Default Probability of Listed Companies Based on the Generalized Error Distribution

2010 ◽  
Author(s):  
Yingqiu Li ◽  
Wei He ◽  
Xiaobing Yan
Keyword(s):  
Listed Companies ◽  
Error Distribution ◽  
Default Probability ◽  
Generalized Error Distribution

scholarly journals Modeling and Estimating Volatility of Day-Ahead Electricity Prices

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 750
Author(s):  
Sherzod N. Tashpulatov
Keyword(s):  
Heavy Tails ◽  
Price Level ◽  
Power Market ◽  
Error Distribution ◽  
Electricity Prices ◽  
Volatility Model ◽  
Generalized Error Distribution ◽  
Student’S T ◽  
The Uk ◽  
The Impact

We model day-ahead electricity prices of the UK power market using skew generalized error distribution. This distribution allows us to take into account the features of asymmetry, heavy tails, and a peak higher than in normal or Student’s t distributions. The adequacy of the estimated volatility model is verified using various tests and criteria. A correctly specified volatility model can be used for analyzing the impact of reforms or other events. We find that, after the start of the COVID-19 pandemic, price level and volatility increased.

Is China the price taker in soybean futures?

2015 ◽  
Vol 7 (3) ◽  
pp. 389-404 ◽  
Author(s):  
Bruce Jianhe Liu ◽  
Yubin Wang ◽  
Jingjing Wang ◽  
Xin Wu ◽  
Shu Zhang
Keyword(s):  
Genetically Modified ◽  
Spillover Effects ◽  
Futures Market ◽  
Error Distribution ◽  
Conditional Heteroskedasticity ◽  
Subprime Crisis ◽  
Genetically Modified Soybean ◽  
Content Type ◽  
Generalized Error Distribution ◽  
Futures Price

Purpose – The purpose of this paper is to examine whether China is still a passive price taker from the US soybean futures, or instead domestic futures market has developed certain degrees of pricing power through time. The finding helps to identify the importance of China soybean futures in the perspective of portfolio selection for international futures traders. If China soybean futures market is no longer a price taker after the subprime crisis, traders need to include it as a separate category in their portfolio. Design/methodology/approach – This paper uses exponential generalized autoregressive conditional heteroskedasticity-generalized error distribution (EGARCH-GED) and generalized autoregressive conditional heteroskedasticity-generalized error distribution (GARCH-GED) models to test spillover effects between Dalian Commodity Exchange (DCE) and Chicago Board of Trade (CBOT) soybean futures. The authors divide daily samples into three subperiods based on the subprime crisis. Three research questions – whether China is still the price taker, the importance of Chinese soybean futures in international futures portfolio selection, and the influences of subprime crisis on soybean futures volatility relationship – are examined by comparing estimation results through time and different contracts. Findings – The spillover effect from CBOT soybean futures to DCE No. 1 soybean futures becomes weaker through time. China is no longer a soybean futures price taker after the subprime crisis. The authors also find the shocks of bearish news on DCE soybeans are greater than those of bullish news. Potential volatility of DCE in long positions is bigger than that in short positions. Practical implications – China is the largest soybean importer. DCE is a very important futures market for non-genetically modified soybeans. It is necessary for both international and domestic futures traders to understand the changes in international soybean futures price relationship and take corresponding strategies. It is also important for market to realize that DCE soybean futures are to a less degree price taker after the subprime crisis. Originality/value – The paper applies EGARCH-GED and GARCH-GED models to identify changes in spillover effects before, during, and after the subprime crisis. Different from other studies, this paper finds after the subprime crisis, China is no longer the soybean futures price taker. This paper also compares the spillover effects of non-genetically modified soybean futures (No. 1 soybean futures) with genetically modified soybean futures (No. 2 soybean futures).

scholarly journals Modeling stock market volatility in Croatia

2021 ◽  
Vol 34 (2) ◽  
pp. 431-442
Author(s):  
Hrvoje Jošić ◽  
Berislav Žmuk
Keyword(s):  
Stock Market ◽  
Degrees Of Freedom ◽  
Stock Exchange ◽  
Error Distribution ◽  
Stock Market Volatility ◽  
Stock Market Index ◽  
The Past ◽  
Market Index ◽  
Generalized Error Distribution ◽  
Error Distributions

Purpose: In this paper, the volatility of the Croatian stock market index CROBEX is investigated using the GARCH(1,1) model. Methodology: The novelty provided by this paper is the estimation of the GARCH(1,1) model by using three conditional error distributions (normal (Gaussian) distribution, Student’s-distribution with fixed degrees of freedom and generalized error distribution (GED) with fixed parameters). Results: The findings obtained in the research are in the line with previous research in this field (Erjavec & Cota, 2007; Sajter & Ćorić, 2009). The volatility of CROBEX returns is positively correlated with the volume of trade on the Zagreb Stock Exchange and movements on the main European and American stock markets. The movement of S&P 500 stock market index returns is transmitted from the previous day, providing signals for the direction of change of CROBEX index returns in the present. Conclusion: Therefore, this paper provides evidence that investors in Croatia strongly rely on the past information received from the American S&P500 stock market index. Furthermore, there seems to exist the co-movement between CROBEX and main European indexes on the same trading day.

scholarly journals Analysis on financing structure and default probability of listed companies

2021 ◽  
Vol 292 ◽  
pp. 02032
Author(s):  
Xiangyun Luo ◽  
Miao Luo
Keyword(s):  
Cash Flow ◽  
Default Risk ◽  
Listed Companies ◽  
Mediating Effect ◽  
Default Probability ◽  
Probability Of Default ◽  
Financing Structure ◽  
Debt Default ◽  
Cash Flow Risk ◽  
The Impact

This paper studies the relationship between the financing structure and the probability of default of A-share listed companies from 2001 to 2020. The purpose is to prevent the occurrence of default and ensure the healthy development of various industries. It is found that the higher the proportion of external financing is, the higher the probability of default is. The impact of debt financing on default risk is higher than equity financing. In addition, this paper tests the mediating effect of cash flow risk, and the effect of financing structure on debt default probability is heterogeneous among regions and enterprises. These findings show that enterprises must control their financing structure, optimize the allocation of resources, prevent cash flow risk, reduce the probability of debt default, so as to make various industries flourish and optimize the industrial structure.

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