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on Risk Management |
| By: | Brkic, Sabina; Hodzic, Migdat; Dzanic, Enis |
| Abstract: | This paper deals with the use of fuzzy logic as a support tool for evaluation of corporate client credit risk in a commercial banking environment. It defines possibilistic distribution of soft data used for corporate client credit risk assessment by applying fuzzy logic modeling, with a major goal to develop a new expert decisionmaking fuzzy model for evaluating credit risk of corporate clients in a bank. Currently, predicting a credit risk of companies is inaccurate and ambiguous, as well as affected by many internal and external factors that cannot be precisely defined. Unlike traditional methods for credit risk assessment, fuzzy logic can easily incorporate linguistic terms and expert opinions which makes it more adapted to cases with insufficient and imprecise hard data, as well as for modeling risks that are not fully understood. Fuzzy model of soft data, presented in this paper, is created based on expert experience of corporate lending of a commercial bank in Bosnia and Herzegovina. This market is very small and it behaves irrationally and often erratically and therefore makes the risk assessment and management decision making process very complex and uncertain which requires new methods for risk modeling to be evaluated. Experts were interviewed about the types of soft variables used for credit risk assessment of corporate clients, as well as for providing the inputs for generating membership functions of these soft variables. All identified soft variables can be grouped into following segments: stability, capability and readiness/willingness of the client to repay a loan. The results of this work represent a new approach for soft data usage/assessment with an aim of being incorporated into a new and superior soft-hard data fusion model for client credit risk assessment. |
| Keywords: | fuzzy logic, credit risk, default risk, commercial banking |
| JEL: | C53 G21 G32 |
| Date: | 2017–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:83028&r=rmg |
| By: | Nguyen, Duc Binh Benno; Prokopczuk, Marcel; Wese Simen, Chardin |
| Abstract: | This paper examines the properties of the gold risk premium. We estimate a parsimonious model for the gold risk premium and uncover important time variations in the dynamics of the risk premium. We also estimate risk premia of the stock and bond markets, and investigate the role of gold as a hedge and safe haven asset from an ex-ante point of view. The results show that gold is not expected to serve as hedge and safe haven for the bond and stock markets, but it is so realized ex-post. Further, we find that gold is neither expected to be an inflation hedge nor is it realized. |
| Keywords: | Jump Risk; Tail Risk; Safe Haven; Hedge; Gold |
| JEL: | G01 G10 G11 Q02 |
| Date: | 2017–11 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-616&r=rmg |
| By: | Dominique Guegan (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Labex ReFi - Université Paris1 - Panthéon-Sorbonne); Bertrand Hassani (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Labex ReFi - Université Paris1 - Panthéon-Sorbonne) |
| Abstract: | The arrival of big data strategies is threatening the lastest trends in financial regulation related to the simplification of models and the enhancement of the comparability of approaches chosen by financial institutions. Indeed, the intrinsic dynamic philosophy of Big Data strategies is almost incompatible with the current legal and regulatory framework as illustrated in this paper. Besides, as presented in our application to credit scoring, the model selection may also evolve dynamically forcing both practitioners and regulators to develop libraries of models, strategies allowing to switch from one to the other as well as supervising approaches allowing financial institutions to innovate in a risk mitigated environment. The purpose of this paper is therefore to analyse the issues related to the Big Data environment and in particular to machine learning models highlighting the issues present in the current framework confronting the data flows, the model selection process and the necessity to generate appropriate outcomes. |
| Keywords: | Regulation,AUC,Machine Learning,Big Data,Credit Scoring |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:halshs-01592168&r=rmg |
| By: | Sivec, Vasja; Volk, Matjaz |
| Abstract: | This paper uncovers current, and estimates future, responses by banks which are under notification of increased capital requirements. We collect notifications on regulatory capital requirements sent to Slovenian banks in the period 2009-2015 and construct a forward-looking measure of capital surplus/shortfall. Using a differences-in-differences model we show that the same firm has on average a 3.54 p.p. lower loan growth when the loan is obtained through a bank with 1 p.p. higher capital shortfall. Once the capital surplus/shortfall is included in the regression model, the coefficient on the capital adequacy ratio, often used as the main policy variable in empirical literature, becomes insignificant. It is insignificant because surplus/shortfall is a forward-looking measure of bank capitalization and conveys more information about future lending. Finally, we show that in response to an increase in capital requirements banks engage in more risk-taking behaviour. Our paper carries policy implications for regulators in countries with distressed banking sector. |
| Keywords: | capital requirement, credit, regulation, risk taking, policy |
| JEL: | G01 G21 G28 |
| Date: | 2017–11–20 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:83058&r=rmg |
| By: | Marco Lo Duca; Anne Koban; Marisa Basten; Elias Bengtsson; Benjamin Klaus; Piotr Kusmierczyk; Jan Hannes Lang; Carsten Detken (editor); Tuomas Peltonen (editor) |
| Abstract: | This paper presents a new database for financial crises in European countries, which serves as an important step towards establishing a common ground for macroprudential oversight and policymaking in the EU. The database focuses on providing precise chronological definitions of crisis periods to support the calibration of models in macroprudential analysis. An important contribution of this work is the identification of financial crises by combining a quantitative approach based on a financial stress index with expert judgement from national and European authorities. Key innovations of this database are (i) the inclusion of qualitative information about events and policy responses, (ii) the introduction of a broad set of non-exclusive categories to classify events, and (iii) a distinction between event and post-event adjustment periods. The paper explains the two-step approach for identifying crises and other key choices in the construction of the dataset. Moreover, stylised facts about the systemic crises in the dataset are presented together with estimations of output losses and fiscal costs associated with these crises. A preliminary assessment of the performance of standard early warning indicators based on the new crises dataset confirms findings in the literature that multivariate models can improve compared to univariate signalling models. JEL Classification: G01, E44, E58, E60, H12 |
| Keywords: | financial crises, macroprudential, crises database, early warning models, central bank statistics |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:srk:srkops:201713&r=rmg |
| By: | Jacopo Piana (City University London); Daniele Bianchi (University of Warwick) |
| Abstract: | We analyse a novel time series of investors expectations on future commodity spot prices, and show that a model with adaptive learning can replicate investors' forecasts. We use this framework to back out the dynamics of the (ex-ante) risk premia for different commodities and maturities, and provide evidence that commodity risk premia are time-varying and their dynamics is predominantly due to the changing nature of risk sharing and appetite, as proxied by open interest, hedging pressure and time-series momentum. Finally, we show that the explanatory power of alternative factors is not constant over time, both across commodities and time horizons. |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:red:sed017:1149&r=rmg |
| By: | Hollstein, Fabian; Prokopczuk, Marcel; Wese Simen, Chardin |
| Abstract: | We study the term structure of variance (total risk), systematic and idiosyncratic risk. Consistent with the expectations hypothesis, we find that, for the entire market, the slope of the term structure of variance is mainly informative about the path of future variance. Thus, there is little indication of a time-varying term premium. Turning the focus to individual stocks, we cannot reject the expectations hypothesis for the systematic variance, but we strongly reject it for idiosyncratic variance. Our results are robust to jumps and potential statistical biases. |
| Keywords: | options; term structure; expectations hypothesis; model-free option implied variance; implied correlation; systematic risk; beta; idiosyncratic variance |
| JEL: | G12 G11 G17 |
| Date: | 2017–11 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-618&r=rmg |
| By: | Elie Bouri (USEK Business School, Holy Spirit University of Kaslik (USEK)); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Wing-Keung Wong (Department of Finance, Asia University, Department of Economics and Finance, Hang Seng Management College and Department of Economics,Lingnan University); Zhenzhen Zhu (School of Statistics, Shandong University of Finance and Economics) |
| Abstract: | We extend our understanding on the role of wine investment within a portfolio of different assets (US/UK equities, bonds, gold, and housing) by considering a rich methodology based, among others, on the mean-variance and stochastic-dominance approaches. The main findings suggest that wine is the best investment among all individual assets under study, and investors prefer to invest in with-wine portfolios than without-wine portfolios to gain higher expected utility when short sale is not allowed. However, investors are indifferent between portfolios with and without wine when short-selling is allowed. In addition, with-wine portfolios generally either dominate individual assets or are indifferent from individual assets. Interestingly, the with-wine portfolios first-order stochastically dominates housing in both long-only and short-allowed strategies, pointing towards market inefficiency and thus the possibility for an expected arbitrage opportunity. Finally, we reveal that investors prefer the low-risk with-wine portfolios to the equal-weighted portfolio, but are indifferent between the high-risk with-wine portfolios and the naïve portfolio for both long-only and short-allowed strategies. Our findings can be used by investors in their investment processes and reveal the possibility of earning abnormal returns when wine is included in the investment. |
| Keywords: | Wine investment, mean-variance portfolio optimization, mean-risk criterion, stochastic dominance, asset classes |
| JEL: | C10 G10 G15 |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201781&r=rmg |
| By: | Bucci, Andrea |
| Abstract: | Modeling financial volatility is an important part of empirical finance. This paper provides a literature review of the most relevant volatility models, with a particular focus on forecasting models. We firstly discuss the empirical foundations of different kinds of volatility. The paper, then, analyses the non-parametric measure of volatility, named realized variance, and its empirical applications. A wide range of realized volatility models, both univariate and multivariate, is presented, such as time series models, MIDAS and GARCH-MIDAS models, Realized GARCH, and HEAVY models. We further discuss forecasting evaluation methods specifically suited for volatility models. |
| Keywords: | Realized Volatility; Stochastic Volatility; Volatility Models |
| JEL: | C22 C53 G10 |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:83232&r=rmg |
| By: | Guillermo Ordonez (University of Pennsylvania); Selman Erol (MIT, CMU) |
| Abstract: | Optimal regulatory restrictions on banks have to solve a delicate balance. Tighter regulations reduce the likelihood of banks’ distress. Looser regulations foster the allocation of funds towards productive investments. With multiple banks, optimal regulation becomes even more challenging. Banks form partnerships in the interbank lending market to face liquidity needs and meet investment possibilities. We show that the interbank network may suddenly collapse once regulations are pushed above a critical level, with a discontinuous increase in systemic risk as banks’ cross-insurance collapses. |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:red:sed017:1125&r=rmg |
| By: | Juan Carlos Escanciano (Indiana University); Javier Hualde (Universidad Publica de Navarra) |
| Keywords: | Nonlinear dependence; Tail risk; Expected Short-fall; Persistence in variance; Market crashes |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:inu:caeprp:2017017&r=rmg |
| By: | Nguyen, Duc Binh Benno; Prokopczuk, Marcel; Wese Simen, Chardin |
| Abstract: | We examine the pricing of tail risk in international stock markets. We find that the tail risk of different countries is highly integrated. Introducing a new World Fear index, we find that local and global aggregate market returns are mainly driven by global tail risk rather than local tail risk. World fear is also priced in the crosssection of stock returns. Buying stocks with high sensitivities to World Fear while selling stocks with low sensitivities generates excess returns of up to 2.72% per month. |
| Keywords: | Jump Risk; Tail Risk; International Stock Market Returns; Return Predictability; International Asset Pricing; Factor Models |
| JEL: | G01 G11 G12 G17 |
| Date: | 2017–11 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-620&r=rmg |
| By: | Mokinski, Frieder |
| Abstract: | The severity function approach (abbreviated SFA) is a method of selecting adverse scenarios from a multivariate density. It requires the scenario user (e.g. an agency that runs banking sector stress tests) to specify a "severity function", which maps candidate scenarios into a scalar severity metric. The higher the value of this metric, the more harmful a scenario is. In selecting a scenario the SFA proceeds as follows: First, it isolates a set of equally severe scenario candidates. This set is determined by the condition that more severe scenarios only occur with some user-specified probability. Second, from this set it selects the candidate with the highest probability density, i.e. the most plausible scenario. The approach hence operationalizes the mantra that "scenarios should be severe yet plausible". |
| Keywords: | Stress Testing,Conditional Forecasting,Density Forecasting,Time series,Bayesian VAR,Simulation |
| JEL: | C11 C32 C53 C61 G01 G32 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:342017&r=rmg |
| By: | Rodosthenous, Neofytos; Zervos, Mihail |
| Abstract: | We consider a new family of derivatives whose payoffs become strictly positive when the price of their underlying asset falls relative to its historical maximum. We derive the solution to the discretionary stopping problems arising in the context of pricing their perpetual American versions by means of an explicit construction of their value functions. In particular, we fully characterise the free-boundary functions that provide the optimal stopping times of these genuinely two-dimensional problems as the unique solutions to highly non-linear first order ODEs that have the characteristics of a separatrix. The asymptotic growth of these free-boundary functions can take qualitatively different forms depending on parameter values, which is an interesting new feature. |
| Keywords: | optimal stopping; running maximum process; variational inequality; two dimensional free-boundary problem; separatrix |
| JEL: | C61 G13 |
| Date: | 2017–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:67859&r=rmg |