nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒02‒03
eighteen papers chosen by

  1. Modelling Risk on the Egyptian Stock Market: Evidence from a Markov-Regime Switching GARCH Process. By Ibrahim, Omar
  2. Macro-Financial Linkages in the High-Frequency Domain: The Effects of Uncertainty on Realized Volatility By Guglielmo Maria Caporale; Menelaos Karanasos; Stavroula Yfanti
  3. Systemic Risk in Networks with a Central Node By Hamed Amini; Damir Filipović; Andreea Minca
  4. Comparison of various risk measures for an optimal portfolio By Alev Meral
  5. Systemic Risk of the Consumer Credit Network across Financial Institutions By Hyun Hak Kim; Hosung Jung
  6. Optimal Dividends Paid in a Foreign Currency for a L\'evy Insurance Risk Model By Julia Eisenberg; Zbigniew Palmowski
  7. Neglecting Uncertainties Leads to Suboptimal Decisions About Home-Owners Flood Risk Management By Mahkameh Zarekarizi; Vivek Srikrishnan; Klaus Keller
  8. Banks' Credit Losses and Provisioning over the Business Cycle: Implications for IFRS 9 By Simona Malovana; Zaneta Tesarova
  9. Grouping of Contracts in Insurance using Neural Networks By Mark Kiermayer; Christian Wei{\ss}
  10. Oil price uncertainty as a predictor of stock market volatility By Vlastakis, Nikolaos; Triantafyllou, Athanasios; Kellard, Neil
  11. An Artificial Intelligence approach to Shadow Rating By Angela Rita Provenzano; Daniele Trifir\`o; Nicola Jean; Giacomo Le Pera; Maurizio Spadaccino; Luca Massaron; Claudio Nordio
  12. Clustering Approaches for Global Minimum Variance Portfolio By Jinwoo Park
  13. Path-dependent volatility models By Antoine Jacquier; Chloe Lacombe
  14. Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach By Bluwstein, Kristina; Buckmann, Marcus; Joseph, Andreas; Kang, Miao; Kapadia, Sujit; Simsek, Özgür
  15. Discovering Hidden Patterns in Loan Reimbursement By Niknamian, Sorush
  16. Extreme values, means, and inequality measurement By Walter Bossert; Conchita D fAmbrosio; Kohei Kamaga
  17. News-Driven Expectations and Volatility Clustering By Sabiou M. Inoua
  18. “Expected, Unexpected, Good and Bad Uncertainty" By Helena Chuliá; Jorge M. Uribe

  1. By: Ibrahim, Omar
    Abstract: This research aims at evaluating among market risk measures to equity exposures on the Egyptian stock market, while utilising a variety of parametric and non-parametric methods to estimating volatility dynamics. Historical Simulation, EWMA (RiskMetrics), GARCH, GJR-GARCH, and Markov-Regime switching GARCH models are empirically estimated. Value at Risk and Conditional Value at Risk measures are backtested in order to evaluate among the alternative models. Results indicate the superiority of asymmetric GARCH models when combined with a Markov-Regime switching process in quantifying market risk - as is evident from the results of the backtests - which have been performed in accordance with the current regulatory demands. Implications are important to regulators and practitioners.
    Keywords: Risk Management, Value at Risk, GARCH, Markov Chains
    JEL: C58
    Date: 2019–12
  2. By: Guglielmo Maria Caporale; Menelaos Karanasos; Stavroula Yfanti
    Abstract: This paper estimates a bivariate HEAVY system including daily and intra-daily volatility equations and its macro-augmented asymmetric power extension. It focuses on economic factors that exacerbate stock market volatility and represent major threats to financial stability. In particular, it extends the HEAVY framework with powers, leverage, and macro effects that improve its forecasting accuracy significantly. Higher uncertainty is found to increase the leverage and macro effects from credit and commodity markets on stock market realized volatility. Specifically, Economic Policy Uncertainty is shown to be one of the main drivers of US and UK financial volatility alongside global credit and commodity factors.
    Keywords: asymmetries, economic policy uncertainty, HEAVY model, high-frequency data, macro-financial linkages, power transformations, realized variance, risk management
    JEL: C22 C58 D80 E44 G01 G15
    Date: 2019
  3. By: Hamed Amini (J. Mack Robinson College of Business); Damir Filipović (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Andreea Minca (Cornell University)
    Abstract: We examine the effects on a financial network of clearing all contracts though a central node (CN) thereby transforming the original network into a star-shaped one. The CN is capitalized with external equity and a guaranty fund. We introduce a structural systemic risk measure that captures the shortfall of end users. We show that it is possible to simultaneously improve the expected surplus of the banks and the CN as well as decrease the shortfall of end users. We determine the CN's equity and guaranty fund policies as a Nash bargaining solution. We illustrate our findings on simulated Credit Default Swap networks compatible with aggregate market data.
    Keywords: Star-shaped Networks, Central Node, Market Design, Financial Network, Contagion, Systemic Risk, Credit Default Swap Markets
    JEL: C44 C54 C62 G01 G18 G32
    Date: 2020–01
  4. By: Alev Meral
    Abstract: In this paper, we search for optimal portfolio strategies in the presence of various risk measure that are common in financial applications. Particularly, we deal with the static optimization problem with respect to Value at Risk, Expected Loss and Expected Utility Loss measures. To do so, under the Black- Scholes model for the financial market, Martingale method is applied to give closed-form solutions for the optimal terminal wealths; then via representation problem the optimal portfolio strategies are achieved. We compare the performances of these measures on the terminal wealths and optimal strategies of such constrained investors. Finally, we present some numerical results to compare them in several respects to give light to further studies.
    Date: 2019–12
  5. By: Hyun Hak Kim (Department of Economics, Kookmin University); Hosung Jung (Economic Research Institute, Bank of Korea)
    Abstract: We investigate a network of financial institutions in Korea using the Korea Consumer Credit Panel (KCCP). The main contribution of this paper is that we construct the network of financial institution from the consumer credit level. We assume each consumer make a loan from multiple institutions so that those institutions share same risk from same consumer no matter of quality or type of loan. Then we construct the financial network between institutions and compute contagion index based on those multiple connection with a weight of default probability of individual borrowers. We found strong connection with banking institutions and credit card firms due to convenience in making small-amount loans with credit cards. However, when we give an weight with default probability to the linkage among institutions, connections of banking institution with savings bank, non-credit card finance corporation and merchant banking are stronger than others, while banking institution holds center position and has biggest amount of loans individually. Contagion index hit a peak in 2013Q1 and then fell rapidly, finally has been fluctuated in relatively low level from 2016 to 2017Q2. The result in our paper enables the authority to watch the systemic risk from consumer credit level with specific consumer type with their default probability.
    Keywords: Systemic risk, Financial network, Consumer credit, Financial stability
    JEL: C23 D14 G20 G21 G23
    Date: 2019–09–17
  6. By: Julia Eisenberg; Zbigniew Palmowski
    Abstract: This paper considers an optimal dividend distribution problem for an insurance company where the dividends are paid in a foreign currency. In the absence of dividend payments, our risk process follows a spectrally negative L\'evy process. We assume that the exchange rate is described by a an exponentially L\'evy process, possibly containing the same risk sources like the surplus of the insurance company under consideration. The control mechanism chooses the amount of dividend payments. The objective is to maximise the expected dividend payments received until the time of ruin and a penalty payment at the time of ruin, which is an increasing function of the size of the shortfall at ruin. A complete solution is presented to the corresponding stochastic control problem. Via the corresponding Hamilton--Jacobi--Bellman equation we find the necessary and sufficient conditions for optimality of a single dividend barrier strategy. A number of numerical examples illustrate the theoretical analysis.
    Date: 2020–01
  7. By: Mahkameh Zarekarizi; Vivek Srikrishnan; Klaus Keller
    Abstract: Homeowners around the world elevate houses to manage flood risks. Deciding how high to elevate the house poses a nontrivial decision problem. The U.S. Federal Emergency Management Agency (FEMA) recommends elevating a house to the Base Flood Elevation (the elevation of the 100-yr flood) plus a freeboard. This recommendation neglects many uncertainties. Here we use a multi-objective robust decision-making framework to analyze this decision in the face of deep uncertainties. We find strong interactions between the economic, engineering, and Earth science uncertainties, illustrating the need for an integrated analysis. We show that considering deep uncertainties surrounding flood hazards, the discount rate, the house lifetime, and the fragility increases the economically optimal house elevation to values well above the recommendation by FEMA. An improved decision-support for home-owners has the potential to drastically improve decisions and outcomes.
    Date: 2020–01
  8. By: Simona Malovana; Zaneta Tesarova
    Abstract: We examine the procyclicality of banks' credit losses and provisions in the Czech Republic using pre-2018 data and then discuss the implications of the findings for provisioning in stage 3 under IFRS 9. This is possible because the majority of banks seem to have aligned their accounting definitions of default with the regulatory definition prior to the implementation of IFRS 9. We find significant asymmetries in banks' behavior over the cycle. Firstly, provisioning procyclicality is strongest in the later contractionary phase and early recovery phase, while it is non-existent in the early contractionary phase. Secondly, banks with higher credit risk behave more procyclically than their peers. If this behavior persists under IFRS 9, it may lead to delayed transfer of exposures between stages and exaggerate cyclical fluctuations.
    Keywords: Credit losses, IFRS 9, procyclicality, provisions
    JEL: C22 E32 G21
    Date: 2019–12
  9. By: Mark Kiermayer; Christian Wei{\ss}
    Abstract: Despite the high importance of grouping in practice, there exists little research on the respective topic. The present work presents a complete framework for grouping and a novel method to optimize model points. Model points are used to substitute clusters of contracts in an insurance portfolio and thus yield a smaller, computationally less burdensome portfolio. This grouped portfolio is controlled to have similar characteristics as the original portfolio. We provide numerical results for term life insurance and defined contribution plans, which indicate the superiority of our approach compared to K-means clustering, a common baseline algorithm for grouping. Lastly, we show that the presented concept can optimize a fixed number of model points for the entire portfolio simultaneously. This eliminates the need for any pre-clustering of the portfolio, e.g. by K-means clustering, and therefore presents our method as an entirely new and independent methodology.
    Date: 2019–12
  10. By: Vlastakis, Nikolaos; Triantafyllou, Athanasios; Kellard, Neil
    Abstract: In this paper we empirically examine the impact of oil price uncertainty shocks on US stock market volatility. We define the oil price uncertainty shock as the unanticipated component of oil price fluctuations. We find that our oil price uncertainty factor is the most significant predictor of stock market volatility when compared with various observable oil price and volatility measures commonly used in the literature. Moreover, we find that oil price uncertainty is a common volatility forecasting factor of S&P500 constituents, and it outperforms lagged stock market volatility and the VIX when forecasting volatility for medium and long-term forecasting horizons. Interestingly, when forecasting the volatility of S&P500 constituents, we find that the highest predictive power of oil price uncertainty is for the stocks which belong to the financial sector. Overall, our findings show that financial stability is significantly damaged when the degree of oil price unpredictability rises, while it is relatively immune to observable fluctuations in the oil market.
    Keywords: Stock market, Oil, Uncertainty, Realized Variance, Volatility
    Date: 2020–01–22
  11. By: Angela Rita Provenzano; Daniele Trifir\`o; Nicola Jean; Giacomo Le Pera; Maurizio Spadaccino; Luca Massaron; Claudio Nordio
    Abstract: We analyse the effectiveness of modern deep learning techniques in predicting credit ratings over a universe of thousands of global corporate entities obligations when compared to most popular, traditional machine-learning approaches such as linear models and tree-based classifiers. Our results show a adequate accuracy over different rating classes when applying categorical embeddings to artificial neural networks (ANN) architectures.
    Date: 2019–12
  12. By: Jinwoo Park
    Abstract: The only input to attain the portfolio weights of global minimum variance portfolio (GMVP) is the covariance matrix of returns of assets being considered for investment. Since the population covariance matrix is not known, investors use historical data to estimate it. Even though sample covariance matrix is an unbiased estimator of the population covariance matrix, it includes a great amount of estimation error especially when the number of observed data is not much bigger than number of assets. As it is difficult to estimate the covariance matrix with high dimensionality all at once, clustering stocks is proposed to come up with covariance matrix in two steps: firstly, within a cluster and secondly, between clusters. It decreases the estimation error by reducing the number of features in the data matrix. The motivation of this dissertation is that the estimation error can still remain high even after clustering, if a large amount of stocks is clustered together in a single group. This research proposes to utilize a bounded clustering method in order to limit the maximum cluster size. The result of experiments shows that not only the gap between in-sample volatility and out-of-sample volatility decreases, but also the out-of-sample volatility gets reduced. It implies that we need a bounded clustering algorithm so that maximum clustering size can be precisely controlled to find the best portfolio performance.
    Date: 2020–01
  13. By: Antoine Jacquier; Chloe Lacombe
    Abstract: We provide a thorough analysis of the path-dependent volatility model introduced by Guyon, proving existence and uniqueness of a strong solution, characterising its behaviour at boundary points, and deriving large deviations estimates. We further develop a numerical algorithm in order to jointly calibrate SP500 and VIX market data.
    Date: 2020–01
  14. By: Bluwstein, Kristina (Bank of England); Buckmann, Marcus (Bank of England); Joseph, Andreas (Bank of England and King’s College London); Kang, Miao (Bank of England); Kapadia, Sujit (European Central Bank); Simsek, Özgür (University of Bath)
    Abstract: We develop early warning models for financial crisis prediction using machine learning techniques on macrofinancial data for 17 countries over 1870–2016. Machine learning models mostly outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of our machine learning models using a novel framework based on Shapley values, uncovering non-linear relationships between the predictors and crisis risk. Throughout, the most important predictors are credit growth and the slope of the yield curve, both domestically and globally. A flat or inverted yield curve is of most concern when nominal interest rates are low and credit growth is high.
    Keywords: Machine learning; financial crisis; financial stability; credit growth; yield curve; Shapley values; out-of-sample prediction
    JEL: C40 C53 E44 F30 G01
    Date: 2020–01–03
  15. By: Niknamian, Sorush
    Abstract: Loans are the major resources at banks. However, in some cases the cost that they incur to banks soar and finally makes them detrimental, as a result of irregular or delaying reimbursement or not paying at all. Due to the low wage rates in Iranian banks and the Central Bank of Iran (CBI) regulations in determining interest rates for deposits and loans, banks are becoming more and more dependent to the loans and their related profits. Therefore, banks have to look for customers with low risk for punctual payment. According to defect loan reimbursement in past years, banks have to specify severe prerequisites and limited contracts in granting loans to their customers. Contravening banking regulations and lack of consistent customers' accreditation banks are getting into heavy losses. Evaluating situations of the granted loans in EN Bank of Iran during a six-month period, based upon the profiles and loans history and the trend of payments useful patterns are discovered; designing a practical model of loan payment in Iran, the future default or failure to regain the granted loans is predicted and sensible methods of granting loans in Iran are developed. In order to extract hidden patterns in data statistical methods and data mining tools with focus on decision tree techniques are applied.
    Date: 2019–12–31
  16. By: Walter Bossert; Conchita D fAmbrosio; Kohei Kamaga
    Abstract: We examine some ordinal measures of inequality that are familiar from the literature. These measures have a quite simple structure in that their values are determined by combinations of specific summary statistics such as the extreme values and the arithmetic mean of a distribution. In spite of their common appearance, there seem to be no axiomatizations available so far, and this paper is intended to fill that gap. In particular, we consider the absolute and relative variants of the range; the max-mean and the mean-min orderings; and quantile-based measures. In addition, we provide some empirical observations that are intended to illustrate that, although these orderings are straightforward to define, some of them display a surprisingly high correlation with alternative (more complex) measures. Journal of Economic Literature Classification Nos.: H24, I31.
    Date: 2020–01
  17. By: Sabiou M. Inoua (Chapman University)
    Abstract: Financial volatility obeys two well-established empirical properties: it is fattailed (power-law distributed) and it tends to be clustered in time. Many interesting models have been proposed to account for these regularities, notably agent-based computational models, which typically invoke complicated mechanisms, however. It can be shown that trend-following speculation generates the power law in an intrinsic way. But this model cannot exaplain clustered volatility. This paper extends the model and offers a simple explanation for clustered volatility: the impact of exogenous news on traders’ expectations. Owing to the famous no-trade results, rational expectations, the dominant model of news-driven expectations, is hard to reconcile with the incessant high-frequency trading behind the volatility clustering. The simplest alternative model of news-driven expectations is to assume that traders have prior views about the market (an asset’s future price change or its present value) and then modify their views with the advent of a news. This simple news-driven random walk of traders’ expectations explains volatility clustering in a generic way. Liquidity plays a crucial role in this dynamics of volatility, which is emphasized in a dicussions section.
    Keywords: Volatility Clustering; Power Law; Trend Following; Efficient Market Hypothesis; Liquidity
    Date: 2019
  18. By: Helena Chuliá (Department of Econometrics & Riskcenter-IREA, Universidad de Barcelona, (Barcelona, Spain)); Jorge M. Uribe (Faculty of Economics and Business Studies, Open University of Catalonia)
    Abstract: By distinguishing between four general notions of uncertainty (good-expected, bad-expected, good-unexpected, bad-unexpected) within a common and simple framework, we show that it is bad-unexpected uncertainty shocks that generate a negative reaction of macroeconomic variables (such as investment and consumption), and asset prices. Other notions of uncertainty might produce even positive responses in the macroeconomy. We also show that small uncertainty shocks might have larger impacts on economic activity and financial markets than bigger shocks between one to three years after its realization. We explore the time and magnitude of uncertainty shocks by means of a novel distributed lag nonlinear model. Our results help to elucidate the real and complex nature of uncertainty, which can be both a backward or forward-looking expected or unexpected event, with markedly different consequences for the economy. They have implications for policy making, asset pricing and risk management.
    Keywords: Uncertainty, Economic activity, Asset prices JEL classification: C58, E20, E44, G10
    Date: 2019–11

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