High-frequency analysis, Model Validation and Intraday risk metrics

2019 ◽  
Author(s):  
Ravi Summinga-Sonagadu
Keyword(s):  
Model Validation ◽  
Internal Control ◽  
High Frequency ◽  
Insurance Industry ◽  
Garch Model ◽  
Capital Requirements ◽  
Garch Models ◽  
Analysis Model ◽  
Error Distributions ◽  
Distributional Assumptions

https://www.mdpi.com/2227-9091/7/1/10 Background Despite the growing amount of research in the field of high frequency financial data analysis, few studies have focused on model validation and high-frequency risk measures. This study contributes to the literature in the following ways: A rigorous model validation, both in terms of in-sample fit and out-sample performance for the MC-GARCH model under five error distributions is provided. Statistical and graphical tests are conducted to validate the models. One component of the MC-GARCH model is the daily variance forecast. For this purpose, the GARCH(1,1) and EGARCH(1,1) under the five error distributions are compared and the best model among the 10 GARCH models is used to forecast the daily variance. The modelling and forecasting performance of the MC-GARCH model under different distributional assumptions is assessed in this study. The 99% intraday VaR is forecasted and three backtesting procedures are used. This is the first study to assess the VaR predictive ability of the MC-GARCH models by using an asymmetric VaR loss function. This is the first study to forecast the intraday expected shortfall under different distributional assumptions for the MC-GARCH model. Again, three backtests are used including the recently proposed ES regression backtest. Due to the high importance of risk management, the results of this study may contribute in many fields. This study is highly relevant to the banking industry since banks are required to calculate risk metrics on a daily basis for internal control purposes and for determining their capital requirements. Risk measurement is also essential to the insurance industry from the pricing of insurance contracts to determining the Solvency Capital Requirement (SCR) and therefore the results of this study might be useful. Any other organisation having an exposure to some kind of financial risk might benefit from this study. 

scholarly journals Risk Model Validation: An Intraday VaR and ES Approach Using the Multiplicative Component GARCH

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 10
Author(s):  
Ravi Summinga-Sonagadu ◽  
Jason Narsoo
Keyword(s):  
Loss Function ◽  
Value At Risk ◽  
Garch Model ◽  
Innovation Process ◽  
Expected Shortfall ◽  
Error Distribution ◽  
Garch Models ◽  
Intraday Data ◽  
Distributional Assumptions ◽  
T Distribution

In this paper, we employ 99% intraday value-at-risk (VaR) and intraday expected shortfall (ES) as risk metrics to assess the competency of the Multiplicative Component Generalised Autoregressive Heteroskedasticity (MC-GARCH) models based on the 1-min EUR/USD exchange rate returns. Five distributional assumptions for the innovation process are used to analyse their effects on the modelling and forecasting performance. The high-frequency volatility models were validated in terms of in-sample fit based on various statistical and graphical tests. A more rigorous validation procedure involves testing the predictive power of the models. Therefore, three backtesting procedures were used for the VaR, namely, the Kupiec’s test, a duration-based backtest, and an asymmetric VaR loss function. Similarly, three backtests were employed for the ES: a regression-based backtesting procedure, the Exceedance Residual backtest and the V-Tests. The validation results show that non-normal distributions are best suited for both model fitting and forecasting. The MC-GARCH(1,1) model under the Generalised Error Distribution (GED) innovation assumption gave the best fit to the intraday data and gave the best results for the ES forecasts. However, the asymmetric Skewed Student’s-t distribution for the innovation process provided the best results for the VaR forecasts. This paper presents the results of the first empirical study (to the best of the authors’ knowledge) in: (1) forecasting the intraday Expected Shortfall (ES) under different distributional assumptions for the MC-GARCH model; (2) assessing the MC-GARCH model under the Generalised Error Distribution (GED) innovation; (3) evaluating and ranking the VaR predictability of the MC-GARCH models using an asymmetric loss function.

FACTORS INFLUENCING CAPITAL ADEQUACY IN BUSINESS ORGANIZATIONS: A CASE OF KENYA’S INSURANCE INDUSTRY

2017 ◽  
Vol 2 (4) ◽  
pp. 1
Author(s):  
Lewis Wanja Jane ◽  
Dr. Aloys B. Ayako ◽  
Mr. William Kinai
Keyword(s):  
Regression Analysis ◽  
Insurance Industry ◽  
Calculated Data ◽  
Capital Requirements ◽  
Capital Adequacy ◽  
Insurance Companies ◽  
Unique Contribution ◽  
Analysis Model ◽  
Factors Influencing ◽  
Beta Coefficient

AbstractPurpose: The purpose of this study was to determine the factors influencing capital adequacy in business organizations.Methodology: A case study design was. The study consisted of 46 insurance companies. The data is quantitative and secondary data collection method was used. The study used descriptive and regression approach in data analysis. Using Statistical Package for Social Sciences (SPSS) regression analysis model was calculated. Data was presented in tables and graphs.Results: Regression analysis showed that ownership listing status had a beta coefficient of (-16.614) and that it was statistically insignificant (0.329). Regression analysis showed that dividend payout ratio had a beta coefficient of -0.455 and that it was statistically significant (0.000). Regression analysis showed that the profitability ratio had a beta coefficient of 0.485 and that it was statistically significant (0.000). Regression analysis showed that the liquidity ratio had a beta coefficient of 0.226 and that it was statistically significant (0.000). Regression analysis showed that the cost of capital had a beta coefficient of -0.566 and that it was statistically insignificant (0.125).Unique contribution to theory, practice and policy: The study recommended that insurance Regulatory Authority should put in measures to make the insurance companies adhere to the recent regulations and policies which require the companies to have minimum capital requirements and hence in turn increasing capital adequacy.

The dependence structure in volatility between Shanghai and Shenzhen stock market in China

2016 ◽  
Vol 6 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Mingyuan Guo ◽  
Xu Wang
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Estimation Method ◽  
Garch Model ◽  
Price Volatility ◽  
Market Volatility ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Content Type

Purpose – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings – This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view. Originality/value – Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

The Multi-Component Model for the semantic analysis of slurs

2020 ◽  
Vol 11 (2) ◽  
pp. 219-240
Author(s):  
Björn Technau
Keyword(s):  
Ethnic Group ◽  
High Frequency ◽  
Philosophy Of Language ◽  
Semantic Analysis ◽  
Component Model ◽  
Analysis Model ◽  
Multiple Component ◽  
Analysis Models

Abstract The semantics of slur terms has provoked some debate within the philosophy of language, and different analysis models have been proposed to account for the complex meaning of these terms. The present paper acknowledges the complexity of the matter and presents an analysis model that is inspired by multiple-component approaches to slurs, such as those by Camp (2018) and Jeshion (2018). The Multi-Component Model for the semantic analysis of slurs (MCM) tracks down altogether four meaning components in group-based slur terms: a referential and a pejorative meaning component (being xy and despicable because of it), as well as a scalar component capturing the term’s individual degree of offensiveness, and an expressive component indexing heightened emotions in all contexts of use. The notion of individual offensiveness degrees (that are fed by a multitude of semantic, pragmatic, and/or extralinguistic sources) allows us to account for the differences between slurs for the same ethnic group (such as nigger, negro, coon, darkie for Blacks); and the separation of the expressive component from the pejorative component can (1) explain the high frequency of non-pejorative uses, and (2) correctly describe these uses as expressive.

scholarly journals Regulatory Forbearance in the U.S. Insurance Industry: The Effects of Removing Capital Requirements for an Asset Class

2021 ◽  
Author(s):  
Bo Becker ◽  
Marcus M Opp ◽  
Farzad Saidi
Keyword(s):  
Insurance Industry ◽  
Capital Requirements ◽  
Insurance Companies ◽  
High Yield ◽  
Capital Regulation ◽  
Fixed Income ◽  
Portfolio Holdings ◽  
Asset Class ◽  
Regulatory Forbearance ◽  
The U.S

Abstract We analyze the effects of a reform of capital regulation for U.S. insurance companies in 2009. The reform eliminates capital buffers against unexpected losses associated with portfolio holdings of MBS, but not for other fixed-income assets. After the reform, insurance companies are much more likely to retain downgraded MBS compared to other downgraded assets. This pattern is more pronounced for financially constrained insurers. Exploiting discontinuities in the reform’s implementation, we can identify the relevance of the capital requirements channel. We also document that the insurance industry crowds outs other investors in the new issuance of (high-yield) MBS.

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