We define and study in this work a simple model allowing for a prudential valuation of solvency capital requirement while avoiding over-assessment specifically after market disruption. The main idea is to include a dampener component in charge of refining risk assessment after a market failure. Rather than aiming at a realistic, and thus complex, description of equity prices movements, our model concentrates on minimal features enabling accurate computation of regulatory capital requirements. The model is defined both in a discrete and continuous fashion. In the latter case, we prove the existence, uniqueness and stability of the solution of the stochastic functional differential equation that specifies the model. One difficulty is that the proposed underlying stochastic process has neither stationary nor independent increments. We are however able to perform statistical analyses in view of its validation. Numerical experiments show that our model outperforms more elaborate ones of common use as far as medium term (between 6 months and 5 years) risk assessment is concerned. We believe that our approach offers an attractive alternative for insurance and reinsurance companies to assess their 1 year equity-risk solvency capital requirement with an internal model and their ORSA capital.
{"title":"A Conditional Equity Risk Model for Regulatory Assessment","authors":"A. Floryszczak, J. Lévy véhel, M. Majri","doi":"10.2139/ssrn.2728953","DOIUrl":"https://doi.org/10.2139/ssrn.2728953","url":null,"abstract":"We define and study in this work a simple model allowing for a prudential valuation of solvency capital requirement while avoiding over-assessment specifically after market disruption. The main idea is to include a dampener component in charge of refining risk assessment after a market failure. Rather than aiming at a realistic, and thus complex, description of equity prices movements, our model concentrates on minimal features enabling accurate computation of regulatory capital requirements. The model is defined both in a discrete and continuous fashion. In the latter case, we prove the existence, uniqueness and stability of the solution of the stochastic functional differential equation that specifies the model. One difficulty is that the proposed underlying stochastic process has neither stationary nor independent increments. We are however able to perform statistical analyses in view of its validation. Numerical experiments show that our model outperforms more elaborate ones of common use as far as medium term (between 6 months and 5 years) risk assessment is concerned. We believe that our approach offers an attractive alternative for insurance and reinsurance companies to assess their 1 year equity-risk solvency capital requirement with an internal model and their ORSA capital.","PeriodicalId":351643,"journal":{"name":"ERN: Other Monetary Economics: International Financial Flows","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134351309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The global financial crisis of 2008–2009 illustrates how financial turmoil in advanced economies could trigger severe financial stress in emerging markets. Previous studies dealing with financial crises and contagion show the linkages through which financial stress are transmitted from advanced to emerging markets. This paper extends the existing literature on the use of financial stress index (FSI) in understanding the channels of financial transmission in emerging market economies. Using FSI of 25 emerging markets, our panel regression estimates show that not only advanced economies FSI, but also regional and nonregional emerging market FSIs significantly increase domestic financial stress. Our findings also suggest that there is a common regional factor significantly affecting domestic FSI in emerging Asia and emerging Europe. Furthermore, the results from a structural vector autoregression model with contemporaneous restrictions indicate that although a domestic financial shock still accounts for most of the variation in domestic FSI, regional shocks play an important role in emerging Asia.
{"title":"Determinants of Financial Stress in Emerging Market Economies","authors":"Cyn‐Young Park, Rogelio V. Mercado","doi":"10.2139/ssrn.2242382","DOIUrl":"https://doi.org/10.2139/ssrn.2242382","url":null,"abstract":"The global financial crisis of 2008–2009 illustrates how financial turmoil in advanced economies could trigger severe financial stress in emerging markets. Previous studies dealing with financial crises and contagion show the linkages through which financial stress are transmitted from advanced to emerging markets. This paper extends the existing literature on the use of financial stress index (FSI) in understanding the channels of financial transmission in emerging market economies. Using FSI of 25 emerging markets, our panel regression estimates show that not only advanced economies FSI, but also regional and nonregional emerging market FSIs significantly increase domestic financial stress. Our findings also suggest that there is a common regional factor significantly affecting domestic FSI in emerging Asia and emerging Europe. Furthermore, the results from a structural vector autoregression model with contemporaneous restrictions indicate that although a domestic financial shock still accounts for most of the variation in domestic FSI, regional shocks play an important role in emerging Asia.","PeriodicalId":351643,"journal":{"name":"ERN: Other Monetary Economics: International Financial Flows","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123185293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research project aims to analyse the role of banks governance as an explanation to the recent financial crises. We claim to contribute to the current debate on the issue of governance mechanisms in explaining the performance of the banking sector, crucial to understanding of the 2007-2008 financial crises, in three important ways. Firstly, providing a unique and innovative analyse of the impact of board social and economic networks on bank performance, including political connections and business networks. Secondly, analysing the impact of deposit insurance on bank performance, considering both country and bank specific characteristics. Thirdly, analysing the banks’ characteristics that received government bailouts, namely in what concerns their corporate governance and try to shed more light on the extent to which banks’ governance characteristics determine the response to the financial crisis. The sample consists of 181 publicly listed banks from EMU countries, between 2002 and 2009 subject to the following criteria (1) being listed at least 1 year before year of analysis and (2) do no changed its governance structure due to mergers or acquisitions (M&A), in order to exclude banks than, eventually, around M&A have influenced stocks prices and/or manipulate results.
{"title":"The Impact of Corporate Governance in the Banking Sector Performance: The Case of the 2007-2008 Financial Crisis","authors":"Catarina Fernandes, J. Farinha, Cesario Mateus","doi":"10.2139/ssrn.1717960","DOIUrl":"https://doi.org/10.2139/ssrn.1717960","url":null,"abstract":"This research project aims to analyse the role of banks governance as an explanation to the recent financial crises. We claim to contribute to the current debate on the issue of governance mechanisms in explaining the performance of the banking sector, crucial to understanding of the 2007-2008 financial crises, in three important ways. Firstly, providing a unique and innovative analyse of the impact of board social and economic networks on bank performance, including political connections and business networks. Secondly, analysing the impact of deposit insurance on bank performance, considering both country and bank specific characteristics. Thirdly, analysing the banks’ characteristics that received government bailouts, namely in what concerns their corporate governance and try to shed more light on the extent to which banks’ governance characteristics determine the response to the financial crisis. The sample consists of 181 publicly listed banks from EMU countries, between 2002 and 2009 subject to the following criteria (1) being listed at least 1 year before year of analysis and (2) do no changed its governance structure due to mergers or acquisitions (M&A), in order to exclude banks than, eventually, around M&A have influenced stocks prices and/or manipulate results.","PeriodicalId":351643,"journal":{"name":"ERN: Other Monetary Economics: International Financial Flows","volume":"223 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131485984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Korean Abstract: 본 논문은 금융산업지수(KOSPI 금융업 지수와 KRX Banks 지수)와 개별 금융기관(6개 시중은행과 4개 지방은행)의 주가 수익률의 조건부 분산을 GARCH-ARJI 모형을 활용하여 분석하고, 수익률의 급격한 변화와 관련 있는 조건부 점프 강도(jump intensity)를 금융산업의 시스템 리스크 지표로 활용하는 방안에 관한 연구를 수행하였다. GARCH-ARJI 모형은 수익률의 조건부 분산을 GARCH에 의해 설명되는 부분과 점프에 의해 설명되는 두 부분으로 나누어 설명하는 모형으로, 점프의 군집화현상을 설명할 수 있는 모형이다. 실증분석 결과, GARCH-ARJI 모형을 활용하여 추출한 금융산업지수의 점프 강도가 2000년 IT시장 버블붕괴로 인한 충격, 2003년 카드채 부실사건, 2008년 리먼 브러더스 파산 등의 위기기간에서 뚜렷이 증가함을 확인하였다. 특히, 금융산업지수와 개별 금융기관의 점프 강도의 상관성이 매우 강하다는 것은 발견하였는데 이러한 결과는 우리나라 금융산업의 시스템 리스크의 노출 정도가 강하다는 것을 의미하고, 아울러 거시 정책적 관점의 시스템 리스크 관리의 필요성을 보여주는 결과로 해석될 수 있다.
English Abstract: In this paper, we employ GARCH-ARJI model and extract time varying conditional jump intensity using this model. We argue that conditional jump intensity is possibly applicable as an indicator for systemic risk of financial industry. We analyzed two stock index (KOSPI financial industry index and KRX Banks index) and 10 individual bank's return series. The conditional jump intensity clearly shows similar time varying pattern and strongly correlated, which means there is a great possibility of simultaneous crisis in financial industry. Additionally, the conditional jump intensity dramatically increased during the period of financial shocks such as IT bubble in 2000, crisis resulted from credit card in 2003 and global financial crisis in 2008.
korean abstract:本文金融产业指数(kospi指数和krx banks金融业指数)和个别金融机构(6家商业银行和4家地方银行)的股价收益率的附条件运用garch - arji模型分析了分散,收益率的急剧变化和相关的有条件的跳跃强度(jump intensity)作为金融产业的系统风险指标的方案完成的研究。GARCH-ARJI模型是将收益率的条件分散分为GARCH说明的部分和跳跃说明的两部分进行说明的模型，是能够说明跳跃的群集化现象的模型。实证分析结果显示，利用GARCH-ARJI模型提取的金融产业指数的反弹强度在2000年IT市场泡沫崩溃带来的冲击、2003年信用卡债务亏损事件、2008年雷曼兄弟破产等危机期间明显增加。特别是,金融产业指数和个别金融机构的跳跃强度的关系很强,是发现了,这样的结果,是我国金融产业的系统风险的暴露程度强,意味着,同时宏观政策观点的系统风险管理的必要性,表明有可能会被理解为结果。English Abstract: In this paper, we employ GARCH-ARJI model and extract time varying conditional jump intensity using this model。We argue that conditional jump intensity is possibly applicable as an indicator for systemic risk of financial industry。韩国综合金融industry index and KRX Banks index, 10 individual bank's return series。The conditional jump intensity clearly shows similar time varying pattern and strongly correlated, which means there is a great possibility of simultaneous crisis in financial industry。Additionally, conditional jump intensity dramatically increased during the period of financial shocks such as IT bubble in 2000;crisis resulted from credit card in 2003 and global financial crisis in 2008。
{"title":"GARCH-ARJI 모형을 활용한 금융산업의 시스템 리스크에 관한 연구 (Empirical Analysis on the Indicator for Systemic Risk in Banking Industry)","authors":"W. Kim, Joohyun Kim, Jiyoon Lee","doi":"10.2139/ssrn.3017123","DOIUrl":"https://doi.org/10.2139/ssrn.3017123","url":null,"abstract":"<b>Korean Abstract:</b> 본 논문은 금융산업지수(KOSPI 금융업 지수와 KRX Banks 지수)와 개별 금융기관(6개 시중은행과 4개 지방은행)의 주가 수익률의 조건부 분산을 GARCH-ARJI 모형을 활용하여 분석하고, 수익률의 급격한 변화와 관련 있는 조건부 점프 강도(jump intensity)를 금융산업의 시스템 리스크 지표로 활용하는 방안에 관한 연구를 수행하였다. GARCH-ARJI 모형은 수익률의 조건부 분산을 GARCH에 의해 설명되는 부분과 점프에 의해 설명되는 두 부분으로 나누어 설명하는 모형으로, 점프의 군집화현상을 설명할 수 있는 모형이다. 실증분석 결과, GARCH-ARJI 모형을 활용하여 추출한 금융산업지수의 점프 강도가 2000년 IT시장 버블붕괴로 인한 충격, 2003년 카드채 부실사건, 2008년 리먼 브러더스 파산 등의 위기기간에서 뚜렷이 증가함을 확인하였다. 특히, 금융산업지수와 개별 금융기관의 점프 강도의 상관성이 매우 강하다는 것은 발견하였는데 이러한 결과는 우리나라 금융산업의 시스템 리스크의 노출 정도가 강하다는 것을 의미하고, 아울러 거시 정책적 관점의 시스템 리스크 관리의 필요성을 보여주는 결과로 해석될 수 있다.<br><br><b>English Abstract:</b> In this paper, we employ GARCH-ARJI model and extract time varying conditional jump intensity using this model. We argue that conditional jump intensity is possibly applicable as an indicator for systemic risk of financial industry. We analyzed two stock index (KOSPI financial industry index and KRX Banks index) and 10 individual bank's return series. The conditional jump intensity clearly shows similar time varying pattern and strongly correlated, which means there is a great possibility of simultaneous crisis in financial industry. Additionally, the conditional jump intensity dramatically increased during the period of financial shocks such as IT bubble in 2000, crisis resulted from credit card in 2003 and global financial crisis in 2008.","PeriodicalId":351643,"journal":{"name":"ERN: Other Monetary Economics: International Financial Flows","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124250393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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