Risk Management eJournal最新文献

{"title":"Cash as a Perpetual Option","authors":"J. Andreasen, Søren Bundgaard Brøgger","doi":"10.2139/ssrn.3669204","DOIUrl":"https://doi.org/10.2139/ssrn.3669204","url":null,"abstract":"We consider the option value of cash when nominal interest rates are no longer constrained by the zero lower bound. We provide a general valuation principle and solve for the value of cash in semi-closed form under Vasicek (1977) dynamics for the nominal short rate. In the absence of a zero lower bound, cash can have substantial option value and becomes a powerful recessionary hedge. However, a significant fraction of the value derives from the ability to hold cash over an extremely long horizon, and the risk of early redemption decreases the value and hedging performance of cash.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126819869","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":"Log-Modulated Rough Stochastic Volatility Models","authors":"Christian Bayer, Fabian A. Harang, P. Pigato","doi":"10.2139/ssrn.3668973","DOIUrl":"https://doi.org/10.2139/ssrn.3668973","url":null,"abstract":"We propose a new class of rough stochastic volatility models obtained by modulating the power-law kernel defining the fractional Brownian motion (fBm) by a logarithmic term, such that the kernel retains square integrability even in the limit case of vanishing Hurst index $H$. The so-obtained log-modulated fractional Brownian motion (log-fBm) is a continuous Gaussian process even for $H = 0$. As a consequence, the resulting super-rough stochastic volatility models can be analysed over the whole range $0 le H","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132821765","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 New Scheme for Proactive Risk Management in Stock Market","authors":"Akihiko Takahashi, Soichiro Takahashi","doi":"10.2139/ssrn.3666682","DOIUrl":"https://doi.org/10.2139/ssrn.3666682","url":null,"abstract":"This paper proposes a novel state-space approach to explain stock market dynamics driven by different types of trading, which leads to a new promising scheme for proactive risk management in financial investment. Particularly, it is assumed that the current price changes are formulated through daily trading by multiple types of traders, each of whom follows a specific investment strategy based on technical indicators and a fuzzy logic using past data of stock prices, volumes and yield curves. Moreover, the current price changes are represented by a linear combination of those multiple trading types, where the coefficients corresponding with the size of impact on the price changes are regarded as time-varying state variables to be sequentially estimated under a state-space framework. Thereby, this work develops a new factor decomposition method on price changes from a perspective of different traders' demand and supply to analyze the current situations and potential risks in financial markets.<br> <br>In empirical experiments, it is shown that the implementation of particle filtering algorithm makes it possible to replicate market price changes. Further, new signals based on the estimated states are developed, which are applied to proactive risk management in financial investment. Especially, it has been found that the demands of yield curve-based traders subtracting those of trend-followers could be a promising signal of stock market crashes, which has successfully enhanced simple buy-and-hold strategy of SP, as well as constant proportion strategies.<br>","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124267757","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":"Liquidation Value and Loan Pricing","authors":"F. Barbiero, G. Schepens, Jean-David Sigaux","doi":"10.2139/ssrn.3633769","DOIUrl":"https://doi.org/10.2139/ssrn.3633769","url":null,"abstract":"This paper shows that the liquidation value of collateral depends on the interdependency between borrower and collateral risk. Using transaction‐level data on short‐term repurchase agreements (repo), we show that borrowers pay a premium of 1.1 to 2.6 basis points when their default risk is positively correlated with the risk of the collateral that they pledge. Moreover, we show that borrowers internalize this premium when making their collateral choices. Loan‐level credit registry data suggest that the results extend to the corporate loan market as well.This article is protected by copyright. All rights reserved","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124404306","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 Deep Learning Approach to Estimate Forward Default Intensities","authors":"Marc-Aurèle Divernois","doi":"10.2139/ssrn.3657019","DOIUrl":"https://doi.org/10.2139/ssrn.3657019","url":null,"abstract":"This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous literature is to allow the estimation of non-linear forward intensities by using neural networks instead of classical maximum likelihood estimation. The model specification allows an easy replication of previous literature using linear assumption and shows the improvement that can be achieved.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124652855","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":"Duration Risks of Value-at-Risk","authors":"Kent Osband","doi":"10.2139/ssrn.3653461","DOIUrl":"https://doi.org/10.2139/ssrn.3653461","url":null,"abstract":"Since borrowers want minimal pressure to repay early while depositors want minimal constraints on withdrawals, banks typically borrow short to lend long. This is known as duration mismatch. To mitigate the risks, banks are required to hold capital buffers, which are intended to cover all losses from default nearly all of the time. A favored threshold is 99.9% per year, or one breach expected per thousand years. The capital needed to provide this protection is known as Value at Risk or VaR.<br><br>Unfortunately, major breaches of VaR occur far more often than standard models predict. The latter focus too much on outliers given static risks and not enough on the possibility that risks themselves are perceived to surge. For long duration credit bonds, the markdowns on pessimistic shifts in expectations can greatly outweigh the direct impact of a spurt in defaults.<br><br>Standard capital buffers cannot reliably cover these markdowns. Readjusting buffer requirements to duration and forecasts of future risks is fraught with estimation error and bound to induce regulatory arbitrage. The simplest remedy with the least moral hazard would limit duration mismatch.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133115541","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":"Approximate XVA for European Claims","authors":"Fabio Antonelli, A. Ramponi, S. Scarlatti","doi":"10.2139/ssrn.3652443","DOIUrl":"https://doi.org/10.2139/ssrn.3652443","url":null,"abstract":"We consider the problem of computing the Value Adjustment of European contingent claims when default of either party is considered, possibly including also funding and collateralization requirements. As shown in Brigo et al. (cite{BLPS}, cite{BFP}), this leads to a more articulate variety of Value Adjustments ({XVA}) that introduce some nonlinear features. When exploiting a reduced-form approach for the default times, the adjusted price can be characterized as the solution to a possibly nonlinear Backward Stochastic Differential Equation (BSDE). The expectation representing the solution of the BSDE is usually quite hard to compute even in a Markovian setting, and one might resort either to the discretization of the Partial Differential Equation characterizing it or to Monte Carlo Simulations. Both choices are computationally very expensive and in this paper we suggest an approximation method based on an appropriate change of numeraire and on a Taylor's polynomial expansion when intensities are represented by means of affine processes correlated with the asset's price. The numerical discussion at the end of this work shows that, at least in the case of the CIR intensity model, even the simple first-order approximation has a remarkable computational efficiency.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123413680","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":"Country Risk: Determinants, Measures and Implications – The 2020 Edition","authors":"A. Damodaran","doi":"10.2139/ssrn.3653512","DOIUrl":"https://doi.org/10.2139/ssrn.3653512","url":null,"abstract":"As companies and investors globalize, we are increasingly faced with estimation questions about the risk associated with this globalization. When investors invest in China Mobile, Infosys or Vale, they may be rewarded with higher returns, but they are also exposed to additional risk. When Siemens and Apple push for growth in Asia and Latin America, they clearly are exposed to the political and economic turmoil that often characterize these markets. In practical terms, how, if at all, should we adjust for this additional risk? We will begin the paper with an overview of overall country risk, its sources and measures. We will continue with a discussion of sovereign default risk and examine sovereign ratings and credit default swaps (CDS) as measures of that risk. We will extend that discussion to look at country risk from the perspective of equity investors, by looking at equity risk premiums for different countries and consequences for valuation. In the fourth section, we argue that a company’s exposure to country risk should not be determined by where it is incorporated and traded. By that measure, neither Coca Cola nor Nestle are exposed to country risk. Exposure to country risk should come from a company’s operations, making country risk a critical component of the valuation of almost every large multinational corporation. In the final section, we will also look at how to move across currencies in valuation and capital budgeting, and how to avoid mismatching errors.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128645663","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":"Adjusted Expected Shortfall","authors":"Matteo Burzoni, Cosimo Munari, Ruodu Wang","doi":"10.2139/ssrn.3650887","DOIUrl":"https://doi.org/10.2139/ssrn.3650887","url":null,"abstract":"We introduce and study the main properties of a class of convex risk measures that refine Expected Shortfall by simultaneously controlling the expected losses associated with different portions of the tail distribution. The corresponding adjusted Expected Shortfalls quantify risk as the minimum amount of capital that has to be raised and injected into a financial position $X$ to ensure that Expected Shortfall $ES_p(X)$ does not exceed a pre-specified threshold $g(p)$ for every probability level $pin[0,1]$. Through the choice of the benchmark risk profile $g$ one can tailor the risk assessment to the specific application of interest. We devote special attention to the study of risk profiles defined by the Expected Shortfall of a benchmark random loss, in which case our risk measures are intimately linked to second-order stochastic dominance.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124396671","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":"Critical Decisions in Crisis Management: Rational Strategies of Decision Making","authors":"M. Coccia","doi":"10.1453/JEL.V7I2.2049","DOIUrl":"https://doi.org/10.1453/JEL.V7I2.2049","url":null,"abstract":"Abstract. Turbulent environment can create crises that management has to soles in a limited time with critical decisions. Critical decisions are an attempt to apply efficient modes of cognition and action to enable the organization to cope with consequential environmental threats or take advantage of important opportunities in the presence of highly restricted time in turbulent markets and/or specific situations. Critical decisions involve a process of the organization’s leadership to think, consult, act, gain acceptance for optimal solutions to complex problems in the presence of highly restricted time in crisis given by scarce resources, uncertain factors, aversive environment, environmental difficulties, ambiguous circumstances, unclear and volatile situations, or a combination of these factors. This study presents the endogenous and exogenous types of crises for organizations and vital factors for critical decisions that can be categorized in responsitive, proactive and recovery critical decisions. After that, the study shows strategic operations and steps of critical decisions in a perspective of reductionism, and a rational structure based on tree diagram to systematize the process of decision making. The study here also suggests strategies for critical decisions in different environments based on theory of rational choice, such as max-min, max-max and min-max approaches, described with a vital example.  Final part of this study shows how a complex problem can be treated in different ways in a wider perspective of ecological rationality by approaches of resolution, solution and dissolution. The implications of strategic management are that the approach of dissolution of a complex problem requires design of a critical decision that may incorporate research and trial and error activities. Overall, then, this paper suggests one of the most effective way of solving systemic and complex problems by private and public organizations operating in, more and more, turbulent markets and volatile environments. Keywords.  Strategic management, Decision making, Critical decision, Crisis management, Competitive advantage, Strategies, Strategic change, Business Strategy, Operational excellence, Problem solving, Bounded rationality, Decision rule, Decision theory, Natural disasters, Risk management, Bounded rationality, Environmental threats, Ecological rationality, Theory of rational choice. JEL. C44, D70, D81, D91, H12, M51, Q54.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123967952","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}