nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒02‒24
thirteen papers chosen by

  1. Intra-Horizon Expected Shortfall and Risk Structure in Models with Jumps By Walter Farkas; Ludovic Mathys; Nikola Vasiljevi\'c
  2. Stochastic control of optimized certainty equivalents By Julio Backhoff Veraguas; A. Max Reppen; Ludovic Tangpi
  3. Crowded trades, market clustering, and price instability By Marc van Kralingen; Diego Garlaschelli; Karolina Scholtus; Iman van Lelyveld
  4. Interest and credit risk management in German banks: Evidence from a quantitative survey By Dräger, Vanessa; Heckmann-Draisbach, Lotta; Memmel, Christoph
  5. Optimal Bank Regulation In the Presence of Credit and Run-Risk By Anil K. Kashyap; Dimitrios P. Tsomocos; Alexandros P. Vardoulakis
  6. Equal Risk Pricing and Hedging of Financial Derivatives with Convex Risk Measures By Saeed Marzban; Erick Delage; Jonathan Yumeng Li
  7. Bank Regulation and Supervision Ten Years after the Global Financial Crisis By Anginer,Deniz; Bertay,Ata Can; Cull,Robert J.; Demirguc-Kunt,Asli; Mare,Davide Salvatore
  8. Affine Modeling of Credit Risk, Pricing of Credit Events and Contagion By Alain MONFORT; Jean-Paul RENNE; Guillaume ROUSSELLET
  9. Managing GDP Tail Risk By Thibaut Duprey; Alexander Ueberfeldt
  10. Risk management and its implications on household incomes By Collins-Sowah, Peron A.; Henning, Christian H. C. A.
  11. Estimating high dimensional multivariate stochastic volatility models By Matteo, Pelagatti; Giacomo, Sbrana
  12. Sensitivity Analysis in the Dupire Local Volatility Model with Tensorflow By Francois Belletti; Davis King; James Lottes; Yi-Fan Chen; John Anderson
  13. Financial Risk Management in Agriculture : Analyzing Data from a New Module of the Global Findex Database By Klapper,Leora; Singer,Dorothe; Ansar,Saniya; Hess,Jake Richard

  1. By: Walter Farkas; Ludovic Mathys; Nikola Vasiljevi\'c
    Abstract: The present article deals with intra-horizon risk in models with jumps. Our general understanding of intra-horizon risk is along the lines of the approach taken in Boudoukh, Richardson, Stanton and Whitelaw (2004), Rossello (2008), Bhattacharyya, Misra and Kodase (2009), Bakshi and Panayotov (2010), and Leippold and Vasiljevi\'c (2019). In particular, we believe that quantifying market risk by strictly relying on point-in-time measures cannot be deemed a satisfactory approach in general. Instead, we argue that complementing this approach by studying measures of risk that capture the magnitude of losses potentially incurred at any time of a trading horizon is necessary when dealing with (m)any financial position(s). To address this issue, we propose an intra-horizon analogue of the expected shortfall for general profit and loss processes and discuss its key properties. Our intra-horizon expected shortfall is well-defined for (m)any popular class(es) of L\'evy processes encountered when modeling market dynamics and constitutes a coherent measure of risk, as introduced in Cheridito, Delbaen and Kupper (2004). On the computational side, we provide a simple method to derive the intra-horizon risk inherent to popular L\'evy dynamics. Our general technique relies on results for maturity-randomized first-passage probabilities and allows for a derivation of diffusion and single jump risk contributions. These theoretical results are complemented with an empirical analysis, where popular L\'evy dynamics are calibrated to S&P 500 index data and an analysis of the resulting intra-horizon risk is presented.
    Date: 2020–02
  2. By: Julio Backhoff Veraguas; A. Max Reppen; Ludovic Tangpi
    Abstract: Optimized certainty equivalents (OCEs) is a family of risk measures widely used by both practitioners and academics. This is mostly due to its tractability and the fact that it encompasses important examples, including entropic risk measures and average value at risk. In this work we consider stochastic optimal control problems where the objective criterion is given by an OCE risk measure, or put in other words, a risk minimization problem for controlled diffusions. A major difficulty arises since OCEs are often time inconsistent. Nevertheless, via an enlargement of state space we achieve a substitute of sorts for time consistency in fair generality. This allows us to derive a dynamic programming principle and thus recover central results of (risk-neutral) stochastic control theory. In particular, we show that the value of our risk minimization problem can be characterized via the viscosity solution of a Hamilton--Jacobi--Bellman--Issacs equation. We further establish the uniqueness of the latter under suitable technical conditions.
    Date: 2020–01
  3. By: Marc van Kralingen (Aegon N.V.); Diego Garlaschelli (Lorentz Institute); Karolina Scholtus (Erasmus University); Iman van Lelyveld (Vrije Universiteit Amsterdam)
    Abstract: Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock the clustering measure captures the degree of trading overlap among any two investors in that stock. We investigate the effect of crowded trades on stock price stability and show that market clustering has a causal effect on the properties of the tails of the stock return distribution, particularly the positive tail, even after controlling for commonly considered risk drivers. Reduced investor pool diversity could thus negatively affect stock price stability.
    Keywords: crowded trading, tail-risk, financial stability
    JEL: G02 G14 G20
    Date: 2020–02–04
  4. By: Dräger, Vanessa; Heckmann-Draisbach, Lotta; Memmel, Christoph
    Abstract: Using unique data of a survey among small and medium-sized German banks, we analyze various aspects of risk management over a short-term and medium-term horizon. We especially analyze the effect of a 200-bp increase in the interest level. We find that, in the first year, the impairments of banks' bond portfolios are much larger than the reductions in their net interest income, that banks attenuate the resulting write-downs by liquidating hidden reserves and that banks which use interest derivatives have lower impairments in their bond portfolios. In addition, we find that banks' exposures to interest rate risk and to credit risk are remunerated, that banks' try to stabilize the mid-term net interest margin with exposure to interest rate risk and that they act as if they have a risk budget which they allocate either to interest rate risk or credit risk.
    Keywords: net interest margin,bond portfolio,interest rate risk,credit risk
    JEL: G21
    Date: 2020
  5. By: Anil K. Kashyap; Dimitrios P. Tsomocos; Alexandros P. Vardoulakis
    Abstract: We modify the Diamond and Dybvig (1983) model so that, besides offering liquidity services to depositors, banks also raise equity funding, make loans that are risky, and can invest in safe, liquid assets. The bank and its borrowers are subject to limited liability. When profitable, banks monitor borrowers to ensure that they repay loans. Depositors may choose to run based on conjectures about the resources that are available for people withdrawing early and beliefs about banks’ monitoring. We use a new type of global game to solve for the run decision. We find that banks opt for a more deposit-intensive capital structure than a social planner would choose. The privately chosen asset portfolio can be more or less lending-intensive, while the scale of intermediation can also be higher or lower depending on a planner’s preferences between liquidity provision and credit extension. To correct these three distortions, a package of three regulations is warranted.
    JEL: E44 G01 G21 G28
    Date: 2020–01
  6. By: Saeed Marzban; Erick Delage; Jonathan Yumeng Li
    Abstract: In this paper, we consider the problem of equal risk pricing and hedging in which the fair price of an option is the price that exposes both sides of the contract to the same level of risk. Focusing for the first time on the context where risk is measured according to convex risk measures, we establish that the problem reduces to solving independently the writer and the buyer's hedging problem with zero initial capital. By further imposing that the risk measures decompose in a way that satisfies a Markovian property, we provide dynamic programming equations that can be used to solve the hedging problems for both the case of European and American options. All of our results are general enough to accommodate situations where the risk is measured according to a worst-case risk measure as is typically done in robust optimization. Our numerical study illustrates the advantages of equal risk pricing over schemes that only account for a single party, pricing based on quadratic hedging (i.e. $\epsilon$-arbitrage pricing), or pricing based on a fixed equivalent martingale measure (i.e. Black-Scholes pricing). In particular, the numerical results confirm that when employing an equal risk price both the writer and the buyer end up being exposed to risks that are more similar and on average smaller than what they would experience with the other approaches.
    Date: 2020–02
  7. By: Anginer,Deniz; Bertay,Ata Can; Cull,Robert J.; Demirguc-Kunt,Asli; Mare,Davide Salvatore
    Abstract: This paper summarizes the latest update of the World Bank Bank Regulation and Supervision Survey. The paper explores and summarizes the evolution in bank capital regulations, capitalization of banks, market discipline, and supervisory power since the global financial crisis. It shows that regulatory capital increased, but some elements of capital regulations became laxer. Market discipline may have deteriorated as the financial safety nets became more generous after the crisis. Bank supervision became stricter and more complex compared with the pre?global financial crisis period. However, supervisory capacity did not increase in proportion to the extent and complexity of new bank regulations. The paper documents the importance of defining bank regulatory capital narrowly, as the quality of capital matters in reducing bank risk. This is particularly true for large banks, because they have more discretion in the computation of risk weights and are better able to issue a variety of capital instruments.
    Date: 2019–10–15
  8. By: Alain MONFORT (CREST); Jean-Paul RENNE (University of Lausanne, HEC); Guillaume ROUSSELLET (Desautels Faculty of Management, McGill University)
    Abstract: We propose a discrete-time affi ne pricing model that simultaneously allows for (i) the presence of systemic entities by departing from the no-jump condition on the factors'conditional distribution, (ii) contagion effects, (iii) and the pricing of credit events. Our a ffine framework delivers explicit pricing formulas for default-sensitive securities like bonds and credit default swaps (CDS). We estimate a multi-country version of the model and address economic questions related to the pricing of sovereign credit risk. Speci cally, using euro-area data, we explore the in fluence of allowing for the pricing of credit events, we compare frailty and contagion channels, and we extract measures of depreciation-at-default from CDS denominated indifferent currencies.
    Keywords: a ffine credit risk model, Gamma-zero distribution, no-jump condition, contagion, credit-eventrisk, sovereign credit risk and exchange rates.
    JEL: E43 G12
    Date: 2019–12–31
  9. By: Thibaut Duprey; Alexander Ueberfeldt
    Abstract: We propose a novel framework to analyze how policy-makers can manage risks to the median projection and risks specific to the tail of gross domestic product (GDP) growth. By combining a quantile regression of GDP growth with a vector autoregression, we show that monetary and macroprudential policy shocks can reduce credit growth and thus GDP tail risk. So policymakers concerned about GDP tail risk would choose a tighter policy stance at the expense of macroeconomic stability. Using Canadian data, we show how our framework can add tail event information to projection models that ignore them and give policy-makers a tool to communicate the trade-offs they face.
    Keywords: Central bank research; Economic models; Financial stability; Financial system regulation and policies; Interest rates; Monetary Policy; Monetary policy framework
    JEL: E44 E52 E58 D8 G01
    Date: 2020–01
  10. By: Collins-Sowah, Peron A.; Henning, Christian H. C. A.
    Abstract: The subject of risk in agricultural production is very pertinent and touches on various aspects such as investments, food security, income levels of farmers, and market stability. Unmanaged, risks can have profound impacts on the agricultural sector and at the same time severely hamper long-term economic growth and poverty reduction efforts. Furthermore, risk management by farm households are multifarious with each having different cost and benefit implications. Using empirical data from a nationally representative farm household survey in Senegal, we evaluated the effect of different risk management strategies employed by farm households on agriculture income and dispersions around incomes. We achieve this by employing a Multinomial Endogenous Switching Regression model and a Moment-Based Approach. We find mix results of the impact of risk management on agriculture incomes. The use of risk mitigation and transfer significantly reduces agriculture incomes while risk coping strategies significantly increases agriculture incomes. Risk mitigation strategies were observed to be associated with opportunity costs relating to income loss and likely inefficient resource allocations. On the contrary, the reduced agricultural incomes observed with the use of risk transfer might be related moral hazard problems such that insurance policy holders do not take care or expend less effort in their production activities. We also find that risk management strategies significantly reduce dispersions around agriculture incomes with risk transfer producing the largest effect. Furthermore, the effect of risk transfer strategies on dispersions around agriculture incomes is reduced when combine with other strategies. For the other risk management strategies, we find that when used in combinations, the dispersion reduction effect is greatly enhanced.
    Keywords: Risk management,strategies,dispersion,multinomial,mitigation,transfer,coping
    Date: 2019
  11. By: Matteo, Pelagatti; Giacomo, Sbrana
    Abstract: This paper proposes tree main results that enable the estimation of high dimensional multivariate stochastic volatility models. The first result is the closed-form steady-state Kalman filter for the multivariate AR(1) plus noise model. The second result is an accelerated EM algorithm for parameters estimation. The third result is an estimator of the correlation of two elliptical random variables with time-varying variances that is consistent and asymptotically normal regardless of the variances evolution. Speed and precision of our methodology are evaluated in a simulation experiment. Finally, we implement our method and compare its performance with other approaches in a minimum variance portfolio composed by the constituents of the CAC40 and S&P100 indexes.
    Keywords: Riccati equation, EM algorithm, Kalman filter, Correlation estimation, Large covariance matrix, Multivariate stochastic volatility
    Date: 2020–01
  12. By: Francois Belletti; Davis King; James Lottes; Yi-Fan Chen; John Anderson
    Abstract: In a recent paper, we have demonstrated how the affinity between TPUs and multi-dimensional financial simulation resulted in fast Monte Carlo simulations that could be setup in a few lines of python Tensorflow code. We also presented a major benefit from writing high performance simulations in an automated differentiation language such as Tensorflow: a single line of code enabled us to estimate sensitivities, i.e. the rate of change in price of financial instrument with respect to another input such as the interest rate, the current price of the underlying, or volatility. Such sensitivities (otherwise known as the famous financial "Greeks") are fundamental for risk assessment and risk mitigation. In the present follow-up short paper, we extend the developments exposed in our previous work about the use of Tensor Processing Units and Tensorflow for TPUs.
    Date: 2020–02
  13. By: Klapper,Leora; Singer,Dorothe; Ansar,Saniya; Hess,Jake Richard
    Abstract: The ability to manage financial risk is especially important for people earning their living through agriculture. Many farmers only get paid once or twice a year, and households need to stretch their earnings across the year by saving or borrowing money. Moreover, agricultural production faces a variety of risks related to both production and markets because of their exposure to weather and disease shocks. Households engaged in agriculture may thus especially benefit from financial inclusion?access to and use of formal financial services. This paper explores the topic of financial risk management in agriculture?how adults who rely on growing crops or raising livestock as their household's main source of income manage financial risk and use financial services. The paper summarizes new data based on a nationally representative survey of about 15,000 adults in 15 lower-middle- and low-income Sub-Saharan African economies collected as part of the World Bank's Global Findex database. The majority of these adults reported suffering a bad harvest or significant livestock loss in the past five years, and most bear the entire financial risk of such a loss. Most adults in agricultural households lack the financial tools -- such as insurance, accounts, savings, and credit -- that could help them manage financial risks.
    Keywords: Livestock and Animal Husbandry,Crops and Crop Management Systems,Climate Change and Agriculture,Food Security,Financial Sector Policy
    Date: 2019–12–11

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