Risk Management eJournal最新文献

{"title":"Delta Hedging and Volatility-Price Elasticity: A Two-Step Approach","authors":"Kun Xia, Xuewei Yang, Peng Cheng Zhu","doi":"10.2139/ssrn.3707369","DOIUrl":"https://doi.org/10.2139/ssrn.3707369","url":null,"abstract":"Traditional Black-Scholes delta do not minimize variance of hedging risk since there exists a long run negative relationship between implied volatility and underlying price. This paper presents a two-step empirical approach of option delta hedging in which the hedging ratio is determined by volatility-price relationship. Specifically, we find that the dependency of minimum variance (MV) hedging ratio on volatility-price elasticity is quite stable and that the volatility-price elasticity exhibits characteristic of mean-reverting. Therefore we first estimate a model which can capture the dependency of hedging ratio on volatility-price elasticity, and then substitute predictions of future volatility-price elasticity into the pre-fixed model to obtain the MV hedging ratio. We test the new approach using the S&P 500 daily option data and show that our approach results in higher hedging gain than related methods appeared in recent works.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126017708","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}

{"title":"Granular Credit Risk","authors":"S. Galaasen, Rustam Jamilov, Ragnar E. Juelsrud, Hélène Rey","doi":"10.3386/w27994","DOIUrl":"https://doi.org/10.3386/w27994","url":null,"abstract":"What is the impact of granular credit risk on banks and on the economy? We provide the first causal identification of single-name counterparty exposure risk in bank portfolios by applying a new empirical approach on an administrative matched bank-firm dataset from Norway. Exploiting the fat tail properties of the loan share distribution we use a Gabaix and Koijen (2020a,b) granular instrumental variable strategy to show that idiosyncratic borrower risk survives aggregation in banks portfolios. We also find that this granular credit risk spills over from affected banks to firms, decreases investment, and increases the probability of default of non-granular borrowers, thereby sizably affecting the macroeconomy.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125926441","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}

{"title":"Новые способы измерения катастрофических рисков: меры \"VaR в степени t\" и их вычисление (New Ways to Measure Catastrophic Risk: 'VAR to Degree T' Measures and Their Calculation)","authors":"V. Minasyan","doi":"10.2139/ssrn.3700817","DOIUrl":"https://doi.org/10.2139/ssrn.3700817","url":null,"abstract":"<b>Russian Abstract:</b> Автор предлагает ввести семейство новых мер риска — «VaR в степени t». Цель работы — исследовать свойства данного семейства мер и вывести формулы для их вычисления. В исследовании использованы методы оценки финансовых рисков в виде мер риска VaR и ES. В результате предложен новый инструмент оценки катастрофических.<br><br>финансовых рисков: «VaR в степени t». Доказано, что для вычисления ( )t VaR достаточно рассчитать обычную меру риска VaR с определенным образом измененной доверительной вероятностью. Автор делает вывод, что данное семейство мер может быть полезно в практике риск-менеджмента компаний при решении задачи проникновения в риски событий с малыми вероятностями, но с катастрофическими финансовыми потерями. Результаты данного исследования также могут применяться регулятором для оценки достаточности капитала финансовых институтов.<br><br> При t &gt; 1 эти меры риска катастрофических потерь оказываются более консервативными, чем известные меры риска VaR, ES и GlueVaR.<br><br><b>English Abstract:</b> The author concludes that this family of measures can be useful in the practice of risk management of companies when solving the problem of penetration into risks of events with small probabilities, but with catastrophic financial losses. The results of this study can also be used by the regulator to assess the capital adequacy of financial institutions.<br><br>The author proposes a new family of new risk measures, \"VaR to degree t.\" The purpose of the work is to study the properties of this measure family and to derive formulas for their calculation. The study uses existing methods of assessing financial risks in the form of risk measures VaR and proposes a new tool for assessing them. As a result, it has been proven that it is sufficient to calculate a usual VaR risk measure, with a certain manner of changed confidence probability, for the calculation. The author concludes that this family of measures can be useful in the practice of risk management of companies when solving the problem of penetration into risks of events with small probabilities, but with catastrophic financial losses. The results of this study can also be used by the regulator to assess the capital adequacy of financial institutions.<br><br>If t &gt; 1, these measures prove to be more conservative risk measures for catastrophic losses than known risk measures for VaR, ES and GlueVaR.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134338769","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}

{"title":"Unmasking Mutual Fund Derivative Use","authors":"Ron Kaniel, Pingle Wang","doi":"10.2139/ssrn.3692838","DOIUrl":"https://doi.org/10.2139/ssrn.3692838","url":null,"abstract":"Utilizing new SEC data enabling us to compute mutual funds' derivative positions performance, this paper studies how derivatives impact fund performance. In contrast to prior research concluding derivatives are used for hedging, we find that most active equity funds use them to amplify market exposure in conjunction with reducing fund equity exposure to market risk. Despite seemingly small weights, derivatives have a significant impact on funds' leverage and contribute largely to fund returns and cross-sectional differences in returns. Funds extensively using derivatives underperform, yet receive more flows. In response to the COVID-19 pandemic outbreak, funds regularly utilizing derivatives trade more heavily on short derivative positions. The pattern is more prevalent among managers for which the risk of recession is likely more salient. There is no change in extensive margin of derivative use. Hedging funds, which are the minority, outperform significantly during the outbreak. Amplifying funds suffer a double whammy. While they do shift strategies, they are slow to react and experience similarly large losses to nonusers in the outbreak phase. By the time they shift, the market has already started to rebound, and they lose on their short positions. Funds are slow to unwind shorts during the recovery. Shifts in derivative return distributions during the COVID-19 crisis are mostly driven by swaps, which have been ignored by previous studies.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129291302","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}

{"title":"Stochastic Comparisons of Largest-Order Statistics for Proportional Reversed Hazard Rate Model and Applications","authors":"Lu Li, Qinyu Wu, Tiantian Mao","doi":"10.1017/jpr.2020.40","DOIUrl":"https://doi.org/10.1017/jpr.2020.40","url":null,"abstract":"We investigate stochastic comparisons of parallel systems (corresponding to the largest-order statistics) with respect to the reversed hazard rate and likelihood ratio orders for the proportional reversed hazard rate (PRHR) model. As applications of the main results, we obtain the equivalent characterizations of stochastic comparisons with respect to the reversed hazard rate and likelihood rate orders for exponentiated generalized gamma and exponentiated Pareto distributions. Our results recover and strengthen some recent results in the literature.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130602100","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}

{"title":"Arc-Sine Law and the Libor Reform","authors":"Vladimir V. Piterbarg","doi":"10.2139/ssrn.3684535","DOIUrl":"https://doi.org/10.2139/ssrn.3684535","url":null,"abstract":"The fallback \"Libor adjustment spread\" spread to be used for calculating Libor replacement rates in the future is \u0000defined as the median (50%-th percentile) of five years of historical \u0000observations of the spread between Libor and compounded OIS rates, \u0000calculated on the future date of Libor cessation announcement. Some of the \u0000observations entering this calculation have already occurred and some are \u0000still in the future. In this note we assert that the future realized median \u0000is a non-linear function of future, yet unknown, spread observations and \u0000therefore its fair value calculation must account for spread dynamics and \u0000not just forward values. We propose a model of the future evolution of \u0000spreads and derive a very numerically-efficient algorithm for calculating \u0000the fair value of the median that incorporates both the historical \u0000observations and the future dynamics of the spread. We establish that, given \u0000our model, the market expectations of the fallback spreads are at, or \u0000somewhat above, the upper range of theoretically-justifiable values. The \u0000approximation method we develop is based in part on the Arc-Sine Law and \u0000should be of independent interest to math finance professionals.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131905684","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}

{"title":"A Model-Based Approach to Determine the Number of Scenarios and Scenario Probabilities for Loan Loss Provision Calculations Under the Accounting Standards of IFRS 9 and US-GAAP CECL","authors":"Oliver Blümke","doi":"10.2139/ssrn.3679940","DOIUrl":"https://doi.org/10.2139/ssrn.3679940","url":null,"abstract":"The accounting standards of the International Financial Reporting Standards (IFRS) and the United States Generally Accepted Accounting Principles (US-GAAP) require from financial institutions to consider multiple macroeconomic scenarios when calculating loan loss provisions. At present, however, it is unclear how to determine the number of scenarios and scenario probabilities without resorting to - often subjective - expert judgement. The paper discusses a model-based approach and proposes to use hidden Markov models to determine the number of relevant scenarios and scenario probabilities. The tool of the hidden Markov model allows to use established model selection criteria, such as the Akaike information criterion, to decide on the number of scenarios. Hidden Markov models also provide estimates of the transition matrix of the hidden states, which constitute the required conditional scenario probabilities. The tool of the hidden Markov model is discussed by using a time series of defaults from Standard &amp; Poor's.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133196000","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}

{"title":"Benchmarking LGD Discount Rates","authors":"Harald Scheule, Stephan Jortzik","doi":"10.2139/ssrn.3673120","DOIUrl":"https://doi.org/10.2139/ssrn.3673120","url":null,"abstract":"This paper provides a theoretical and empirical analysis of alternative discount rate concepts for computing LGDs using historical bank workout data. It benchmarks five discount rate concepts for workout recovery cash flows to derive observed Loss rates Given Default (LGDs) in terms of economic robustness and empirical implications: contract rate at origination, loan weighted average cost of capital, return on equity, market return on defaulted debt, and market equilibrium return. The paper develops guiding principles for LGD discount rates and argues that the Weighted Average Cost of Capital (WACC) and market equilibrium return dominate the popular contract rate method. The empirical analysis of data provided by Global Credit Data (GCD) shows that declining risk-free rates are in part offset by increasing market risk premiums. Common empirical discount rates are between the risk-free rate and the return on equity. The variation of empirical LGDs is moderate for the various discount rate approaches. Furthermore, a simple correction technique for resolution bias is developed and increases observed LGDs for all periods, particularly recent periods.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131356914","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}

{"title":"Risk Management Model Development by Integrating House of Risk Model and ANP Model","authors":"C. Natalia, C. Oktavia, T. P. Hidayat, W. Makatita","doi":"10.20474/jabs-6.4.4","DOIUrl":"https://doi.org/10.20474/jabs-6.4.4","url":null,"abstract":"Nowadays business communities are vulnerable to risk, because risk is not only inherent in every activity or business process of the company but also arise in various forms and types. Risks that occur in each company might be different and can present in various forms and cause negative impacts. Thus it is essential for companies to have a proper SC risk management in order to survive in a risky business environment. In this study, integration of the supply chain risk management model will be carried out by using HOR and ANP model. HOR is an innovative model for proactive SC risk management which combines the basic ideas of two well-known tools: the house of quality of the quality function deployment and the failure mode and effect analysis. HOR phase 1 is used to determine which risk agent will be prioritized in advance for precautionary measures and ANP to obtain the best alternative action. There are several steps of the integration method. First of all is mapping business processes. Second, identifying risk events and risk causes. Third, conducting risk assessments and determining the Aggregate Risk Potential (ARP), which risk agents are need to be prioritized based on the ARP value from HOR 1. Fourth, identifying which actions are appropriate to minimize risk agents by ANP modeling and each of these actions will be given weight to obtain the best alternative action. ANP is a tool to determine correlation between risk mitigation and hence rank those mitigation based on the priorities. The second phase of HOR is used to determine correlation between risk mitigation and the event risk and also its effectiveness based on its degree of difficulty to be applied. The output of this phase is a list of priorities on the mitigation risk which is relatively possible and feasible to be applied and capable to mitigate the risks. This study is conducted on 3 reputable manufacturing industries to be compared and analysed. HOR2 is intended to prioritize the proactive actions that the company should pursue to maximize the cost-effectiveness of the effort in dealing with the selected risk agents in HOR 1. This research shows that risks apply differently on each company. Hence, the ANP ranking is different. Mitigation risk’s rank is determined from the second phase of HOR as it has considered correlation aspect of risk agents and its degree of difficulty to be mitigate. Finally, special actions are applied to various risks such as performance appraisal to the staffs. Further study is needed such as by ISM method in order to make mitigation actions are focused on the companies.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124477674","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}

{"title":"A Machine Learning Based Regulatory Risk Index for Cryptocurrencies","authors":"Xinwen Ni, W. Härdle, Taojun Xie","doi":"10.2139/ssrn.3699345","DOIUrl":"https://doi.org/10.2139/ssrn.3699345","url":null,"abstract":"Cryptocurrencies' values often respond aggressively to major policy changes, but none of the existing indices informs on the market risks associated with regulatory changes. In this paper, we quantify the risks originating from new regulations on FinTech and cryptocurrencies (CCs), and analyse their impact on market dynamics. Specifically, a Cryptocurrency Regulatory Risk IndeX (CRRIX) is constructed based on policy-related news coverage frequency. The unlabeled news data are collected from the top online CC news platforms and further classified using a Latent Dirichlet Allocation model and Hellinger distance. Our results show that the machine-learning-based CRRIX successfully captures major policy-changing moments. The movements for both the VCRIX, a market volatility index, and the CRRIX are synchronous, meaning that the CRRIX could be helpful for all participants in the cryptocurrency market. The algorithms and Python code are available for research purposes on www.quantlet.de.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":"18 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129960495","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}