nep-rmg New Economics Papers
on Risk Management
Issue of 2018‒05‒28
twenty papers chosen by

  1. Predicting risk with risk measures : an empirical study By Marcel Bräutigam; Michel Dacorogna; Marie Kratz
  2. Estimating the Volatility of Asset Pricing Factors By Becker, Janis; Leschinski, Christian
  3. The Rise of Shadow Banking: Evidence from Capital Regulation By Irani, Rustom M; Iyer, Rajkamal; Meisenzahl, Ralf; Peydró, José Luis
  4. Forward-looking portfolio selection with multivariate non-Gaussian models and the Esscher transform By Michele Leonardo Bianchi; Gian Luca Tassinari
  5. Insurers as asset managers and systemic risk By Ellul, Andrew; Jotikasthira, Chotibhak; Kartasheva, Anastasia; Lundblad, Christian T.; Wagner, Wolf
  6. What Is the Impact of Successful Cyberattacks on Target Firms? By Kamiya, Shinichi; Kang, Jun-Koo; Kim, Jungmin; Milidonis, Andreas; Stulz, Rene M.
  7. Regulating the doom loop By Alogoskoufis, Spyros; Langfield, Sam
  8. Multifractal analysis of financial markets By Zhi-Qiang Jiang; Wen-Jie Xie; Wei-Xing Zhou; Didier Sornette
  9. Which portfolio is better? A discussion of several possible comparison criteria By Henryk Gzyl; Alfredo Rios
  10. Multiplex network analysis of the UK OTC derivatives market By Bardosci, Marco; Bianconi, Ginestra; Ferrara, Gerardo
  11. The strong Fatou property of risk measures By Shengzhong Chen; Niushan Gao; Foivos Xanthos
  12. Implications of Extreme Value Theory for stock market investments By Cristiana Tudor
  13. Do CEOs Make Their Own Luck? Relative Versus Absolute Performance Evaluation and Firm Risk By Wruck, Karen H.; Wu, YiLin
  14. Bitcoin Risk Modeling with Blockchain Graphs By Cuneyt Akcora; Matthew Dixon; Yulia Gel; Murat Kantarcioglu
  15. Network Sensitivity of Systemic Risk By Domenico Di Gangi; D. Ruggiero Lo Sardo; Valentina Macchiati; Tuan Pham Minh; Francesco Pinotti; Amanah Ramadiah; Mateusz Wilinski; Giulio Cimini
  16. Liquidity and its determinants By Basir, Yana
  17. Predicting Stock Market Movements in the United States: The Role of Presidential Approval Ratings By Rangan Gupta; Patrick Kanda; Mark E. Wohar
  18. Geographic Focus and Systematic Risk in REITs By Brigitte Frutig; Prashant Das
  19. Non-parametric Estimation of GARCH (2, 2) Volatility model: A new Algorithm By Cassim, Lucius
  20. Nonparametric Bayesian volatility learning under microstructure noise By Shota Gugushvili; Frank van der Meulen; Moritz Schauer; Peter Spreij

  1. By: Marcel Bräutigam (LabEx MME-DII - UCP - Université de Cergy Pontoise - Université Paris-Seine, ESSEC Business School - Essec Business School, LPSM UMR 8001 - Laboratoire de Probabilités, Statistique et Modélisation - UPMC - Université Pierre et Marie Curie - Paris 6 - UPD7 - Université Paris Diderot - Paris 7 - CNRS - Centre National de la Recherche Scientifique); Michel Dacorogna (SCOR SE - SCOR SE, DEAR Consulting); Marie Kratz (SID - Information Systems, Decision Sciences and Statistics Department - Essec Business School)
    Abstract: In this study we consider the risk estimation as a stochastic process based on the Sample Quantile Process (SQP) - which is a generalization of the Value-at-Risk calculated on a rolling sample. Using SQP's, we are able to show and quantify the pro-cyclicality of the current way nancial institutions measure their risk. Analysing 11 stock indices, we show that, if the past volatility is low, the historical computation of the risk measure underestimates the future risk, while in periods of high volatility, the risk measure overestimates the risk. Moreover, using a simple GARCH(1,1) model, we conclude that this pro-cyclical e ect is related to the clustering of volatility. We argue that this has important consequences for the regulation in times of crisis.
    Keywords: risk measure,sample quantile process,stochastic model,VaR,volatility
    Date: 2018–02–28
  2. By: Becker, Janis; Leschinski, Christian
    Abstract: Models based on factors such as size, value, or momentum are ubiquitous in asset pricing. Therefore, portfolio allocation and risk management require estimates of the volatility of these factors. While realized volatility has become a standard tool for liquid individual assets, this measure is not available for factor models, due to their construction from the CRSP data base that does not provide high frequency data and contains a large number of less liquid stocks. Here, we provide a statistical approach to estimate the volatility of these factors. The efficacy of this approach relative to the use of models based on squared returns is demonstrated for forecasts of the market volatility and a portfolio allocation strategy that is based on volatility timing.
    Keywords: Asset Pricing; Realized Volatility; Factor Models; Volatility Forecasting
    JEL: C58 G11 G12 G17 G32
    Date: 2018–05
  3. By: Irani, Rustom M; Iyer, Rajkamal; Meisenzahl, Ralf; Peydró, José Luis
    Abstract: We investigate the connections between bank capital regulation and the prevalence of lightly regulated nonbanks (shadow banks) in the U.S. corporate loan market. For identification, we exploit a supervisory credit register of syndicated loans, loan-time fixed-effects, and shocks to capital requirements arising from surprise features of the U.S. implementation of Basel III. We find that less-capitalized banks reduce loan retention and nonbanks step in, particularly among loans with higher capital requirements and at times when capital is scarce. This reallocation has important spillovers: loans funded by nonbanks with fragile liabilities experience greater sales and price volatility during the 2008 crisis.
    Keywords: Basel III; Distressed debt; Interactions between banks and nonbanks; Risk-based capital regulation; Shadow banks; Trading by banks
    JEL: G01 G21 G23 G28
    Date: 2018–05
  4. By: Michele Leonardo Bianchi; Gian Luca Tassinari
    Abstract: In this study we suggest a portfolio selection framework based on option-implied information and multivariate non-Gaussian models. The proposed models incorporate skewness, kurtosis and more complex dependence structures among stocks log-returns than the simple correlation matrix. The two models considered are a multivariate extension of the normal tempered stable (NTS) model and the generalized hyperbolic (GH) model, respectively, and the connection between the historical measure P and the risk-neutral measure Q is given by the Esscher transform. We consider an estimation method that simultaneously calibrate the time series of univariate log-returns and the univariate observed volatility smile. To calibrate the models, there is no need of liquid multivariate derivative quotes. The method is applied to fit a 50-dimensional series of stock returns, to evaluate widely known portfolio risk measures and to perform a portfolio selection analysis.
    Date: 2018–05
  5. By: Ellul, Andrew; Jotikasthira, Chotibhak; Kartasheva, Anastasia; Lundblad, Christian T.; Wagner, Wolf
    Abstract: Financial intermediaries often provide guarantees that resemble out-of-the-money put options, exposing them to tail risk. Using the U.S. life insurance industry as a laboratory, we present a model in which variable annuity (VA) guarantees and associated hedging operate within the regulatory capital framework to create incentives for insurers to overweight illiquid bonds (“reach-for-yield”). We then calibrate the model to insurer-level data, and show that the VAwriting insurers’ collective allocation to illiquid bonds exacerbates system-wide fire sales in the event of negative asset shocks, plausibly erasing up to 20-70% of insurers’ equity capital. JEL Classification: G11, G12, G14, G18, G22
    Keywords: financial stability, insurance companies, inter-connectedness, systemic risk
    Date: 2018–05
  6. By: Kamiya, Shinichi (Nanyang Technological University); Kang, Jun-Koo (Nanyang Technological University); Kim, Jungmin (Hong Kong Polytechnic University); Milidonis, Andreas (University of Cyprus); Stulz, Rene M. (Ohio State University)
    Abstract: We examine which firms are targets of successful cyberattacks and how they are affected. We find that cyberattacks are more likely to occur at larger and more visible firms, more highly valued firms, firms with more intangible assets, and firms with less board attention to risk management. These attacks affect firms adversely when consumer financial information is appropriated, but seem to have little impact otherwise. Attacks where consumer financial information is appropriated are associated with a significant negative stock market reaction, an increase in leverage following greater debt issuance, a deterioration in credit ratings, and an increase in cash flow volatility. These attacks also affect sales growth adversely for large firms and firms in retail industries, and there is evidence that they decrease investment in the short run. Affected firms respond to such attacks by cutting the CEO's bonus as a fraction of total compensation, by reducing the risk-taking incentives of management, and by taking actions to strengthen their risk management. The evidence is consistent with cyberattacks increasing boards' assessment of target firm risk exposures and decreasing their risk appetite.
    JEL: G14 G32 G34 G35
    Date: 2018–03
  7. By: Alogoskoufis, Spyros; Langfield, Sam
    Abstract: Euro area governments have committed to break the doom loop between bank risk and sovereign risk. But policymakers have not reached consensus on whether and how to reform the regulatory treatment of banks’ sovereign exposures. To inform policy discussions, this paper simulates portfolio reallocations by euro area banks under scenarios for regulatory reform. Simulations highlight a tension in regulatory design between concentration and credit risk. An area-wide low-risk asset—created by pooling and tranching cross-border portfolios of government debt securities— would resolve this tension by expanding the portfolio opportunity set. Banks could therefore reinvest into an asset that has both low concentration and low credit risk. JEL Classification: G01, G11, G21, G28
    Keywords: bank regulation, sovereign risk, systemic risk
    Date: 2018–05
  8. By: Zhi-Qiang Jiang (ECUST); Wen-Jie Xie (ECUST); Wei-Xing Zhou (ECUST); Didier Sornette (ETH Zurich)
    Abstract: Multifractality is ubiquitously observed in complex natural and socioeconomic systems. Multifractal analysis provides powerful tools to understand the complex nonlinear nature of time series in diverse fields. Inspired by its striking analogy with hydrodynamic turbulence, from which the idea of multifractality originated, multifractal analysis of financial markets has bloomed, forming one of the main directions of econophysics. We review the multifractal analysis methods and multifractal models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. We survey the cumulating evidence for the presence of multifractality in financial time series in different markets and at different time periods and discuss the sources of multifractality. The usefulness of multifractal analysis in quantifying market inefficiency, in supporting risk management and in developing other applications is presented. We finally discuss open problems and further directions of multifractal analysis.
    Date: 2018–05
  9. By: Henryk Gzyl; Alfredo Rios
    Abstract: During the last few years, there has been an interest in comparing simple or heuristic procedures for portfolio selection, such as the naive, equal weights, portfolio choice, against more "sophisticated" portfolio choices, and in explaining why, in some cases, the heuristic choice seems to outperform the sophisticated choice. We believe that some of these results may be due to the comparison criterion used. It is the purpose of this note to analyze some ways of comparing the performance of portfolios. We begin by analyzing each criterion proposed on the market line, in which there is only one random return. Several possible comparisons between optimal portfolios and the naive portfolio are possible and easy to establish. Afterwards, we study the case in which there is no risk free asset. In this way, we believe some basic theoretical questions regarding why some portfolios may seem to outperform others can be clarified.
    Date: 2018–05
  10. By: Bardosci, Marco (Bank of England); Bianconi, Ginestra (School of Mathematical Sciences, Queen Mary University of London); Ferrara, Gerardo (Bank of England)
    Abstract: In this paper, we analyse the network of exposures constructed by using the UK trade repository data for three different categories of contracts: interest rate, credit, and foreign exchange derivatives. We study how liquidity shocks related to variation margins propagate across the network and translate into payment deficiencies. A key finding of the paper is that, in extreme theoretical scenarios where liquidity buffers are small, a handful of institutions may experience significant spillover effects due to the directionality of their portfolios. Additionally, we show that a variant of a recently introduced centrality measure — Functional Multiplex PageRank — can be used as a proxy of the vulnerability of financial institutions, outperforming in this respect the commonly used eigenvector centrality.
    Keywords: Central counterparty (CCP); liquidity shock; multiplex networks; systemic risk; financial networks
    JEL: D85 G01 G17 L14
    Date: 2018–05–18
  11. By: Shengzhong Chen; Niushan Gao; Foivos Xanthos
    Abstract: In this paper, we explore several Fatou-type properties of risk measures. The paper continues to reveal that the strong Fatou property, which was introduced in [17], seems to be most suitable to ensure nice dual representations of risk measures. Our main result asserts that every quasiconvex law-invariant functional on a rearrangement invariant space $\mathcal{X}$ with the strong Fatou property is $\sigma(\mathcal{X},L^\infty)$ lower semicontinuous and that the converse is true on a wide range of rearrangement invariant spaces. We also study inf-convolutions of law-invariant or surplus-invariant risk measures that preserve the (strong) Fatou property.
    Date: 2018–05
  12. By: Cristiana Tudor (Bucharest University of Economic Studies)
    Abstract: Young post-communist Eastern European equity are more vulnerable to external and internal shocks than more mature, developed markets. Also, previous studies have attested that generally the distribution of stock prices exhibits deviations from Gaussianity, including the so-called ?heavy tails?. The higher degree of volatility encountered on these markets leads to the expectation that heavy-tailedness properties for stock returns will be more pronounced. In this research we attempt to confirm the fat/heavy tails hypothesis for a selection of 98 stocks listed on the Romanian Stock Market and we manage to accomplish this in a fairly large number of cases, both for the left and the right side of the distribution of daily logarithmic returns. Next, we try to establish whether ?booms? are more likely than ?crashes? for each company in the sample. Robustness checks via bootstraping enable the rejection or acceptance of this hypothesis with 95% confidence. The empirical results have important implications both for practioners via portfolio investment decisions and also for academic research i.e. value-at-risk or asset alocation models.
    Keywords: equity market, Eastern Europe, volatility, heavy-tails, portfolio decisions
    JEL: G15 G11 G14
    Date: 2018–04
  13. By: Wruck, Karen H. (Ohio State University); Wu, YiLin (National Taiwan University)
    Abstract: Influenced by their compensation plans, CEOs make their own luck through decisions that affect future firm risk. After adopting a relative performance evaluation (RPE) plan, total and idiosyncratic risk are higher, and the correlation between firm and industry performance is lower. The opposite is true for firms that adopt absolute performance evaluation (APE) plans. Plans including accounting-based performance metrics and/or cash payouts have weaker risk-related incentives. The higher idiosyncratic risk associated with RPE increases a firm's exposure to downside stock return risk and lowers credit quality. Our findings are economically consistent with observed differences in firms' financial and investment policies.
    JEL: D22 G12 G32 G34 J33 J41 O31
    Date: 2017–10
  14. By: Cuneyt Akcora; Matthew Dixon; Yulia Gel; Murat Kantarcioglu
    Abstract: A key challenge for Bitcoin cryptocurrency holders, such as startups using ICOs to raise funding, is managing their FX risk. Specifically, a misinformed decision to convert Bitcoin to fiat currency could, by itself, cost USD millions. In contrast to financial exchanges, Blockchain based crypto-currencies expose the entire transaction history to the public. By processing all transactions, we model the network with a high fidelity graph so that it is possible to characterize how the flow of information in the network evolves over time. We demonstrate how this data representation permits a new form of microstructure modeling - with the emphasis on the topological network structures to study the role of users, entities and their interactions in formation and dynamics of crypto-currency investment risk. In particular, we identify certain sub-graphs ('chainlets') that exhibit predictive influence on Bitcoin price and volatility, and characterize the types of chainlets that signify extreme losses.
    Date: 2018–05
  15. By: Domenico Di Gangi; D. Ruggiero Lo Sardo; Valentina Macchiati; Tuan Pham Minh; Francesco Pinotti; Amanah Ramadiah; Mateusz Wilinski; Giulio Cimini
    Abstract: The recent stream of literature of systemic risk in financial markets emphasized the key importance of considering the complex interconnections among financial institutions. Much efforts has been put to model the contagion dynamics of financial shocks, and to assess the resilience of specific financial markets---either using real data, reconstruction techniques or simple toy networks. Here we address the more general problem of how the shock propagation dynamics depends on the topological details of the underlying network. To this end, we consider different network topologies, all consistent with balance sheets information obtained from real data on financial institutions. In particular, we consider networks with varying density and mesoscale structures, and vary as well the details of the shock propagation dynamics. We show that the systemic risk properties of a financial network are extremely sensitive to its network features. Our results can thus aid in the design of regulatory policies to improve the robustness of financial markets.
    Date: 2018–05
  16. By: Basir, Yana
    Abstract: Liquidity risk management is really important in any organization to manage their liquidity. This study attempted to investigate the relationship between firm-specific and macroeconomic factors toward liquidity risk in Bajaj Auto company. This study is based on annual report of 5 years, the duration starting 2011-2015. The analysis show that firm specific factors and macroeconomic factors influence liquidity risk.
    Keywords: Liquidity risk, Corporate governance, Firm-specific factors, Macroeconomic factors
    JEL: Z00
    Date: 2018–05–20
  17. By: Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Patrick Kanda (Laboratoire THéorie Économique, Modélisation et Applications (THEMA), Université de Cergy-Pontoise, France); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha, Omaha, USA and School of Business and Economics, Loughborough University, Leicestershire, UK)
    Abstract: In this paper we analyze whether presidential approval ratings can predict the S&P500 returns over the monthly period of 1941:07 to 2018:04, using a dynamic conditional correlation multivariate generalized autoregressive conditional heteroscedasticity (DCC-MGARCH) model. Our results show that, standard linear Granger causality test fail to detect any evidence of predictability. However, the linear model is found to be misspecified due to structural breaks and nonlinearity, and hence, the result of no causality from presidential approval ratings to stock returns cannot be considered reliable. When we use the DCC-MGARCH model, which is robust to such misspecifications, in 69 percent of the sample period, approval ratings in fact do strongly predict the S&P500 stock return. Moreover, using the DCC-MGARCH model we find that presidential approval rating is also a strong predictor of the realized volatility of S&P500. Overall, our results highlight that presidential approval ratings is helpful in predicting stock return and volatility, when one accounts for nonlinearity and regime changes through a robust time-varying model.
    Keywords: US Presidential Approval Ratings, DCC-MGARCH, Stock Returns, Realized Volatility, S&P500
    JEL: C32 G10
    Date: 2018–05
  18. By: Brigitte Frutig; Prashant Das
    Abstract: We examine how the systematic risk of large commercial real estate owners is associated with geographic diversification. In particular, we analyze time-varying equity betas and geographic exposure of publicly traded pure-play lodging REITs. Contrary to popular expectation, we find that stock investors perceive smaller risk in geographic focus rather than diversification. Further, regional focus becomes insignificant in reducing the risk if the focus expands beyond two or three regions. The findings are robust to multiple measures of geographic diversification. Our study re-affirms the impact of geographic focus in the context of commercial real estate as a risk minimization strategy.
    Keywords: REIT; stock beta
    JEL: R3
    Date: 2017–07–01
  19. By: Cassim, Lucius
    Abstract: The main objective of this paper is to provide an estimation approach for non-parametric GARCH (2, 2) volatility model. Specifically the paper, by combining the aspects of multivariate adaptive regression splines(MARS) model estimation algorithm proposed by Chung (2012) and an algorithm proposed by Buhlman and McNeil(200), develops an algorithm for non-parametrically estimating GARCH (2,2) volatility model. Just like the MARS algorithm, the algorithm that is developed in this paper takes a logarithmic transformation as a preliminary analysis to examine a nonparametric volatility model. The algorithm however differs from the MARS algorithm by assuming that the innovations are i.d.d. The algorithm developed follows similar steps to that of Buhlman and McNeil (200) but starts by semi parametric estimation of the GARCH model and not parametric while relaxing the dependency assumption of the innovations to avoid exposing the estimation procedure to risk of inconsistency in the event of misspecification errors.
    Keywords: GARCH (2,2), MARS, Algorithm, Parametric, Semi parametric, Nonparametric
    JEL: C1 C14 C4
    Date: 2018–05–18
  20. By: Shota Gugushvili; Frank van der Meulen; Moritz Schauer; Peter Spreij
    Abstract: Aiming at financial applications, we study the problem of learning the volatility under market microstructure noise. Specifically, we consider noisy discrete time observations from a stochastic differential equation and develop a novel computational method to learn the diffusion coefficient of the equation. We take a nonparametric Bayesian approach, where we model the volatility function a priori as piecewise constant. Its prior is specified via the inverse Gamma Markov chain. Sampling from the posterior is accomplished by incorporating the Forward Filtering Backward Simulation algorithm in the Gibbs sampler. Good performance of the method is demonstrated on two representative synthetic data examples. Finally, we apply the method on the EUR/USD exchange rate dataset.
    Date: 2018–05

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