nep-rmg New Economics Papers
on Risk Management
Issue of 2018‒09‒10
thirteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Risk Management and Regulation By Tobias Adrian
  2. Modelling Volatility of Cryptocurrencies Using Markov-Switching Garch Models By Guglielmo Maria Caporale; Timur Zekokh
  3. ALM and Credit Risk By Edward Bace
  4. Diversification benefits under multivariate second order regular variation By Das, Bikramjit; Kratz, Marie
  5. Elements of an index-based margin insurance – an application to wheat production in Austria By Heinschink, Karin; Sinabell, Franz; Url, Thomas
  6. Repo market functioning: The role of capital regulation By Kotidis, Antonis; Van Horen, Neeltje
  7. Capital Requirements in a Quantitative Model of Banking Industry Dynamics By Pablo D'Erasmo; Dean Corbae
  8. La correlazione tra PD ed LGD nell’analisi del rischio di credito/The correlation between probability of default and loss given default in the credit risk analysis By Franco Varetto
  9. Volatility Term Structure Modeling Using Nelson-Siegel Model By Jozef Barunik; Barbora Malinska
  10. Put, call or strangle? About the challenges in designing weather index insurances to hedge performance risk in agriculture By Doms, Juliane
  11. Lessons from the U.S. risk management instruments for the future CAP By Gohin, Alexandre
  12. A note on representation of BSDE-based dynamic risk measures and dynamic capital allocations By Lesedi Mabitsela; Calisto Guambe; Rodwell Kufakunesu
  13. Manager Sentiment and Stock Market Volatility By Rangan Gupta

  1. By: Tobias Adrian
    Abstract: The evolution of risk management has resulted from the interplay of financial crises, risk management practices, and regulatory actions. In the 1970s, research lay the intellectual foundations for the risk management practices that were systematically implemented in the 1980s as bond trading revolutionized Wall Street. Quants developed dynamic hedging, Value-at-Risk, and credit risk models based on the insights of financial economics. In parallel, the Basel I framework created a level playing field among banks across countries. Following the 1987 stock market crash, the near failure of Salomon Brothers, and the failure of Drexel Burnham Lambert, in 1996 the Basel Committee on Banking Supervision published the Market Risk Amendment to the Basel I Capital Accord; the amendment went into effect in 1998. It led to a migration of bank risk management practices toward market risk regulations. The framework was further developed in the Basel II Accord, which, however, from the very beginning, was labeled as being procyclical due to the reliance of capital requirements on contemporaneous volatility estimates. Indeed, the failure to measure and manage risk adequately can be viewed as a key contributor to the 2008 global financial crisis. Subsequent innovations in risk management practices have been dominated by regulatory innovations, including capital and liquidity stress testing, macroprudential surcharges, resolution regimes, and countercyclical capital requirements.
    Keywords: Stock exchanges;Capital movements;Financial risk;Risk management;
    Date: 2018–08–01
  2. By: Guglielmo Maria Caporale; Timur Zekokh
    Abstract: This paper aims to select the best model or set of models for modelling volatility of the four most popular cryptocurrencies, i.e. Bitcoin, Ethereum, Ripple and Litecoin. More than 1,000 GARCH models are fitted to the log returns of the exchange rates of each of these cryptocurrencies to estimate a one-step ahead prediction of Value-at-Risk (VaR) and Expected Shortfall (ES) on a rolling window basis. The best model or superior set of models is then chosen by backtesting VaR and ES as well as using a Model Confidence Set (MCS) procedure for their loss functions. The results imply that using standard GARCH models may yield incorrect VaR and ES predictions, and hence result in ineffective risk-management, portfolio optimisation, pricing of derivative securities etc. These could be improved by using instead the model specifications allowing for asymmetries and regime switching suggested by our analysis, from which both investors and regulators can benefit.
    Keywords: cryptocurrencies, volatility, Markov-switching, GARCH
    JEL: C22 G12
    Date: 2018
  3. By: Edward Bace (Middlesex University)
    Abstract: Credit risk is the main risk exposure of the vast majority of banks in any country. It represents a primary risk to the balance sheet. In a financial institution, credit risk management must be the responsibility of the Asset and Liability Committee (ALCO). The recommended operating model is that ALCO has effective authority to monitor, and ultimately approve, all operational aspects that impact the balance sheet.Individual business lines will manage their respective credit risks under the direction of the credit risk committee which also sets the firm-wide policy. Management of credit exposure (at the balance sheet level) is frequently undertaken by the treasury or ALM department, through use of credit derivatives, for example.The nature of ALCO oversight is technical: capital, liquidity, market and non-traded market and other cash flow impacts on the balance sheet. Given this core aspect of ALCO?s role, the need arises to establish a technical sub-committee of ALCO, perhaps called The Balance Sheet Management Committee (BSMCO), chaired by the Treasurer, to review the balance sheet and escalates issues where necessary to ALCO. Membership of BSMCO is at one level below the senior executives (CEO, CFO, CRO) with the exception of the Treasurer.The other recommended technical sub-committee of ALCO is the Product Pricing Committee / Deposit Pricing Committee (PPCO/DPCO). This is a smaller committee whose remit is to ensure that, based on the recommended model, all pricing decisions are made by ALCO. Products in question would be customer deposit products, perhaps extended to customer asset products if deemed necessary. PPCO (or DPCO) has delegated authority to approve specific changes to standard rates for one-off transactions.Given its importance to the balance sheet, ALCO can only undertake its mission effectively if it has final authority on credit risk exposure and credit risk appetite. This means the overall policy of the Credit Risk Committee must fall to ALCO review.ALCO is responsible for through-the-cycle sustainability of the balance sheet. Since credit risk exposure is the main negative impact potential on the balance sheet, ALCO must have oversight of it. This does not mean day-to-day running and minutiae of credit risk origination. It means approval of policies, monitoring of exposure and approval authority on significant transactions and any policy changes. This research presents such recommendations for effective implementations of a bank ALM process.
    Keywords: Asset liability management, treasury, credit risk
    JEL: G21 G32
    Date: 2018–07
  4. By: Das, Bikramjit (Singapore University of Technology and Design); Kratz, Marie (ESSEC Research Center, ESSEC Business School)
    Abstract: We analyze risk diversification in a portfolio of heavy-tailed risk factors under the assumption of second order multivariate regular variation. Asymptotic limits for a measure of diversification benefit are obtained when considering, for instance, the value-at-risk . The asymptotic limits are computed in a few examples exhibiting a variety of different assumptions made on marginal or joint distributions. This study ties up existing related results available in the literature under a broader umbrella.
    Keywords: asymptotic theory; diversification benefit; heavy tail; risk concentration; second order regular variation; value-at-risk
    JEL: C02
    Date: 2017–04
  5. By: Heinschink, Karin; Sinabell, Franz; Url, Thomas
    Abstract: Farmers may use financial market instruments to hedge price risks. Moreover, various types of insurance products are on the market to protect against production losses. An insurance that covers losses of both input and output prices was recently introduced in the US. We develop this concept further by proposing a prototype of an index-based margin insurance which accounts for both production risks and price risks (input and output prices). The prototype is based on standardised gross margin time series for specific activities. It accounts for revenues, variable costs by cost item, various insurance coverage levels, and gross margin. Indemnities are paid if the gross margin falls short of a determined level. We identify steps necessary to accomplish a market-ready insurance product (e.g. data validation, defining the details of the sub-indexes and the premium calculation, evaluating acceptance on the market prior to its launch). Using Austrian data, the innovative approach is exemplified with respect to different farm management practices, more specifically for the case of conventional and organic wheat production. Farmers could benefit from such a margin insurance since production and price risks would be covered in one scheme, thus reducing opportunity costs.
    Keywords: Agricultural and Food Policy, Agricultural Finance, Risk and Uncertainty
    Date: 2017–04–24
  6. By: Kotidis, Antonis; Van Horen, Neeltje
    Abstract: This paper shows that the leverage ratio affects repo intermediation for banks and non-bank financial institutions. We exploit a novel regulatory change in the UK to identify an exogenous intensification of the leverage ratio and combine this with supervisory transaction-level data capturing the near-universe of gilt repo trading. Studying adjustments at the dealer-client level and controlling for demand and confounding factors, we find that dealers subject to a more binding leverage ratio reduced liquidity in the repo market. This affected their small but not their large clients. We further document a reduction in frequency of transactions and a worsening of repo pricing, but no adjustment in haircuts or maturities. Finally, we find evidence of market resilience, based on existing, rather than new repo relationships, with foreign, non-constrained dealers stepping in. Overall, our findings help shed light on the impact of Basel III capital regulation on repo markets.
    Keywords: Capital regulation; leverage ratio; non-bank financial institutions; repo market
    JEL: G10 G21 G23
    Date: 2018–07
  7. By: Pablo D'Erasmo (FRB Philadelphia); Dean Corbae (University of Wisconsin)
    Abstract: We develop a model of banking industry dynamics to study the quantitative impact of capital requirements on bank risk taking, commercial bank failure, and market structure. We propose a market structure where big banks with market power interact with small, competitive fringe banks. Banks face idiosyncratic funding shocks as well as aggregate shocks to the fraction of performing loans in their portfolio. A nontrivial size distribution of banks arises out of endogenous entry and exit, as well as banks' buffer stock of net worth. We test the model using business cycle properties and the bank lending channel across banks of different sizes. We then conduct a series of counterfactuals (including countercyclical requirements and size contingent (e.g. SIFI) requirements). We find that regulatory policies can have an important impact on market structure itself.
    Date: 2018
  8. By: Franco Varetto (CNR-IRCRES, National Research Council, Research Institute on Sustainable Economic Growth, via Real Collegio 30, Moncalieri (TO) – Italy and The Munk School of Global Affairs, University of Toronto, 315 Bloor Street West, Toronto, ON, M5S 0A7 Canada)
    Abstract: The international regulation on banking developed by Basel Committee on Banking Supervision has set a simplified link between default probabilities and loss given default, avoiding to introduce the correlation. The scientific literature ha proposed many models that try to improve the Basel framework. This article examines the most important models proposed in the literature and apply two of them to aggregate data from the Bank of Italy.
    Keywords: Default probability, loss given default, correlation, credit risk, credit portfolio model, credit VaR
    JEL: G21 G28 G33 C18
    Date: 2017–12
  9. By: Jozef Barunik (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic; Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod Vodarenskou Vezi 4, 182 00, Prague, Czech Republic); Barbora Malinska (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic)
    Abstract: Understanding of volatility term structure is highly relevant both for market agents and policymakers. As traditional methodologies often bring results contradicting situation on the markets, we revisit volatility term structure modeling in univariate case. In this paper we benefi t from extensive high-frequency dataset of US Treasury futures prices allowing us to empirically inspect the behaviour of the respective realized volatility term structure. We believe that the discovered properties justify the application of multi-factor modeling techniques primarily developed for yield curves. Finally we develop the comprehensive methodology fitting empirical data efficiently by term structure decomposition using Nelson-Siegel class of models.
    Keywords: Realized volatility; Term structure; Dynamic Nelson-Siegel model; High-frequency data
    Date: 2018–08
  10. By: Doms, Juliane
    Abstract: Due to an expected increase of extreme weather events caused by climate change, weather index insurances (WII), which can be used to hedge weather-related income fluctuations, are shifting into the spotlight. Most previous studies focus on the index design as it is an important part of a weather index insurance. Nevertheless, also of main importance is the general contract structure. This holds especially true for farms in regions, which are not characterized by extreme climatic conditions. In the present study, it is investigated whether precipitation and soil moisture index based put- and call-options as well as strangles reduce the volatility of total gross margins (hedging efficiency) of 20 German farms in regions with moderate natural conditions. In particular, the hedging efficiency of standardized and customized WII are analyzed. It could be found that customized contracts are better suitable to reduce performance risk than standardized contracts. Further, although the hedging efficiency varies considerably from farm to farm and depends highly on the contract type, the analyzed customized call-options and strangles clearly outperform the customized put-options.
    Keywords: Agribusiness, Farm Management, Risk and Uncertainty
    Date: 2017–08–15
  11. By: Gohin, Alexandre
    Keywords: Agricultural and Food Policy
    Date: 2018–04–26
  12. By: Lesedi Mabitsela; Calisto Guambe; Rodwell Kufakunesu
    Abstract: In this paper, we provide a representation theorem for dynamic capital allocation under It{\^o}-L{\'e}vy model. We consider the representation of dynamic risk measures defined under Backward Stochastic Differential Equations (BSDE) with generators that grow quadratic-exponentially in the control variables. Dynamic capital allocation is derived from the differentiability of BSDEs with jumps. The results are illustrated by deriving a capital allocation representation for dynamic entropic risk measure and static coherent risk measure.
    Date: 2018–08
  13. By: Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa)
    Abstract: This paper hypothesizes that corporate managers’ sentiment can predict aggregate stock market volatility. Using a k-th order nonparametric causality-in-quantiles test, we show that manager sentiment is a stronger predictor for volatility than stock return, especially when one accommodates for misspecification in the linear predictive model via a nonparametric data-driven approach. But, predictability is completely absent at extreme ends of the conditional distribution of return, and at the upper end of the same for volatility.
    Keywords: Manager Sentiment, Asset Pricing, Return and Volatility Predictability
    JEL: C22 C53 G11 G12
    Date: 2018–08

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