nep-rmg New Economics Papers
on Risk Management
Issue of 2019‒07‒22
thirty-one papers chosen by
Stan Miles
Thompson Rivers University

  1. P2P Loan acceptance and default prediction with Artificial Intelligence By Jeremy D. Turiel; Tomaso Aste
  2. Benchmarking Operational Risk Stress Testing Models By Filippo Curti; Marco Migueis; Rob T. Stewart
  3. Variable Annuities: Underlying Risks and Sensitivities By Chahboun, Imad; Hoover, Nathaniel
  4. Cyber-risk insurance — a big challenge facing contemporary economies By Leonardo Badea; Calin Rangu
  5. Pricing Credit Default Swap Subject to Counterparty Risk and Collateralization By Tim, Xiao
  6. Competition and Bank Risk the Role of Securitization and Bank Capital By Yener Altunbas; David Marques‐Ibanez; Michiel van Leuvensteijn; Tianshu Zhao
  7. A sensitivity analysis of the long-term expected utility of optimal portfolios By Hyungbin Park; Stephan Sturm
  8. A comparative analysis of the criteria for evaluating projects funded from structural funds from a risk management perspective By Ciprian Nicolae
  9. Risk-dependent centrality in economic and financial networks By Paolo Bartesaghi; Michele Benzi; Gian Paolo Clemente; Rosanna Grassi; Ernesto Estrada
  10. Mean and Volatility Spillovers between REIT and Stocks Returns A STVAR-BTGARCH-M Model By Das, Mahamitra; Kundu, Srikanta; Sarkar, Nityananda
  11. Tracking VIX with VIX Futures: Portfolio Construction and Performance By Tim Leung; Brian Ward
  12. Have the LVR restrictions improved the resilience of the banking system? By Chris Bloor; Bruce Lu
  13. The Case for Long-Only Agnostic Allocation Portfolios By Pierre-Alain Reigneron; Vincent Nguyen; Stefano Ciliberti; Philip Seager; Jean-Philippe Bouchaud
  14. Machine learning with kernels for portfolio valuation and risk management By Lotfi Boudabsa; Damir Filipovic
  15. Hurricane Katrina Floods New Jersey: The Role of Information in the Market Response to Flood Risk. By Nicholas Z. Muller; Caroline A. Hopkins
  16. Business Tax Policy under Default Risk By Comincioli, Nicola; Vergalli, Sergio; Panteghini, Paolo M.
  17. Rethinking the opportunity/necessity dichotomy with a risk management- based approach By Caroline Bayart; Séverine Saleilles
  18. Empowering Central Bank Asset Purchases: The Role of Financial Policies By Matthieu Darracq Paries; Jenny Korner; Niki Papadopoulou
  19. Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach By Trucíos, Carlos; Mazzeu, João H. G.; Hallin, Marc; Hotta, Luiz K.; Pereira, Pedro L. Valls; Zevallos, Mauricio
  20. Forecasting extreme events on financial markets By Ionut Cosmin Nastase
  21. Large Volatility Matrix Prediction with High-Frequency Data By Xinyu Song
  22. Banking Panic Risk and Macroeconomic Uncertainty By Mikkelsen, Jakob; Poeschl, Johannes
  23. Tax- and expense-modified risk-minimization for insurance payment processes By Kristian Buchardt; Christian Furrer; Thomas M{\o}ller
  24. Switzerland; Financial Sector Assessment Program; Technical Note-Insurance Stress Testing By International Monetary Fund
  25. A vulnerability analysis of Irish SME credit exposures By McGeever, Niall
  26. Systemic Optimal Risk Transfer Equilibrium By Francesca Biagini; Alessandro Doldi; Jean-Pierre Fouque; Marco Frittelli; Thilo Meyer-Brandis
  27. Who are the Loss-Averse Farmers? Experimental Evidence from Structurally Estimated Risk Preferences By Bonjean, Isabelle
  28. Emotions, Risk Attitudes, and Patience By Armando N. Meier
  29. Deep Smoothing of the Implied Volatility Surface By Damien Ackerer; Natasa Tagasovska; Thibault Vatter
  30. Nonparametric Assessment of Hedge Fund Performance By Almeida, Caio; Ardison, Kim; Garcia, René
  31. The Bahamas; Financial Sector Assessment Program-Technical Note on Financial Stability and Stress Testing By International Monetary Fund

  1. By: Jeremy D. Turiel; Tomaso Aste
    Abstract: Logistic Regression and Support Vector Machine algorithms, together with Linear and Non-Linear Deep Neural Networks, are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans. A two phase model is proposed; the first phase predicts loan rejection, while the second one predicts default risk for approved loans. Logistic Regression was found to be the best performer for the first phase, with test set recall macro score of $77.4 \%$. Deep Neural Networks were applied to the second phase only, were they achieved best performance, with validation set recall score of $72 \%$, for defaults. This shows that AI can improve current credit risk models reducing the default risk of issued loans by as much as $70 \%$. The models were also applied to loans taken for small businesses alone. The first phase of the model performs significantly better when trained on the whole dataset. Instead, the second phase performs significantly better when trained on the small business subset. This suggests a potential discrepancy between how these loans are screened and how they should be analysed in terms of default prediction.
    Date: 2019–07
  2. By: Filippo Curti; Marco Migueis; Rob T. Stewart
    Abstract: The Federal Reserve’s Comprehensive Capital Analysis and Review (CCAR) requires large bank holding companies (BHCs) to project losses under stress scenarios. In this paper, we propose multiple benchmarks for operational loss projections and document the industry distribution relative to these benchmarks. The proposed benchmarks link BHCs’ loss projections with both financial characteristics and metrics of historical loss experience. These benchmarks capture different measures of exposure and together provide a comprehensive view of the reasonability of model outcomes. Furthermore, we employ several approaches to assess the conservatism of BHCs’ stress loss projections and our estimates for the conservatism of loss projections for the median bank range from the 90th percentile to above the 99th percentile of the operational loss distribution.
    Keywords: Benchmarking ; Operational Risk ; Stress Testing
    JEL: G28 G21 G32
    Date: 2019–05–28
  3. By: Chahboun, Imad (Federal Reserve Bank of Boston); Hoover, Nathaniel (Federal Reserve Bank of Boston)
    Abstract: This paper presents a quantitative model designed to understand the sensitivity of variable annuity (VA) contracts to market and actuarial assumptions and how these sensitivities make them a potentially important source of risk to insurance companies during times of stress. VA contracts often include long dated guarantees of market performance that expose the insurer to multiple nondiversifiable risks. Our modeling framework employs a Monte Carlo simulation of asset returns and policyholder behavior to derive fair prices for variable annuities in a risk neutral framework and to estimate sensitivities of reserve requirements under a real‐world probability measure. Simulated economic scenarios are applied to four hypothetical insurance company VA portfolios to assess the sensitivity of portfolio pricing and reserve levels to portfolio characteristics, modelling choices, and underlying economic assumptions. Additionally, a deterministic stress scenario, modeled on Japan beginning in the mid‐90s, is used to estimate the potential impact of a severe, but plausible, economic environment on the four hypothetical portfolios. The main findings of this exercise are: (1) interactions between market risk modeling assumptions and policyholder behavior modeling assumptions can significantly impact the estimated costs of providing guarantees, (2) estimated VA prices and reserve requirements are sensitive to market price discontinuities and multiple shocks to asset prices, (3) VA prices are very sensitive to assumptions related to interest rates, asset returns, and policyholder behavior, and (4) a drawn‐out period of low interest rates and asset underperformance, even if not accompanied by dramatic equity losses, is likely to result in significant losses in VA portfolios.
    Keywords: insurance risk; market risk; variable annuities; derivative pricing; policyholder behavior
    JEL: C15 G12 G17 G22 G23
    Date: 2019–04–09
  4. By: Leonardo Badea (Authority of Financial Supervision, Bucharest); Calin Rangu (Authority of Financial Supervision, Bucharest)
    Abstract: Cyber-security beyond the concept must be a product to be offered to modern society. We live in a complex world based on the digitization of products and services. Digitization also involves a complex system of associated risks. The road to the world of tomorrow goes through today's world and an analysis of the current situation in the contemporary economies about cyber risk insurance is only a first step that this article aims to achieve.The study aims to substantiate the need to formulate and assume policies and to support the regulation of cyber risk coverage by ensuring the need to support sectoral strategies to increase the level of maturity of companies from the perspective of protection against cyber threats. There is also a need to set up a cyber-risk reporting system, at least for critical and important infrastructures, the development and use by insurers of advisory and evaluation models based on standards and certifications recognized at the level of including the development of necessary skills for insurers to engineer these risks.
    Keywords: cyber risk, insurance, risk management, GDPR, underwriting, CISO, IT security, privacy, availability, data integrity, critical infrastructure, NIS, CERT
    JEL: D81 G22 H56 M15
    Date: 2019–05
  5. By: Tim, Xiao
    Abstract: This article presents a new model for valuing a credit default swap (CDS) contract that is affected by multiple credit risks of the buyer, seller and reference entity. We show that default dependency has a significant impact on asset pricing. In fact, correlated default risk is one of the most pervasive threats in financial markets. We also show that a fully collateralized CDS is not equivalent to a risk-free one. In other words, full collateralization cannot eliminate counterparty risk completely in the CDS market.
    Keywords: valuation model; credit risk modeling; collateralization; correlation, CDS.
    JEL: D46 G01 G12 G13 G17
    Date: 2019–03–06
  6. By: Yener Altunbas; David Marques‐Ibanez; Michiel van Leuvensteijn; Tianshu Zhao
    Abstract: We examine how bank competition in the run-up to the 2007–2009 crisis affects banks’ systemic risk during the crisis. We then investigate whether this effect is influenced by two key bank characteristics: securitization and bank capital. Using a sample of the largest listed banks from 15 countries, we find that greater market power at the bank level and higher competition at the industry level lead to higher realized systemic risk. The results suggest that the use of securitization exacerbates the effects of market power on the systemic dimension of bank risk, while capitalization partially mitigates its impact.
    Date: 2019–07–02
  7. By: Hyungbin Park; Stephan Sturm
    Abstract: This paper discusses the sensitivity of the long-term expected utility of optimal portfolios for an investor with constant relative risk aversion. Under an incomplete market given by a factor model, we consider the utility maximization problem with long-time horizon. The main purpose is to find the long-term sensitivity, that is, the extent how much the optimal expected utility is affected in the long run for small changes of the underlying factor model. The factor model induces a specific eigenpair of an operator, and this eigenpair does not only characterize the long-term behavior of the optimal expected utility but also provides an explicit representation of the expected utility on a finite time horizon. We conclude that this eigenpair therefore determines the long-term sensitivity. As examples, explicit results for several market models such as the Kim--Omberg model for stochastic excess returns and the Heston stochastic volatility model are presented.
    Date: 2019–06
  8. By: Ciprian Nicolae (Bucharest Academy of Economic Studies, Bucharest, Romania)
    Abstract: The evaluation of projects with non-reimbursable financing is an essential process for the efficiency of funds' use, regardless of the donor. Moreover, in the context of European Unionfunds managementand the multitude of development needs to be covered, Romania needed to establish the most efficient evaluation systems and criteria, the application of which leads to the selection of projects with better economic and social impact.This article outlines the results of a research approach that compared, fromthe perspective of the degree of subjectivity and their relevance to the selection of projects, the assessment criteria setby the managing authoritiesfor several relevant programs funded from Structural Funds 2007-2013 and 2014-2020.The research results allow answers to be definedto an important question for authorities and institutions that manage grants in Romania: Is there a link between how to approach the project evaluation process and the manifestation of risks specific to the evaluation process?Also, the results of the research allow a more detailed analysis of how anunitary risk management methodology for all non-reimbursable grants managed in Romania could be applied to the risks specific forthe project evaluation process.
    Keywords: risk, risk management, grants, projects, project evaluation
    JEL: G32 H83
    Date: 2019–05
  9. By: Paolo Bartesaghi; Michele Benzi; Gian Paolo Clemente; Rosanna Grassi; Ernesto Estrada
    Abstract: Node centrality is one of the most important and widely used concepts in the study of complex networks. Here, we extend the paradigm of node centrality in financial and economic networks to consider the changes of node "importance" produced not only by the variation of the topology of the system but also as a consequence of the external levels of risk to which the network as a whole is submitted. Starting from the "Susceptible-Infected" (SI) model of epidemics and its relation to the communicability functions of networks we develop a series of risk-dependent centralities for nodes in (financial and economic) networks. We analyze here some of the most important mathematical properties of these risk-dependent centrality measures. In particular, we study the newly observed phenomenon of ranking interlacement, by means of which two entities may interlace their ranking positions in terms of risk in the network as a consequence of the change in the external conditions only, i.e., without any change in the topology. We test the risk-dependent centralities by studying two real-world systems: the network generated by collecting assets of the S\&P 100 and the corporate board network of the US top companies, according to Forbes in 1999. We found that a high position in the ranking of the analyzed financial companies according to their risk-dependent centrality corresponds to companies more sensitive to the external market variations during the periods of crisis.
    Date: 2019–07
  10. By: Das, Mahamitra; Kundu, Srikanta; Sarkar, Nityananda
    Abstract: In this study we have examined volatility spillovers as well as volatility-in-mean effect between REIT returns and stock returns for both the USA and the UK by applying a bivariate GARCH-M model where the conditional mean is specified by a smooth transition VAR model. Dynamic conditional correlation approach has been applied with the GJR-GARCH specification so that the intrinsic nature of asymmetric volatility in case of positive and negative shocks can be duly captured. The major findings that we have empirically found is that the mean spillover effect from stock returns to REIT returns is significant for both the countries while the same from REIT returns to stock returns is significant only in the UK. It is also evident from the results that own risk-return relationship of REIT market is positive and significant only in the bear market situation in both the countries while for the stock market own risk-return relationship is insignificant for both the bull and bear markets in the USA but it is negative in the bear market condition and positive in the bull market situation for the UK. We have also found that asymmetric nature of conditional variance and dynamic behavior in the conditional correlation holds as well. Finally, several tests of hypotheses regarding equality of various kinds of spillover effects in the bull and bear market situations show that these spillover effects are not the same in the two market conditions in most of the aspects considered in this study.
    Keywords: REIT; Volatility Spillover; STVAR-BTGARCH_M
    JEL: C58 G1 G11
    Date: 2019–07–01
  11. By: Tim Leung; Brian Ward
    Abstract: We study a series of static and dynamic portfolios of VIX futures and their effectiveness to track the VIX index. We derive each portfolio using optimization methods, and evaluate its tracking performance from both empirical and theoretical perspectives. Among our results, we show that static portfolios of different VIX futures fail to track VIX closely. VIX futures simply do not react quickly enough to movements in the spot VIX. In a discrete-time model, we design and implement a dynamic trading strategy that adjusts daily to optimally track VIX. The model is calibrated to historical data and a simulation study is performed to understand the properties exhibited by the strategy. In addition, comparing to the volatility ETN, VXX, we find that our dynamic strategy has a superior tracking performance.
    Date: 2019–06
  12. By: Chris Bloor; Bruce Lu (Reserve Bank of New Zealand)
    Abstract: As part of sound regulatory practice, the Reserve Bank wants to further its understanding, and the public’s understanding, of how the policy has influenced financial stability. This paper contributes to this objective by developing a modelling framework that quantifies the extent that the loan-to-value ratio (LVR) policy has improved the resilience of the banking system to a severe downturn in house prices. We find that the LVR restrictions have significantly improved the resilience of the banking system. The LVR policy has reduced the scale of mortgage defaults and credit losses that would occur in a housing downturn, due to a reduction in risky loans on bank balance sheets and the mitigation of a potential house price decline. This resilience benefit has been partly offset by a fall in capital requirements that results from lower credit risk, reducing the banks’ buffer for absorbing credit losses. Nevertheless, the LVR policy is estimated to have reduced mortgage losses – as a share of the capital banks hold against their housing loans – by 12 percentage points. The policy is found to have mitigated about half of the deterioration in bank resilience from 2013 that would have occurred in the absence of the policy. Our estimates are sensitive to judgements on key variables and inputs. The resilience benefit of the LVR policy is contingent on the level of housing market risk that would exist without the policy. This suggests a stronger case to deploy the LVR tool when the risk of a house price decline is high. We were unable to model the resilience benefit of restricting property investor lending with confidence, although a provisional estimate suggests that the benefit may be large. Therefore, the headline estimate may understate the resilience benefit of the LVR intervention. A comprehensive assessment of the policy’s efficacy needs to consider the cost of the policy, which is outside the scope of this paper.
    Date: 2019–05
  13. By: Pierre-Alain Reigneron; Vincent Nguyen; Stefano Ciliberti; Philip Seager; Jean-Philippe Bouchaud
    Abstract: We advocate the use of Agnostic Allocation for the construction of long-only portfolios of stocks. We show that Agnostic Allocation Portfolios (AAPs) are a special member of a family of risk-based portfolios that are able to mitigate certain extreme features (excess concentration, high turnover, strong exposure to low-risk factors) of classical portfolio construction methods, while achieving similar performance. AAPs thus represent a very attractive alternative risk-based portfolio construction framework that can be implemented in different situations, with or without an active trading signal.
    Date: 2019–06
  14. By: Lotfi Boudabsa; Damir Filipovic
    Abstract: We introduce a computational framework for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the replicating martingale of a portfolio from a finite sample of its terminal cumulative cash flow. The learned replicating martingale is given in closed form thanks to a suitable choice of the kernel. We develop an asymptotic theory and prove convergence and a central limit theorem. We also derive finite sample error bounds and concentration inequalities. Numerical examples show good results for a relatively small training sample size.
    Date: 2019–06
  15. By: Nicholas Z. Muller; Caroline A. Hopkins
    Abstract: This study uses hedonic property models to explore how coastal real estate markets subject to heterogeneous information treatments respond to flood risk. We identify reactions to flood risk, distinctly from price effects due to flood damage, by examining non-local flooding events. Utilizing a difference-in-difference methodology, we test whether the coastal real estate market in New Jersey responds to several well-publicized hurricanes and tropical storms that did not strike the Atlantic seaboard. We find that homes in high flood risk zones situated in towns that participate in public flood awareness activities incur a 7 to 16 percent decrease in price after the non-local shock.
    JEL: H41 Q51 Q54 R31
    Date: 2019–06
  16. By: Comincioli, Nicola; Vergalli, Sergio; Panteghini, Paolo M.
    Abstract: In this article we use a stochastic model with one representative firm to study business tax policy under default risk. We will show that, for a given tax rate, the government has an incentive to reduce (increase) financial instability and default costs if its objective function is welfare (tax revenue).
    Keywords: Research Methods/ Statistical Methods
    Date: 2019–07–18
  17. By: Caroline Bayart (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Séverine Saleilles (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)
    Date: 2019–06–03
  18. By: Matthieu Darracq Paries (European Central Bank); Jenny Korner (d-fine - analytical. quantitative. tech.); Niki Papadopoulou (Central Bank of Cyprus)
    Abstract: This paper contributes to the debate on the macroeconomic effectiveness of expansionary non-standard monetary policy measures in a regulated banking environment. Based on an estimated DSGE model, we explore the interactions between central bank asset purchases and bank capital-based financial policies (regulatory, supervisory or macroprudential) through its influence on bank risk-shifting motives. We find that weakly-capitalised banks display excessive risk-taking which reinforces the credit easing channel of central bank asset purchases, at the cost of higher bank default probability and risks to financial stability. In such a case, adequate bank capital demand through higher minimum capital requirements curtails the excessive credit origination and restores a more efficient propagation of central bank asset purchases. As supervisors can formulate further capital demands, uncertainty about the supervisory oversight provokes precautionary motives for banks. They build-up extra capital buffer attenuating non-standard monetary policy. Finally, in a weakly-capitalised banking system, countercyclical macroprudential policy attenuates banks risk-taking and dampens the excessive persistence of the non-standard monetary policy impulse. On the contrary, in a well-capitalised banking system, the macroeconomic stabilisation with central bank asset purchases outweigh the marginal financial stability benefits with macroprudential policy.
    Keywords: non-standard monetary policy; asset purchases; bank capital regulation; risk-taking; regulatory uncertainty; effective lower bound
    JEL: E44 E52 E58
    Date: 2019–02
  19. By: Trucíos, Carlos; Mazzeu, João H. G.; Hallin, Marc; Hotta, Luiz K.; Pereira, Pedro L. Valls; Zevallos, Mauricio
    Abstract: Based on a General Dynamic Factor Model with infinite-dimensional factor space, we develop a new estimation and forecasting procedures for conditional covariance matrices in high-dimensional time series. The performance of our approach is evaluated via Monte Carlo experiments, outperforming many alternative methods. The new procedure is used to construct minimum variance portfolios for a high-dimensional panel of assets. The results are shown to achieve better out-of-sample portfolio performance than alternative existing procedures.
    Date: 2019–06
  20. By: Ionut Cosmin Nastase (Academy of Economic Studies)
    Abstract: The events of the recent financial crisis from 2007-2008 were the basis for choosing this topic and justified the desire to deepen crises on financial markets. “Too big to fall” is a statement that this crisis has dismantled in just few months through the bankruptcy of US large-scale financial conglomerates such as Lehman Brothers, Bear Stearns, Merrill Lynch and others. September 2008 is a month that many will not forget, a “dark” month in which the entire global financial system froze, marked by huge creditors taken in collapse (Fannie Mae and Freddie Mac), by the buying of the bankrupted bank Bear Stearns by J.P Morgan for 2$/share, the collapse of Lehman Brothers, followed by the bankruptcy and the collapse of the largest American Insurance Group (AIG), which has been taken by the government. This paper’s objective is to determine if, based on historical events – last financial crisis – we can determine whether we can define certain methods or instruments which can be used as signals for anticipating future extreme events on financial markets and how accurate and applicable they are.
    Keywords: Financial crisis, Extreme events, Financial markets, Global financial system, Bankruptcy, Financial Engineering, Securities
    JEL: G01 G17 G20
    Date: 2017–11
  21. By: Xinyu Song
    Abstract: We provide a novel method for large volatility matrix prediction with high-frequency data by applying eigen-decomposition to daily realized volatility matrix estimators and capturing eigenvalue dynamics with ARMA models. Given a sequence of daily volatility matrix estimators, we compute the aggregated eigenvectors and obtain the corresponding eigenvalues. Eigenvalues in the same relative magnitude form a time series and the ARMA models are further employed to model the dynamics within each eigenvalue time series to produce a predictor. We predict future large volatility matrix based on the predicted eigenvalues and the aggregated eigenvectors, and demonstrate the advantages of the proposed method in volatility prediction and portfolio allocation problems.
    Date: 2019–07
  22. By: Mikkelsen, Jakob; Poeschl, Johannes
    Abstract: We show that systemic risk in the banking sector breeds macroeconomic uncertainty. In a production economy with a banking sector, financial constraints of banks can lead to disastrous banking panics. We find that a higher probability of a banking panic increases uncertainty in the aggregate economy. We explore the implications of this banking panic-driven uncertainty for business cycles, asset prices and macroprudential regulation. Banking panic-driven uncertainty amplifies business cycle volatility, increases risk premia on asset prices and yields a new benefit from countercyclical bank capital buffers.
    Keywords: Banking Panics, Systemic Risk, Endogenous Uncertainty, Macroprudential Policy
    JEL: E44 G12 G21 G28
    Date: 2019–06–27
  23. By: Kristian Buchardt; Christian Furrer; Thomas M{\o}ller
    Abstract: We study the problem of determining risk-minimizing investment strategies for insurance payment processes in the presence of taxes and expenses. We consider the situation where taxes and expenses are paid continuously and symmetrically and introduce the concept of tax- and expense-modified risk-minimization. Risk-minimizing strategies in the presence of taxes and expenses are derived and linked to Galtchouk-Kunita-Watanabe decompositions associated with modified versions of the original payment processes. Furthermore, we show equivalence to an alternative approach involving an artificial market consisting of after-tax and after-expense assets, and we establish a type of consistency with classic risk-minimization. Finally, a case study involving classic multi-state life insurance payments in combination with a bond market exemplifies the results.
    Date: 2019–07
  24. By: International Monetary Fund
    Abstract: Financial Sector Assessment Program; Technical Note-Insurance Stress Testing
    Date: 2019–06–26
  25. By: McGeever, Niall (Central Bank of Ireland)
    Abstract: I use loan-level data from three major banks to analyse the stock of performing Irish SME credit exposures as at June 2018. I calculate a vulnerability score for each exposure by linking borrower characteristics and macroeconomic conditions to historical default outcomes. I find evidence of improvement in the condition of the aggregate SME portfolio, but a subset of exposures – accounting for 7.3 per cent of performing balances – continue to have high vulnerability scores. These exposures are spread across all regions. Accommodation & Food and Wholesale & Retail borrowers account for a large share of high vulnerability balances, while borrowers in the Agriculture, Forestry & Fishing and Manufacturing sectors are under-represented relative to their share of outstanding credit.
    Date: 2019–06
  26. By: Francesca Biagini; Alessandro Doldi; Jean-Pierre Fouque; Marco Frittelli; Thilo Meyer-Brandis
    Abstract: We propose a novel concept of a Systemic Optimal Risk Transfer Equilibrium (SORTE), which is inspired by the B\"uhlmann's classical notion of an Equilibrium Risk Exchange. We provide sufficient general assumptions that guarantee existence, uniqueness, and Pareto optimality of such a SORTE. In both the B\"uhlmann and the SORTE definition, each agent is behaving rationally by maximizing his/her expected utility given a budget constraint. The two approaches differ by the budget constraints. In Buhlmann's definition the vector that assigns the budget constraint is given a priori. On the contrary, in the SORTE approach, the vector that assigns the budget constraint is endogenously determined by solving a systemic utility maximization. SORTE gives priority to the systemic aspects of the problem, in order to optimize the overall systemic performance, rather than to individual rationality.
    Date: 2019–07
  27. By: Bonjean, Isabelle
    Keywords: Farm Management, Research Methods/ Statistical Methods
    Date: 2019–07–13
  28. By: Armando N. Meier
    Abstract: Previous work has shown that preferences are not always stable across time, but surprisingly little is known about the reasons for this instability. I examine whether variation in people’s emotions over time predicts changes in preferences. Using a large panel data set, I find that within-person changes in happiness, anger, and fear have substantial effects on risk attitudes and patience. Robustness checks indicate a limited role of alternative explanations. I further address potential endogeneity concerns by exploiting information about the death of a parent or child. This identification strategy confirms a large causal impact of emotions on preferences.
    Keywords: Emotions, risk attitudes, patience, risk preferences, time preferences
    JEL: D01 D90 D91
    Date: 2019
  29. By: Damien Ackerer; Natasa Tagasovska; Thibault Vatter
    Abstract: We present an artificial neural network (ANN) approach to value financial derivatives. Atypically to standard ANN applications, practitioners equally use option pricing models to validate market prices and to infer unobserved prices. Importantly, models need to generate realistic arbitrage-free prices, meaning that no option portfolio can lead to risk-free profits. The absence of arbitrage opportunities is guaranteed by penalizing the loss using soft constraints on an extended grid of input values. ANNs can be pre-trained by first calibrating a standard option pricing model, and then training an ANN to a larger synthetic dataset generated from the calibrated model. The parameters transfer as well as the non-arbitrage constraints appear to be particularly useful when only sparse or erroneous data are available. We also explore how deeper ANNs improve over shallower ones, as well as other properties of the network architecture. We benchmark our method against standard option pricing models, such as Heston with and without jumps. We validate our method both on training sets, and testing sets, namely, highlighting both their capacity to reproduce observed prices and predict new ones.
    Date: 2019–06
  30. By: Almeida, Caio; Ardison, Kim; Garcia, René
    Abstract: We propose a new class of performance measures for Hedge Fund (HF) returns based on a family of empirically identiable stochastic discount factors (SDFs). The SDF-based measures incorporate no-arbitrage pricing restrictions and naturally embed information about higher-order mixed moments between HF and benchmark factors returns. We provide a full asymptotic theory for our SDF estimators to test for the statistical signicance of each fund's performance and for the relevance of individual benchmark factors within each proposed measure. We apply our methodology to a panel of 4815 individual hedge funds. Our empirical analysis reveals that fewer funds have a statistically signicant positive alpha compared to the Jensen's alpha obtained by the traditional linear regression approach. Moreover, the funds' rankings vary considerably between the two approaches. Performance also varies between the members of our family because of a dierent fund exposure to higherorder moments of the benchmark factors, highlighting the potential heterogeneity across investors in evaluating performance.
    Keywords: Hedge Funds; Admissible Performance Measures; Nonparametric Estimation; Higher-order Moments
    JEL: G12 G13 C14 C58
    Date: 2019–07
  31. By: International Monetary Fund
    Abstract: Macrofinancial risks stem from the economy’s vulnerability to external shocks to tourism and real estate investment, exposure to frequent and severe hurricanes, and a small and illiquid real estate market. Stress tests reveal the overall banking system is resilient to a range of adverse scenarios given large aggregate capital and liquidity buffers. Some domestic banks and the two largest credit unions are more vulnerable to asset quality shocks and tail risk conditions. Asset quality and profitability are key determinants of financial institutions’ resilience to adverse shocks. Liquidity, market, sovereign and financial contagion risks are low. The offshore banking sector is not a source of traditional banking risks.
    Date: 2019–07–01

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