outliers in technical efficiency measurement. The model was applied in paddy rice production in East Java. Output variable was the quantity of production, while the input variables were land, seed, fertilizer, labor and capital. To link the output and inputs, Cobb-Douglas or Translog production functions was chosen using likelihood ratio test, where the parameters were estimated using maximum simulated likelihood. Furthermore, the technical efficiency scores were calculated using Jondrow method. The results showed that Student’s t distribution for noise can reduce the outliers in technical efficiency scores. Student’s t distribution revised the extremely high technical efficiency scores downward and the extremely low technical efficiency scores upward. The performance of model was improved after the outliers were handled, indicated by smaller AIC value.

scholarly journals Value at Risk estimation using GAS models with heavy tailed distributions for cryptocurrencies

2021 ◽  
Vol 10 (4) ◽  
pp. 40-54
Author(s):  
Stephanie Danielle Subramoney ◽  
Knowledge Chinhamu ◽  
Retius Chifurira
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Ratio Test ◽  
Student’S T Distribution ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Student’S T ◽  
Student's T Distribution ◽  
T Distribution

  Risk management and prediction of market losses of cryptocurrencies are of notable value to risk managers, portfolio managers, financial market researchers and academics. One of the most common measures of an asset’s risk is Value-at-Risk (VaR). This paper evaluates and compares the performance of generalized autoregressive score (GAS) combined with heavy-tailed distributions, in estimating the VaR of two well-known cryptocurrencies’ returns, namely Bitcoin returns and Ethereum returns. In this paper, we proposed a VaR model for Bitcoin and Ethereum returns, namely the GAS model combined with the generalized lambda distribution (GLD), referred to as the GAS-GLD model. The relative performance of the GAS-GLD models was compared to the models proposed by Troster et al. (2018), in other words, GAS models combined with asymmetric Laplace distribution (ALD), the asymmetric Student’s t-distribution (AST) and the skew Student’s t-distribution (SSTD). The Kupiec likelihood ratio test was used to assess the adequacy of the proposed models. The principal findings suggest that the GAS models with heavy-tailed innovation distributions are, in fact, appropriate for modelling cryptocurrency returns, with the GAS-GLD being the most adequate for the Bitcoin returns at various VaR levels, and both GAS-SSTD, GAS-ALD and GAS-GLD models being the most appropriate for the Ethereum returns at the VaR levels used in this study.    

scholarly journals Application of Generalized Student’s T-Distribution In Modeling The Distribution of Empirical Return Rates on Selected Stock Exchange Indexes

2014 ◽  
Vol 13 (2) ◽  
pp. 37-48
Author(s):  
Jan Purczyńskiz ◽  
Kamila Bednarz-Okrzyńska
Keyword(s):  
Stock Exchange ◽  
Likelihood Method ◽  
Return Rates ◽  
Student’S T Distribution ◽  
Warsaw Stock Exchange ◽  
Distribution Parameters ◽  
Student’S T ◽  
Logarithmic Moments ◽  
Student's T Distribution ◽  
T Distribution

Abstract This paper examines the application of the so called generalized Student’s t-distribution in modeling the distribution of empirical return rates on selected Warsaw stock exchange indexes. It deals with distribution parameters by means of the method of logarithmic moments, the maximum likelihood method and the method of moments. Generalized Student’s t-distribution ensures better fitting to empirical data than the classical Student’s t-distribution.

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