nep-rmg New Economics Papers
on Risk Management
Issue of 2016‒12‒11
thirteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Undiversifying during Crises: Is It a Good Idea? By Giuzio, Margherita; Paterlini, Sandra
  2. Duality in ruin problems for ordered risk models By Pierre-Olivier Goffard; Claude Lefèvre
  3. Borrowers under water!: Rare disasters, regional banks, and recovery lending By Koetter, Michael; Noth, Felix; Rehbein, Oliver
  4. Originating Loan to Value ratios and the resilience of mortgage portfolios By McCann, Fergal; Ryan, Ellen
  5. Agricultural insurance in Estonia: a tool for production risk management? By Nurmet, Maire
  6. Ambiguity and insurance: capital requirements and premiums By Simon Dietz; Oliver Walker
  7. A Market Driver Volatility Model via Policy Improvement Algorithm By Jun Maeda; Saul D. Jacka
  8. Stock Return Prediction with Fully Flexible Models and Coefficients By Byrne, Joseph; Fu, Rong
  9. Conditional Mean-Variance and Mean-Semivariance models in portfolio optimization By Hanene Salah; Ali Gannoun; Christian De Peretti; Mathieu Ribatet
  10. 'Capital Requirements, Risk Taking and Welfare in a Growing Economy' By Pierre-Richard Agénor; L. Pereira da Silva
  11. Stock Prices Predictability at Long-horizons: Two Tales from the Time-Frequency Domain By Nikolaos Mitianoudis; Theologos Dergiades
  12. Financial Stability of Islamic and Conventional Banks in Saudi Arabia: Evidence using Pooled and Panel Models By Ghassan, Hassan B.; Taher, Farid B.
  13. Towards socially responsible (re)insurance underwriting practices: readily available ‘big data’ contributions to optimize catastrophe risk management By Zvezdov, Ivelin

  1. By: Giuzio, Margherita; Paterlini, Sandra
    Abstract: High levels of correlation among financial assets, as well as extreme losses, are typical during crisis periods. In such situations, quantitative asset allocation models are often not robust enough to deal with estimation errors and lead to identifying underperforming investment strategies. It is an open question if in such periods, it would be better to hold diversified portfolios, such as the equally weighted, rather than investing in few selected assets. In this paper, we show that alternative strategies developed by constraining the level of diversification of the portfolio, by means of a regularization constraint on the sparse lq-norm of portfolio weights, can better deal with the trade-off between risk diversification and estimation error. In fact, the proposed approach automatically selects portfolios with a small number of active weights and low risk exposure. Insights on the diversification relationships between the classical minimum variance portfolio, risk budgeting strategies, and diversification-constrained portfolios are also provided. Finally, we show empirically that the diversification-constrained-based lq-strategy outperforms state-of-art methods during crises, with remarkable out-of-sample performance in risk minimization.
    Keywords: minimum variance portfolio; sparsity; diversification; regularization methods;
    JEL: C58 G11
    Date: 2016–12–02
  2. By: Pierre-Olivier Goffard (UCSB - UCSB - University of California, Santa Barbara [Santa Barbara]); Claude Lefèvre (ULB - Département de Mathématique [Bruxelles] - ULB - Université Libre de Bruxelles [Bruxelles])
    Abstract: The dual risk model is considered when the gain arrivals are governed by an order statistic point process (OSPP). The p.d.f. of the ruin time is obtained in terms of a remarkable family of polynomials. By duality, the p.d.f. of the ruin time is deduced for a Sparre-Andersen insurance risk model where the claim sizes are distributed as the inter-arrival times in an OSPP. On the other hand, duality is used again to derive the finite-time ruin probability in a dual model where the gains correspond to the inter-arrival times of an OSPP. MSC 2010: 60G55, 60G40, 12E10.
    Keywords: Order statistic property,Appell and Abel-Gontcharoff polynomials,Dual risk model,Time to ruin,Risk theory
    Date: 2016–11–17
  3. By: Koetter, Michael; Noth, Felix; Rehbein, Oliver
    Abstract: We test if and how banks adjust their lending in response to disaster risk in the form of a natural catastrophe striking its customers: the 2013 Elbe flooding. The flood affected firms in East and South Germany, and we identify shocked banks based on bank-firm relationships gathered for more than a million firms. Banks with relationships to flooded firms lend 13-23% more than banks without such customers compared to the preflooding period. This lending hike is associated with higher protability and reduced risk. Our results suggest that local banks are an effective mechanism to mitigate rare disaster shocks faced especially by small and medium firms.
    Keywords: disaster risk,credit demand,natural disaster,relationship lenders
    JEL: G21 G29 O16 Q54
    Date: 2016
  4. By: McCann, Fergal (Central Bank of Ireland); Ryan, Ellen (Central Bank of Ireland)
    Abstract: It is widely acknowledged that mortgage lending with lower Loan to Value (LTV) ratios is expected to have a lower probability of default, which will increase the resilience of a bank's mortgage portfolio to adverse events. This Letter focuses on another channel through which lower-LTV lending can lead to improvements in bank balance sheet resilience: the lowering of losses in the event of a default (Loss Given Liquidation, LGL). Using data from three major mortgage lenders in Ireland on loans for property purchase, we focus on originating LTVs on mortgages issued between 2003 and 2016 to make a number of observations on the evolution of mortgage portfolio resilience. Firstly, aggregate hypothetical losses experienced in the event of a common shock are at the lowest level since 2003 among the cohorts of loans issued since the introduction of recent Central Bank of Ireland mortgage market regulations. Secondly, the correlation between originating LTV and loan size has been falling steadily since 2006, reflecting a decreased tendency for banks to make their largest loans also their most highly leveraged, which leads directly to improvements in portfolio-level resilience. Finally, we show that improvements in the resilience of mortgages to adverse house price shocks are most pronounced at the right tail of the LTV distribution, where the highest-risk lending has reduced significantly over the 2008-2016 period.
    Date: 2016–11
  5. By: Nurmet, Maire
    Keywords: Farm Management, Risk and Uncertainty,
    Date: 2016–09–04
  6. By: Simon Dietz; Oliver Walker
    Abstract: Many insurance contracts are contingent on events such as hurricanes, terrorist attacks or political upheavals, whose probabilities are ambiguous. This paper offers a theory to underpin the large body of empirical evidence showing that higher premiums are charged under ambiguity. We model a (re)insurer who maximises profit subject to a survival constraint that is sensitive to the range of estimates of the probability of ruin, as well as the insurer’s attitude towards this ambiguity. We characterise when one book of insurance is more ambiguous than another and general circumstances in which a more ambiguous book requires at least as large a capital holding. We subsequently derive several explicit formulae for the price of insurance contracts under ambiguity, each of which identifies the extra ambiguity load.
    Keywords: ambiguity; ambiguity aversion; ambiguity load; capital requirement; catastrophe risk; insolvency; insurance; more ambiguous; reinsurance; ruin; uncertainty; Solvency II
    JEL: D81 G22
    Date: 2016
  7. By: Jun Maeda; Saul D. Jacka
    Abstract: In the over-the-counter market in derivatives, we sometimes see large numbers of traders taking the same position and risk. When there is this kind of concentration in the market, the position impacts the pricings of all other derivatives and changes the behaviour of the underlying volatility in a nonlinear way. We model this effect using Heston's stochastic volatility model modified to take into account the impact. The impact can be incorporated into the model using a special product called a market driver, potentially with a large face value, affecting the underlying volatility itself. We derive a revised version of Heston's partial differential equation which is to be satisfied by arbitrary derivatives products in the market. This enables us to obtain valuations that reflect the actual market and helps traders identify the risks and hold appropriate assets to correctly hedge against the impact of the market driver.
    Date: 2016–12
  8. By: Byrne, Joseph; Fu, Rong
    Abstract: We evaluate stock return predictability using a fully flexible Bayesian framework, which explicitly allows for different degrees of time-variation in coefficients and in forecasting models. We believe that asset return predictability can evolve quickly or slowly, based upon market conditions, and we should account for this. Our approach has superior out-of-sample predictive performance compared to the historical mean, from a statistical and economic perspective. We also find that our model statistically dominates its nested models, including models in which parameters evolve at a constant rate. By decomposing sources of prediction uncertainty into five parts, we find that our fully flexible approach more precisely identifies time-variation in coefficients and in forecasting models, leading to mitigation of estimation risk and forecasting improvements. Finally, we relate predictability to the business cycle.
    Keywords: Stock Return Prediction, Time-Varying Coefficients and Forecasting Models, Bayesian econometrics, Forecast combination
    JEL: C11 G11 G12 G17
    Date: 2016–11–09
  9. By: Hanene Salah (Laboratoire BESTMOD ISG Tunis - ISG Tunis, IMAG - Institut Montpelliérain Alexander Grothendieck - UM - Université de Montpellier - CNRS - Centre National de la Recherche Scientifique, ISFA - Institut des Science Financière et d'Assurances - PRES Université de Lyon); Ali Gannoun (IMAG - Institut Montpelliérain Alexander Grothendieck - UM - Université de Montpellier - CNRS - Centre National de la Recherche Scientifique); Christian De Peretti (ISFA - Institut des Science Financière et d'Assurances - PRES Université de Lyon); Mathieu Ribatet (IMAG - Institut Montpelliérain Alexander Grothendieck - UM - Université de Montpellier - CNRS - Centre National de la Recherche Scientifique)
    Abstract: It is known that the historical observed returns used to estimate the expected return provide poor guides to predict the future returns. Consequently, the optimal portfolio weights are extremely sensitive to the return assumptions used. Getting information about the future evolution of different asset returns, could help the investors to obtain more efficient portfolio. The solution will be reached by estimating the portfolio risk by conditional variance or conditional semivari-ance. This strategy allows us to take advantage of returns prediction which will be obtained by nonparametric univariate methods. Prediction step uses kernel estimation of conditional mean. Application on the Chinese and the American markets are presented and discussed.
    Keywords: Conditional Semivariance,Conditional Variance,DownSide Risk,Kernel Method,Nonparametric Mean prediction
    Date: 2016–11–29
  10. By: Pierre-Richard Agénor; L. Pereira da Silva
    Abstract: The effects of capital requirements on risk taking and welfare are studied in a stochastic overlapping generations model of endogenous growth with banking, limited liability, and government guarantees. Capital producers face a choice between a safe technology and a risky (but socially inefficient) technology, and bank risk taking is endogenous. Setting the capital adequacy ratio above a structural threshold can eliminate the equilibrium with risky loans (and thus inefficient risk taking), but numerical simulations show that this may entail a welfare loss. In addition, the optimal ratio may be too high in practice and may require concomitantly a broadening of the perimeter of regulation and a strengthening of financial supervision to prevent disintermediation and distortions in financial markets.
    Date: 2016
  11. By: Nikolaos Mitianoudis (Democritus University of Thrace, Greece); Theologos Dergiades (University of Macedonia, Greece)
    Abstract: Accepting non-linearities as an endemic feature of financial data, this paper re-examines Cochrane's "new fact in finance" hypothesis (Cochrane, Economic Perspectives -FRB of Chicago 23, 36-58, 1999). By implementing two methods, frequently encountered in digital signal processing analysis, (Undecimated Wavelet Transform and Empirical Mode Decomposition- both methods extract components in the time-frequency domain), we decompose the real stock prices and the real dividends, for the US economy, into signals that correspond to distinctive frequency bands. Armed with the decomposed signals and acting within a non-linear framework, the predictability of stock prices through the use of dividends is assessed at alternative horizons. It is shown that the "new fact in finance" hypothesis is a valid proposition, provided that dividends contribute significantly to predicting stock prices at horizons spanning beyond 32 months. The identified predictability is entirely non-linear in nature.
    Keywords: Stock prices and dividends, Time-frequency decomposition.
    JEL: G10 C14 C22 C29
    Date: 2016–12
  12. By: Ghassan, Hassan B.; Taher, Farid B.
    Abstract: The financial crises are considered the major challenges facing the prosperity and stability of the banking system and menace its stability. Several studies on the financial and banking sector have demonstrated that Islamic banks have shown more financial robustness and stability compared to conventional banks, over periods of financial crises. This research aims to measure the stability extent of the Saudi Arabia banks including Islamic banks and conventional banks using quarterly data from 2005 to 2011. This period is characterized by the global financial crisis shocks (2007-2008). The sample used is composed of six banks including two Islamic banks (AlRajhi Bank and AlBilad Bank) and four traditional banks (Riyad Bank, Saudi Investment Bank, Saudi British Bank and Saudi American Bank). This sample represents an important part of 64% of the Saudi banking sector and covers close to two-thirds of banks whose shares are traded on the Saudi stock market. The research focuses on three types of variables related to bank, banking system and macroeconomic levels. The paper is based on quantitative tools using panel regression and pooled regression to model the z-score index for testing the banks' stability in Saudi Arabia. The panel data model shows that Islamic banks reduce relatively the value of the financial stability index; meanwhile, they contribute efficiently to enhance the financial stability through the diversification of their assets. The findings indicate those Riyad Bank and SAMBA groups support efficiently the financial stability of banking sector, while AlRajhi bank has a positive but moderate role in enhancing the banking sector stability. The Saudi banking sector has relatively less level of competitiveness, that affecting negatively the financial stability. The limited representation of Islamic banks in the Saudi banking sector jeopardizes any efforts to improve the financial stability index.
    Keywords: Islamic Banks, Financial Crisis, Financial Stability, Z-score Model, Saudi Arabia.
    JEL: C12 G21 G28
    Date: 2015–01
  13. By: Zvezdov, Ivelin
    Abstract: Today's advances in big data technologies readily allow for storing large inter-dependent data sets of historical and modeled natural hazard and financial data and unifying their granularity and accuracy with common geo-spatial and risk-type record identifiers. This is a significant component at both single insurance account, and even more so at the larger multi-policy portfolio scale for enabling optimal and socially responsible insurance underwriting practices. This supports insurance risk transfers by creating more accurate and all-uncertainty encompassing pricing techniques, and exposes these techniques and methodologies to all market players, including insurance policy holders via transparent statistical and actuarial principles.
    Keywords: Big Data, (Re)Insurance Premium Pricing, Sustainable (Re)Insurance Principles
    JEL: D0 D01 G0 O3 O30
    Date: 2016–09–26

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