nep-rmg New Economics Papers
on Risk Management
Issue of 2019‒08‒12
23 papers chosen by

  1. Value at Risk and Expected Shortfall under General Semi-parametric GARCH models By Xuehai Zhang
  2. A simulation of the insurance industry: The problem of risk model homogeneity By Heinrich, Torsten; Sabuco, Juan; Farmer, J. Doyne
  3. The Greenium matters: evidence on the pricing of climate risk By Alessi, Lucia; Ossola, Elisa; Panzica, Roberto
  4. Banking, Capital Regulation, Risk and Dynamics By Larsson, Bo; Wijkander, Hans
  5. System-wide stress simulation By Aikman, David; Chichkanov, Pavel; Douglas, Graeme; Georgiev, Yordan; Howat, James; King, Benjamin
  6. The anatomy of the euro area interest rate swap market By Fontana, Silvia Dalla; Holz auf der Heide, Marco; Pelizzon, Loriana; Scheicher, Martin
  7. Observation-driven Models for Realized Variances and Overnight Returns By Anne Opschoor; André Lucas
  8. Rating firms and sensitivity analysis By Magni, Carlo Alberto; Malagoli, Stefano; Marchioni, Andrea; Mastroleo, Giovanni
  9. Time-varying tail behavior for realized kernels By Anne Opschoor; André Lucas
  10. Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility By Michael Weylandt; Yu Han; Katherine B. Ensor
  11. Evaluating the Effectiveness of Common Technical Trading Models By Joseph Attia
  12. The Gains of Ignoring Risk: Insurance with Better Informed Principals. By Laura Abrardi; Luca Colombo; Piero Tedeschi
  13. Variance Risk Premium Components and International Stock Return Predictability By Juan M. Londono; Nancy R. Xu
  14. Fiscal Risk and Financial Fragility By Thiago Christiano Silva; Solange Maria Guerra; Benjamin Miranda Tabak
  15. Poissonian occupation times of spectrally negative L\'evy processes with applications By Mohamed Amine Lkabous
  16. Climate Disaster Risks – Empirics and a Multi-Phase Dynamic Model By Stefan Mittnik; Willi Semmler; Alexander Haider
  17. US Equity Tail Risk and Currency Risk Premia By Zhenzhen Fan; Juan M. Londono; Xiao Xiao
  18. An Early Warning System for banking crises: From regression-based analysis to machine learning techniques By Elizabeth Jane Casabianca; Michele Catalano; Lorenzo Forni; Elena Giarda; Simone Passeri
  19. Risk-Taking Spillovers of U.S. Monetary Policy in the Global Market for U.S. Dollar Corporate Loans By Seung Jung Lee; Lucy Qian Liu; Viktors Stebunovs
  20. Forecasting High-Risk Composite CAMELS Ratings By Lewis Gaul; Jonathan Jones; Pinar Uysal
  21. Happy to Take Some Risk: Investigating the Dependence of Risk Preferences on Mood Using Biometric Data By Kassas, Bachir; Palma, Marco A.; Porter, Maria
  22. Insurers’ investment strategies: pro- or countercyclical? By Fache Rousová, Linda; Giuzio, Margherita
  23. New Essentials of Economic Theory By Olkhov, Victor

  1. By: Xuehai Zhang (Paderborn University)
    Abstract: Risk management has been emphasized by financial institutions and the Basel Com- mittee on Banking Supervision (BCBS). The core issue in risk management is the mea- surement of the risks. Value at Risk (VaR) and Expected Shortfall (ES) are the widely used tools in quantitative risk management. Due to the ineptitude of VaR on tail risk performances, ES is recommended as the financial risk management metrics by BCBS. In this paper, we generate general SemiGARCH class models with a time-varying scale function. GARCH class models, based on the conditional t-distribution, are parametric extensions. Besides, backtesting with the semiparametric approach is also discussed. Fol- lowing Basel III, the trac light tests are applied in the model validation. Finally, we propose the loss functions with the views from regulators and firms, combing a power transformation in the model selection and it is shown that semiparametric models are a necessary option in practical financial risk management.
    Date: 2019–08
  2. By: Heinrich, Torsten; Sabuco, Juan; Farmer, J. Doyne
    Abstract: We develop an agent-based simulation of the catastrophe insurance and reinsurance industry and use it to study the problem of risk model homogeneity. The model simulates the balance sheets of insurance firms, who collect premiums from clients in return for ensuring them against intermittent, heavy-tailed risks. Firms manage their capital and pay dividends to their investors, and use either reinsurance contracts or cat bonds to hedge their tail risk. The model generates plausible time series of profits and losses and recovers stylized facts, such as the insurance cycle and the emergence of asymmetric, long tailed firm size distributions. We use the model to investigate the problem of risk model homogeneity. Under Solvency II, insurance companies are required to use only certified risk models. This has led to a situation in which only a few firms provide risk models, creating a systemic fragility to the errors in these models. We demonstrate that using too few models increases the risk of nonpayment and default while lowering profits for the industry as a whole. The presence of the reinsurance industry ameliorates the problem but does not remove it. Our results suggest that it would be valuable for regulators to incentivize model diversity. The framework we develop here provides a first step toward a simulation model of the insurance industry for testing policies and strategies for better capital management.
    Keywords: insurance; systemic risk; reinsurance; agent-based simulation; risk modeling
    JEL: C63 G22 G28
    Date: 2019–07–12
  3. By: Alessi, Lucia (European Commission); Ossola, Elisa (European Commission); Panzica, Roberto (European Commission)
    Abstract: This study provides evidence on the existence of a negative Greenium, i.e. a green risk premium, based on European individual stock returns and portfolios. By defining a green factor which is priced by the market, we offer a tool to assess a portfolio exposure to climate risk and hedge against it. We estimate that even in a rather benign scenario, there would be losses at the global level, including for European large banks, should they fail to price the Greenium. By halving the exposure to carbon-intensive sectors, losses would be reduced by 30%. These results call for the introduction of carbon stress tests for systemically important institutions
    Keywords: climate risk, ESG disclosure, factor models, asset pricing, stress test
    JEL: G01 G11 G12 Q01
    Date: 2019–07
  4. By: Larsson, Bo (erfConsulting AB); Wijkander, Hans (Dept. of Economics, Stockholm University)
    Abstract: Effects from risk, bankruptcies, and capital regulation of banks is explored in a dynamic stochastic equilibrium model where banks have two controls, dividends and level of risktaking. Unregulated value-maximizing banks, balance current profit against cost of risk. Banks with capitalization below desired level chose a lower level of risk than well-capitalized banks, but their capital adequacy ratios are yet lower. Binding regulation reduces risk-taking and instantaneous risk of bankruptcy but in the process also reduce endogenous growth of bank capital. This leads to an increased risk of bankruptcy that stems from the longer time banks spend poorly capitalized after large negative shocks due to the capital regulation.
    Keywords: Banking; Dynamic Banking; Banking regulation; Capital adequacy; Dividends; Incentive structure
    JEL: C61 G21 G22
    Date: 2019–06–23
  5. By: Aikman, David (Bank of England); Chichkanov, Pavel (Bank of England); Douglas, Graeme (MKP Capital Europe LLP); Georgiev, Yordan (Standard Chartered Bank); Howat, James (Bank of England); King, Benjamin (Bank of England)
    Abstract: We present a model for assessing how the UK’s system of market-based finance — an increasingly important source of credit to the real economy since the financial crisis — might behave under stress. The core of this model is a set of representative agents, which correspond to key sectors of the UK’s financial system. These agents interact in asset, funding (repo), and derivatives markets and face a range of solvency and liquidity constraints on their behaviour. Our model generates ‘tipping points’ such that, if shocks are large, or if headroom relative to constraints is small, lower asset prices can cause solvency/liquidity constraints to bind, resulting in forced deleveraging and large endogenous illiquidity premia. We illustrate such an outcome via a stress scenario in which a deteriorating corporate sector outlook coincides with tighter leverage limits at key intermediaries. Our findings highlight the key role played by broker-dealers, commercial banks, investment funds and life insurers in shaping these dynamics.
    Keywords: Systemic risk; market-based finance; fire sales; stress testing.
    JEL: G18 G21 G22 G23
    Date: 2019–07–12
  6. By: Fontana, Silvia Dalla; Holz auf der Heide, Marco; Pelizzon, Loriana; Scheicher, Martin
    Abstract: Using a novel regulatory dataset of fully identified derivatives transactions, this paper provides the first comprehensive analysis of the structure of the euro area interest rate swap (IRS) market after the start of the mandatory clearing obligation. Our dataset contains 1.7 million bilateral IRS transactions of banks and non-banks. Our key results are as follows: 1) The euro area IRS market is highly standardised and concentrated around the group of the G16 Dealers but also around a significant group of core "intermediaries"(and major CCPs). 2) Banks are active in all segments of the IRS euro market, whereas non-banks are often specialised. 3) When using relative net exposures as a proxy for the "flow of risk" in the IRS market, we find that risk absorption takes place in the core as well as the periphery of the network but in absolute terms the risk absorption is largely at the core. 4) Among the Basel III capital and liquidity ratios, the leverage ratio plays a key role in determining a bank's IRS trading activity.
    Keywords: derivatives,network analysis,interest rate risk,banking,risk management,hedging
    JEL: G21 E43 E44
    Date: 2019
  7. By: Anne Opschoor (Vrije Universiteit Amsterdam); André Lucas (Vrije Universiteit Amsterdam)
    Abstract: We present a new model to decompose total daily return volatility into a filtered (high-frequency based) open-to-close volatility and a time-varying scaling factor. We use score-driven dynamics based on fat-tailed distributions to limit the impact of incidental large observations. Applying our new model to 100 stocks of the S&P 500 during the period 2001-2014 and evaluating (in-sample and out-of-sample) in terms of Value-at-Risk and Expected Shortfall, we find our model outperforms alternatives like the HEAVY model that uses close-to-close returns and realized variances, and models treating close-to-open en open-to-close returns as separate processes. Results also indicate that the ratio between total and open-to-close volatility changes substantially through time, especially for financial stocks.
    Keywords: overnight volatility, realized variance, F distribution, score-driven dynamics
    JEL: C32 C58
    Date: 2019–07–31
  8. By: Magni, Carlo Alberto; Malagoli, Stefano; Marchioni, Andrea; Mastroleo, Giovanni
    Abstract: This paper introduces a model for rating a firm's default risk based on fuzzy logic and expert system and an associated model of sensitivity analysis (SA) for managerial purposes. The rating model automatically replicates the evaluation process of default risk performed by human experts. It makes use of a modular approach based on rules blocks and conditional implications. The SA model investigates the change in the firm's default risk under changes in the model inputs and employs recent results in the engineering literature of Sensitivity Analysis. In particular, it (i) allows the decomposition of the historical variation of default risk, (ii) identifies the most relevant parameters for the risk variation, and (iii) suggests managerial actions to be undertaken for improving the firm's rating.
    Keywords: Credit rating, default risk, fuzzy logic, fuzzy expert system, sensitivity analysis.
    JEL: C63 C67 G32
    Date: 2019–07–21
  9. By: Anne Opschoor (Vrije Universiteit Amsterdam); André Lucas (Vrije Universiteit Amsterdam)
    Abstract: We propose a new score-driven model to capture the time-varying volatility and tail behavior of realized kernels. We assume realized kernels follow an F distribution with two time-varying degrees-of-freedom parameters, accounting for the Vol-of-Vol and the tail shape of the realized kernel distribution. The resulting score-driven dynamics imply that the influence of large (outlying) realized kernels on future volatilities and tail-shapes is mitigated. We apply our model to 30 stocks from the S&P 500 index over the period 2001-2014. The results show that tail shapes vary over time, even after correcting for the time-varying mean and Vol-of-Vol of the realized kernels. The model outperforms a number of recent competitors, both in-sample and out-of-sample. In particular, accounting for time-varying tail shapes matters for both density forecasts and forecasts of volatility risk quantiles.
    Keywords: realized kernel, heavy tails, F distribution, time-varying shape-parameter, Vol-of-Vol, score-driven dynamics
    JEL: C32 C58
    Date: 2019–07–31
  10. By: Michael Weylandt; Yu Han; Katherine B. Ensor
    Abstract: Financial markets for Liquified Natural Gas (LNG) are an important and rapidly-growing segment of commodities markets. Like other commodities markets, there is an inherent spatial structure to LNG markets, with different price dynamics for different points of delivery hubs. Certain hubs support highly liquid markets, allowing efficient and robust price discovery, while others are highly illiquid, limiting the effectiveness of standard risk management techniques. We propose a joint modeling strategy, which uses high-frequency information from thickly-traded hubs to improve volatility estimation and risk management at thinly traded hubs. The resulting model has superior in- and out-of-sample predictive performance, particularly for several commonly used risk management metrics, demonstrating that joint modeling is indeed possible and useful. To improve estimation, a Bayesian estimation strategy is employed and data-driven weakly informative priors are suggested. Our model is robust to sparse data and can be effectively used in any market with similar irregular patterns of data availability.
    Date: 2019–07
  11. By: Joseph Attia
    Abstract: How effective are the most common trading models? The answer may help investors realize upsides to using each model, act as a segue for investors into more complex financial analysis and machine learning, and to increase financial literacy amongst students. Creating original versions of popular models, like linear regression, K-Nearest Neighbor, and moving average crossovers, we can test how each model performs on the most popular stocks and largest indexes. With the results for each, we can compare the models, and understand which model reliably increases performance. The trials showed that while all three models reduced losses on stocks with strong overall downward trends, the two machine learning models did not work as well to increase profits. Moving averages crossovers outperformed a continuous investment every time, although did result in a more volatile investment as well. Furthermore, once finished creating the program that implements moving average crossover, what are the optimal periods to use? A massive test consisting of 169,880 trials, showed the best periods to use to increase investment performance (5,10) and to decrease volatility (33,44). In addition, the data showed numerous trends such as a smaller short SMA period is accompanied by higher performance. Plotting volatility against performance shows that the high risk, high reward saying holds true and shows that for investments, as the volatility increases so does its performance.
    Date: 2019–07
  12. By: Laura Abrardi; Luca Colombo (Università Cattolica del Sacro Cuore; Dipartimento di Economia e Finanza, Università Cattolica del Sacro Cuore); Piero Tedeschi
    Abstract: We study a competitive insurance market in which insurers have an imperfect informative advantage over policyholders. We show that the presence of insurers privately and heterogeneously informed about risk can explain the concentration levels, the persistent profitability and the pooling of risk observed in some insurance markets. Furthermore, we find that a lower market concentration may entail an increase in insurance premia
    Keywords: Insurance markets, Asymmetric information, Risk assessment, Market concentration.
    JEL: D43 D82 G22
    Date: 2019–07
  13. By: Juan M. Londono; Nancy R. Xu
    Keywords: Variance risk premium, downside variance risk premium, international stock markets, asymmetric state variables, stock return predictability
    JEL: F36 G12 G13 G15
    Date: 2019–07–19
  14. By: Thiago Christiano Silva; Solange Maria Guerra; Benjamin Miranda Tabak
    Abstract: This paper proposes a new methodology to evaluate the importance of fiscal risk to financial stability. We first develop a novel method to estimate the probability of default of public entities, which takes into account a strict legal framework is mandatory for governments. Using options theory, we model the volatile public revenues using country macroeconomic expectations while allowing expenses, which cannot be easily reduced, to grow with inflation. Next, we compute the expected losses due to fiscal risk using a combination of the probability of default with potential losses that the public sector would impose on the economy using a complex network model. Motivated by the crisis on Brazilian states after 2015, we use Brazil to illustrate the usefulness of our model. We estimate the probability of default of states using legal restrictions on consolidated debt and personnel expenses. While most states are struggling to comply with limits on personnel expenses, the richest states have trouble to comply with limits on the consolidated debt. Using a network model that embeds counterparty and funding risks to estimate losses, we find state-owned banks are most likely to be affected if states default on bank credit. Financial contagion is small mostly because the banks that are more exposed to the public sector are highly capitalized.
    Date: 2019–07
  15. By: Mohamed Amine Lkabous
    Abstract: In this paper, we introduce the concept of \emph{Poissonian occupation times} below level $0$ of spectrally negative L\'evy processes. In this case, occupation time is accumulated only when the process is observed to be negative at arrival epochs of an independent Poisson process. Our results extend some well known continuously observed quantities involving occupation times of spectrally negative L\'evy processes. As an application, we establish a link between Poissonian occupation times and insurance risk models with Parisian implementation delays.
    Date: 2019–07
  16. By: Stefan Mittnik; Willi Semmler; Alexander Haider
    Abstract: Recent research in financial economics has shown that rare large disasters have the potential to disrupt financial sectors via the destruction of capital stocks and jumps in risk premia. These disruptions often entail negative feedback e?ects on the macroecon-omy. Research on disaster risks has also actively been pursued in the macroeconomic models of climate change. Our paper uses insights from the former work to study disaster risks in the macroeconomics of climate change and to spell out policy needs. Empirically the link between carbon dioxide emission and the frequency of climate re-lated disaster is investigated using cross-sectional and panel data. The modeling part then uses a multi-phase dynamic macro model to explore this causal nexus and the e?ects of rare large disasters resulting in capital losses and rising risk premia. Our proposed multi-phase dynamic model, incorporating climate-related disaster shocks and their aftermath as one phase, is suitable for studying mitigation and adaptation policies.
    Date: 2019–07–11
  17. By: Zhenzhen Fan; Juan M. Londono; Xiao Xiao
    Date: 2019–07–08
  18. By: Elizabeth Jane Casabianca (Prometeia Associazione per le Previsioni Econometriche, and DiSeS, Polytechnic University of Marche); Michele Catalano (Prometeia Associazione per le Previsioni Econometriche); Lorenzo Forni (Prometeia Associazione per le Previsioni Econometriche, and DSEA, University of Padua); Elena Giarda (Prometeia Associazione per le Previsioni Econometriche, and Cefin, University of Modena and Reggio Emilia); Simone Passeri (Prometeia Associazione per le Previsioni Econometriche)
    Abstract: Ten years after the outbreak of the 2007-2008 crisis, renewed attention is directed to money and credit fluctuations, financial crises and policy responses. By using an integrated dataset that includes 100 countries (advanced and emerging) spanning from 1970 to 2017, we propose an Early Warning System (EWS) to predict the build-up of systemic banking crises. The paper aims at (i) identifying the macroeconomic drivers of banking crises, (ii) going beyond the use of traditional discrete choice models by applying supervised machine learning (ML) and (iii) assessing the degree of countries’ exposure to systemic risks by means of predicted probabilities. Our results show that ML algorithms can have a better predictive performance than the logit models. All models deliver increasing predicted probabilities in the last years of the sample for the advanced countries, warning against the possible build-up of pre-crisis macroeconomic imbalances.
    Keywords: banking crises, EWS, machine learning, decision trees, AdaBoost
    JEL: C40 G01 C25 E44 G21
    Date: 2019–08
  19. By: Seung Jung Lee; Lucy Qian Liu; Viktors Stebunovs
    Date: 2019–07–23
  20. By: Lewis Gaul; Jonathan Jones; Pinar Uysal
    Keywords: Bank supervision and regulation, early warning models, CAMELS ratings, machine learning
    JEL: G21 G28 C53
    Date: 2019–07–23
  21. By: Kassas, Bachir; Palma, Marco A.; Porter, Maria
    Keywords: Research and Development/Tech Change/Emerging Technologies
    Date: 2019–06–25
  22. By: Fache Rousová, Linda; Giuzio, Margherita
    Abstract: Traditionally, insurers are seen as stabilisers of financial markets that act countercyclically by buying assets whose price falls. Recent studies challenge this view by providing empirical evidence of procyclicality. This paper sheds new light on the underlying reasons for these opposing views. Our model predicts procyclicality when prices fall due to increasing risk premia, and countercyclicality in response to rises in the risk-free rate. Using granular data on insurers’ government bond holdings, we validate these predictions empirically. Our findings contribute to the current policy discussion on macroprudential measures beyond banking. JEL Classification: G01, G11, G12, G22, G23
    Keywords: cyclicality, financial stability, insurance companies, portfolio allocation, sovereign debt crisis
    Date: 2019–07
  23. By: Olkhov, Victor
    Abstract: This paper develops economic theory tools and framework free from general equilibrium assumptions. We describe macroeconomics as system of economic agents under action risks. Economic and financial variables of agents, their expectations and transactions between agents define macroeconomic variables. Agents variables depend on transactions between agents and transactions are performed under agents expectations. Agents expectations are formed by economic variables, transactions, expectations of other agents, other factors that impact macroeconomic evolution. We use risk ratings of agents as their coordinates on economic space and approximate description of economic and financial variables, transactions and expectations of numerous separate agents by description of variables, transactions and expectations of aggregated agents as density functions on economic space. Motion of separate agents on economic space due to change of agents risk rating induce economic flows of variables, transactions and expectations and we describe their impact on economic evolution. We apply our model equations to description of business cycles, model wave propagation for disturbances of economic variables and transactions, model asset price fluctuations and argue hidden complexities of classical Black-Scholes-Merton option pricing.
    Keywords: economic theory; risk ratings; economic space; economic flows; density functions
    JEL: C00 C18 E30 E32 G00 G12 G17
    Date: 2019–07–12

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