nep-rmg New Economics Papers
on Risk Management
Issue of 2017‒07‒23
fifteen papers chosen by

  1. Dynamic Semiparametric Models for Expected Shortfall (and Value-at-Risk) By Andrew J. Patton; Johanna F. Ziegel; Rui Chen
  2. Towards Macroprudential Stress Testing; Incorporating Macro-Feedback Effects By Ivo Krznar; Troy D Matheson
  3. Pricing formulae for derivatives in insurance using the Malliavin calculus By Caroline Hillairet; Ying Jiao; Anthony R\'eveillac
  4. Regulatory Learning: how to supervise machine learning models? An application to credit scoring By Dominique Guegan; Bertrand Hassani
  5. Dynamic Linkages between Gold and Equity Prices: Evidence from Indian Financial Services and Information Technology Companies By Dey Shubhasis; Sampath Aravind
  6. SME Collateral: risky borrowers or risky behaviour? By Carroll, James; McCann, Fergal
  7. On the economic determinants of optimal stock-bond portfolios: international evidence By Conrad, Christian; Stuermer, Karin
  8. Firm Risk and Disclosures about Dispersion in Asset Values: By Badia, Marc; Barth, Mary E.; Duro, Miguel; Ormazabal, Gaizka
  9. Quantile relationships between standard, diffusion and jump betas across Japanese banks By Chowdhury, Biplob; Jeyasreedharan, Nagaratnam; Dungey, Mardi
  10. Risk Preferences in Small and Large Stakes: Evidence from Insurance Contract Decisions By Benjamin L. Collier; Daniel Schwartz; Howard C. Kunreuther; Erwann O. Michel-Kerjan
  11. Machine learning application in online lending risk prediction By Xiaojiao Yu
  12. Explaining and benchmarking corporate bond returns By Cici, Gjergji; Gibson, Scott; Moussawi, Rabih
  13. Risk, Unemployment, and the Stock Market: A Rare-Event-Based Explanation of Labor Market Volatility By Jessica Wachter; Mete Kilic
  14. OPEC News Announcement Effect on Volatility Jumps in the Crude Oil Market By Rangan Gupta; Chi Keung Marco Lau; Seong-Min Yoon
  15. Surplus-invariant risk measures By Niushan Gao; Cosimo Munari

  1. By: Andrew J. Patton; Johanna F. Ziegel; Rui Chen
    Abstract: Expected Shortfall (ES) is the average return on a risky asset conditional on the return being below some quantile of its distribution, namely its Value-at-Risk (VaR). The Basel III Accord, which will be implemented in the years leading up to 2019, places new attention on ES, but unlike VaR, there is little existing work on modeling ES. We use recent results from statistical decision theory to overcome the problem of "elicitability" for ES by jointly modelling ES and VaR, and propose new dynamic models for these risk measures. We provide estimation and inference methods for the proposed models, and confirm via simulation studies that the methods have good finite-sample properties. We apply these models to daily returns on four international equity indices, and find the proposed new ES-VaR models outperform forecasts based on GARCH or rolling window models.
    Date: 2017–07
  2. By: Ivo Krznar; Troy D Matheson
    Abstract: Macro-feedback effects have been identified as a key missing element for more effective macro-prudential stress testing. To fill this gap, this paper develops a framework that facilitates the analysis of both the direct effects of macroeconomic shocks on the solvency of individual banks and feedback effects that allow for the amplification and propagation of shocks that can result from bank deleveraging and credit crunches. The framework ensures consistency in the key relationships between macroeconomic and financial variables, and banks’ balance sheets. This is accomplished by embedding a standard stress-testing framework based on individual banks’ data in a semi-structural macroeconomic model. The framework has numerous applications that can strengthen stress testing and macro financial analysis. Moreover, it provides an avenue for many extensions that address the challenges of incorporating other second-round effects important for comprehensive systemic risk analysis, such as interactions between solvency, liquidity and contagion risks. To this end, the paper presents some preliminary simulations of feedback effects arising from the link between the liquidity and solvency risk.
    Date: 2017–06–30
  3. By: Caroline Hillairet (ENSAE ParisTech); Ying Jiao (SAF); Anthony R\'eveillac (INSA Toulouse, IMT)
    Abstract: In this paper we provide a valuation formula for different classes of actuarial and financial contracts which depend on a general loss process, by using the Malliavin calculus. In analogy with the celebrated Black-Scholes formula, we aim at expressing the expected cash flow in terms of a building block. The former is related to the loss process which is a cumulated sum indexed by a doubly stochastic Poisson process of claims allowed to be dependent on the intensity and the jump times of the counting process. For example, in the context of Stop-Loss contracts the building block is given by the distribution function of the terminal cumulated loss, taken at the Value at Risk when computing the Expected Shortfall risk measure.
    Date: 2017–07
  4. By: Dominique Guegan (Centre d'Economie de la Sorbonne and LabEx ReFi); Bertrand Hassani (Group Capgemini and Centre d'Economie de la Sorbonne and LabEx ReFi)
    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: Financial Regulation; Algorithm; Big Data; Risk
    JEL: C55
    Date: 2017–07
  5. By: Dey Shubhasis (Indian Institute of Management Kozhikode); Sampath Aravind (Indian Institute of Management Kozhikode)
    Abstract: In this paper, we use multivariate GARCH models to analyze dynamic linkages between gold and equity price returns. We model dynamic conditional correlations and volatility spillovers between these assets. Our results indicate that spot gold can be an effective hedge against stock prices. A $1 long position in the NIFTY Financial Services index can be hedged for 12 cents with a short position in spot gold and a $1 long position in the NIFTY Information Technology index can be hedged for 5 cents with a short position in spot gold. Gold also seems to act as a safe haven asset during the Global Financial Crisis period between 2007 and 2009. Our results suggest that crisis or not a prudent investor should allocate around 30 per cent of her investable assets in gold within a gold/stock portfolio. Given that in India around 41% of the population is still without access to banking services and are hence deprived of interest-earning deposits, it is not very surprising to find gold’s optimal portfolio weight to be as high as 30 per cent. However as banking services penetration in India improves and its inflation rate stabilizes around a low inflation target, we expect this portfolio weight to gradually come down to around 10% that is widely observed in studies involving more advanced economies.
    Keywords: Spot gold, stock, MGARCH, correlation, volatility spillovers
    Date: 2017–06
  6. By: Carroll, James (Trinity College Dublin); McCann, Fergal (Central Bank of Ireland)
    Abstract: We explore two motives in a bank’s use of collateral: an ex-ante stock-of-risk effect, whereby banks secure observably riskier loans to reduce future losses; an ex-post flow-of-risk effect, whereby banks use collateral to lower the probability of reduced borrower effort. Using loan-level data on Irish enterprise lending, we explore these two mechanisms. We confirm the stock-of-risk hypothesis while finding no evidence that collateral reduces the ex-post flow-of-risk. We also highlight the importance of loan size by showing that banks secure almost all loans in the top quintile of loan size regardless of risk rating, whereas among smaller loans, collateralisation is higher for riskier loans.
    Keywords: SME, Collateral, Risk, Moral Hazard
    Date: 2017–04
  7. By: Conrad, Christian; Stuermer, Karin
    Abstract: Using a modified DCC-MIDAS specification that allows the long-term correlation component to be a function of multiple explanatory variables, we show that the stock-bond correlation in the US, the UK, Germany, France, and Italy is mainly driven by inflation and interest rate expectations as well as a flight-to-safety during times of stress in financial markets. Based on the new DCC-MIDAS model, we construct stock-bond hedge portfolios and show that these portfolios outperform various benchmark portfolios in terms of portfolio risk. While optimal daily weights minimize portfolio risk, we find that portfolio turnover and trading costs can be substantially reduced when switching to optimal monthly weights.
    Keywords: stock-bond correlation; DCC; DCC-MIDAS; survey data; macro expectations; forecasting; portfolio choice; asset allocation
    Date: 2017–07–14
  8. By: Badia, Marc; Barth, Mary E.; Duro, Miguel; Ormazabal, Gaizka
    Abstract: This study examines whether mandated disclosure about the dispersion of the value of oil and gas (O&G) reserves provides information about firm risk. Based on a sample of Canadian O&G firms between 2004 and 2011, we find that the difference between the 10th and 50th percentiles of O&G reserves, which is a measure of dispersion of the reserves distribution, is positively associated with future total and idiosyncratic equity return volatility, systematic risk, and credit risk. We also find that disclosure of increases in reserves dispersion is associated with weaker stock price reactions to increases in reserve levels and with increases in bid-ask spreads, both of which indicate the disclosures convey information about risk associated with the reserves. Additional tests reveal it is unlikely that our findings are attributable to managerial opportunism in estimating reserves. Taken together, our study provides evidence that disclosures relating to the dispersion of non-financial asset values can provide information relevant to assessing firm risk.
    Date: 2017–07
  9. By: Chowdhury, Biplob (Tasmanian School of Business & Economics, University of Tasmania); Jeyasreedharan, Nagaratnam (Tasmanian School of Business & Economics, University of Tasmania); Dungey, Mardi (Tasmanian School of Business & Economics, University of Tasmania)
    Abstract: Using high frequency financial data and associated risk decomposition and quantile regression techniques we characterise some stylised facts and relationship(s) between standard betas, diffusion betas and jump betas of individual stocks and portfolios in Japanese market. We then investigate whether the beta in the conventional CAPM is the weighted average of the jump beta and diffusion beta in the jump-diffusion model and how these different betas behave across different banks. Our empirical findings indicate that jump betas are cross-sectionally more dispersed than diffusion and standard betas. We find that the relationship(s) between the three betas are non-linear. We also find that standard betas are influenced more by diffusion betas than the jump betas, although the actual magnitude of the weights differ significantly across the quantile. This relationship holds for both individual stocks and portfolios. Empirical studies have shown that betas vary systematically across large and small firm equities. For large equity portfolios, the jump beta-diffusion beta ratios are lower that the jump betadiffusion beta ratios of the small equity portfolios. Empirically, we further find that the standard CAPM beta is composed of two-components, i.e. it is the weighted average of the diffusion component and the jump component.
    Keywords: diffusion beta; jump beta; jump-diffusion beta ratio; quantile regression, Japanese banks
    JEL: G12 G19
    Date: 2017
  10. By: Benjamin L. Collier; Daniel Schwartz; Howard C. Kunreuther; Erwann O. Michel-Kerjan
    Abstract: We examine risk preferences using the flood insurance decisions of over 100,000 households. In each contract, households make a small stakes decision, the deductible, and a large stakes one, the coverage limit. Expected utility models predict that households would choose high deductibles and low coverage limits, but households do the opposite. Allowing for probability distortions improves our models. Assessing rank dependent utility models, we find that households follow two tenants of prospect theory: overestimation of small probabilities and diminishing sensitivity to losses. In every tested model, different preferences characterize households' small and large stakes insurance decisions.
    JEL: D12 D81 H42 Q54
    Date: 2017–07
  11. By: Xiaojiao Yu
    Abstract: Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with various format and size were collected from public website, third-parties and assembled with client's loan application information data. Ensemble machine learning models, random forest model and XGBoost model, were built and trained with the historical transaction data and subsequently tested with separate data. XGBoost model shows higher K-S value, suggesting better classification capability in this task. Top 10 important features from the two models suggest external data such as zhimaScore, multi-platform stacking loans information, and social network information are important factors in predicting loan default probability.
    Date: 2017–07
  12. By: Cici, Gjergji; Gibson, Scott; Moussawi, Rabih
    Abstract: We evaluate how different betas and characteristics related to default, term, and liquidity risk fare against one another in explaining the cross-section of corporate bond returns. We find that characteristics-credit rating, duration, and Amihud illiquidity measure-fare better. Yields add incremental explanatory power. Consistent with yields providing a timelier assessment of default risk than ratings, bonds with higher yields but similar credit ratings, durations and Amihud measures experience more subsequent ratings downgrades, fewer upgrades, and a higher frequency of defaults. Based on our findings, we present characteristic portfolios that can be used to benchmark individual bond and portfolio returns.
    Date: 2017
  13. By: Jessica Wachter (University of Pennsylvania); Mete Kilic (The Wharton School, University of Pennsylvania)
    Abstract: What is the driving force behind the cyclical behavior of unemployment and vacancies? What is the relation between job-creation incentives of firms and stock market valuations? We answer these questions in a model with time-varying risk, modeled as a small and variable probability of an economic disaster. A high probability implies greater risk and lower future growth, lowering the incentives of firms to invest in hiring. During periods of high risk, stock market valuations are low and unemployment rises. The model thus explains volatility in equity and labor markets, and the relation between the two.
    Date: 2017
  14. By: Rangan Gupta (Department of Economics, University of Pretoria, South Africa); Chi Keung Marco Lau (Newcastle Business School, Northumbria University, Newcastle, UK); Seong-Min Yoon (Department of Economics, Pusan National University, Busan, Korea)
    Abstract: This paper uses a nonparametric quantile-based methodology to analyse the predictive ability of OPEC meeting dates and production announcements on (Brent Crude and West Texas Intermediate) oil futures market volatility jumps. We found a nonlinear relationship between oil futures volatility jumps and OPEC-based predictors; hence, linear Granger-causality tests are misspecified and the linear model results of non-predictability are unreliable. Results of the quantile-causality test show that OPEC variables’ impact on oil futures markets is restricted to Brent Crude futures, with no effect observed for the WTI market. Specifically, OPEC production announcements and meeting dates predict only lower quantiles of the conditional distribution of Brent futures market volatility jumps.
    Keywords: Oil markets, Volatility jumps, OPEC announcements
    JEL: C22 C58 G14 Q41
    Date: 2017–07
  15. By: Niushan Gao; Cosimo Munari
    Abstract: We present a systematic study of the notion of surplus invariance, which plays a natural and important role in the theory of risk measures and capital requirements. So far, this notion has been investigated in the setting of special spaces of random variables. In this paper we develop a theory of surplus invariance in the framework of vector lattices. Besides providing a unifying perspective on the existing literature, this greater level of generality makes our results applicable to model spaces where a dominating probability is not available, which are becoming increasingly popular in the field of "robust finance". We establish a variety of new results, including structural results for surplus-invariant acceptance sets and powerful dual representations for surplus-invariant risk measures.
    Date: 2017–07

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