nep-rmg New Economics Papers
on Risk Management
Issue of 2019‒06‒17
eighteen papers chosen by

  1. Portfolio diversification based on ratios of risk measures By Mathias Barkhagen; Brian Fleming; Sergio Garcia Quiles; Jacek Gondzio; Jens Kroeske; Sotirios Sabanis; Arne Staal
  2. (In)Stability for the Blockchain: Deleveraging Spirals and Stablecoin Attacks By Ariah Klages-Mundt; Andreea Minca
  3. The calculation of Solvency Capital Requirement using Copulas By Pellecchia, Marco; Perciaccante, Giovambattista
  4. Stress Testing Network Reconstruction via Graphical Causal Mode By Helder Rojas; David Dias
  5. Understanding Distributional Ambiguity via Non-robust Chance Constraint By Qi Wu; Shumin Ma; Cheuk Hang Leung; Wei Liu
  6. Generalized Expected Discounted Penalty Function at General Drawdown for L\'{e}vy Risk Processes By Wenyuan Wang; Ping Chen; Shuanming Li
  7. Neural Learning of Online Consumer Credit Risk By Di Wang; Qi Wu; Wen Zhang
  8. Non-performing loans, governance indicators and systemic liquidity risk: evidence from Greece By Dimitrios Anastasiou; Zacharias Bragoudakis; Ioannis Malandrakis
  9. Fair Pricing of Variable Annuities with Guarantees under the Benchmark Approach By Jin Sun; Kevin Fergusson; Eckhard Platen; Pavel V. Shevchenko
  10. Memory that Drives! New Insights into Forecasting Performance of Stock Prices from SEMIFARMA-AEGAS Model. By Mohamed Chikhi; Claude Diebolt; Tapas Mishra
  11. Optimal Reinsurance and Investment Strategies under Mean-Variance Criteria: Partial and Full Information By Shihao Zhu; Jingtao Shi
  12. Dependent Microstructure Noise and Integrated Volatility: Estimation from High-Frequency Data By Li, Z. M.; Laeven, R. J. A.; Vellekoop, M. H.
  13. Can ETFs contribute to systemic risk? By Pagano, Marco; Sánchez Serrano, Antonio; Zechner, Jozef
  14. Old age or dependence. Which social insurance? By Yukihiro Nishimura; Pierre Pestieau
  15. Stock Market Volatility Analysis using GARCH Family Models: Evidence from Zimbabwe Stock Exchange By Bonga, Wellington Garikai
  16. Implied and Realized Volatility: A Study of Distributions and the Distribution of Difference By M. Dashti Moghaddam; Jiong Liu; R. A. Serota
  17. House Prices, (Un)Affordability and Systemic Risk By Efthymios Pavlidis; Ivan Paya; Alex Skouralis
  18. Information Environments and High Price Impact Trades: Implication for Volatility and Price Efficiency By Dionne, Georges; Zhou, Xiaozhou

  1. By: Mathias Barkhagen; Brian Fleming; Sergio Garcia Quiles; Jacek Gondzio; Jens Kroeske; Sotirios Sabanis; Arne Staal
    Abstract: A new framework for portfolio diversification is introduced which goes beyond the classical mean-variance theory and other known portfolio allocation strategies such as risk parity. It is based on a novel concept called portfolio dimensionality and ultimately relies on the minimization of ratios of convex functions. The latter arises naturally due to our requirements that diversification measures should be leverage invariant and related to the tail properties of the distribution of portfolio returns. This paper introduces this new framework and its relationship to standardized higher order moments of portfolio returns. Moreover, it addresses the main drawbacks of standard diversification methodologies which are based primarily on estimates of covariance matrices. Maximizing portfolio dimensionality leads to highly non-trivial optimization problems with objective functions which are typically non-convex with potentially multiple local optima. Two complementary global optimization algorithms are thus presented. For problems of moderate size, a deterministic Branch and Bound algorithm is developed, whereas for problems of larger size a stochastic global optimization algorithm based on Gradient Langevin Dynamics is given. We demonstrate through numerical experiments that the introduced diversification measures possess desired properties as introduced in the portfolio diversification literature.
    Date: 2019–06
  2. By: Ariah Klages-Mundt; Andreea Minca
    Abstract: We develop a model of stable assets, including noncustodial stablecoins backed by cryptocurrencies. Such stablecoins are popular methods for bootstrapping price stability within public blockchain settings. We demonstrate fundamental results about dynamics and liquidity in stablecoin markets, demonstrate that these markets face deleveraging spirals that cause illiquidity during crises, and show that these stablecoins have `stable' and `unstable' domains. Starting from documented market behaviors, we explain actual stablecoin movements; further our results are robust to a wide range of potential behaviors. In simulations, we show that these systems are susceptible to high tail volatility and failure. Our model builds foundations for stablecoin design. Based on our results, we suggest design improvements that can improve long-term stability and suggest methods for solving pricing problems that arise in existing stablecoins. In addition to the direct risk of instability, our dynamics results suggest a profitable economic attack during extreme events that can induce volatility in the `stable' asset. This attack additionally suggests ways in which stablecoins can cause perverse incentives for miners, posing risks to blockchain consensus.
    Date: 2019–06
  3. By: Pellecchia, Marco; Perciaccante, Giovambattista
    Abstract: Our aim is to present an alternative methodology to the standard formula imposed to the insurance regulation (the European directive knows as Solvency II) for the calculus of the capital requirements. We want to demonstrate how this formula is now obsolete and how is possible to obtain lower capital requirement through the theory of the copulas, function that are gaining increasing importance in various economic areas. A lower capital requirement involves the advantage for the various insurance companies not to have unproductive capital that can therefore be used for the production of further profits. Indeed the standard formula is adequate only with some particular assumptions, otherwise it can overestimate the capital requirements that are actually needed as the standard formula underestimates the effect of diversification.
    Keywords: Solvency II, Solvency Capital Requirement, Standard Formula, Value-at-Risk, Copula.
    JEL: C13 C15 C18 C61
    Date: 2019–05–01
  4. By: Helder Rojas; David Dias
    Abstract: An optimal evaluation of the resilience in financial portfolios implies having initial hypotheses about the causal influence between the macroeconomic variables and the risk parameters. In this paper, we propose a graphical model for to infer the causal structure that links the multiple macroeconomic variables and the assessed risk parameters, Stress Testing Network, in which the relationships between the macroeconomic variables and the risk parameter define a "relational graph" among their time-series, where related time-series are connected by an edge. Our proposal is based on the temporal causal models, but unlike, we incorporate specific conditions in the structure which correspond to intrinsic characteristics to this type of networks. Following the proposed model and given the high-dimensional nature of the problem, we used regularization methods to efficiently detect causality in the time-series and reconstruct the underlying causal structure. In addition, we illustrate the use of model in credit risk data of a portfolio.
    Date: 2019–06
  5. By: Qi Wu; Shumin Ma; Cheuk Hang Leung; Wei Liu
    Abstract: The choice of the ambiguity radius is critical when an investor uses the distributionally robust approach to address the issue that the portfolio optimization problem is sensitive to the uncertainties of the asset return distribution. It cannot be set too large because the larger the size of the ambiguity set the worse the portfolio return. It cannot be too small either; otherwise, one loses the robust protection. This tradeoff demands a financial understanding of the ambiguity set. In this paper, we propose a non-robust interpretation of the distributionally robust optimization (DRO) problem. By relating the impact of an ambiguity set to the impact of a non-robust chance constraint, our interpretation allows investors to understand the size of the ambiguity set through parameters that are directly linked to investment performance. We first show that for general $\phi$-divergences, a DRO problem is asymptotically equivalent to a class of mean-deviation problem, where the ambiguity radius controls investor's risk preference. Based on this non-robust reformulation, we then show that when a boundedness constraint is added to the investment strategy, the DRO problem can be cast as a chance-constrained optimization (CCO) problem without distributional uncertainties. If the boundedness constraint is removed, the CCO problem is shown to perform uniformly better than the DRO problem, irrespective of the radius of the ambiguity set, the choice of the divergence measure, or the tail heaviness of the center distribution. Our results apply to both the widely-used Kullback-Leibler (KL) divergence which requires the distribution of the objective function to be exponentially bounded, as well as those more general divergence measures which allow heavy tail ones such as student $t$ and lognormal distributions.
    Date: 2019–06
  6. By: Wenyuan Wang; Ping Chen; Shuanming Li
    Abstract: This paper considers an insurance surplus process modeled by a spectrally negative L\'{e}vy process. Instead of the time of ruin in the traditional setting, we apply the time of drawdown as the risk indicator in this paper. We study the joint distribution of the time of drawdown, the running maximum at drawdown, the last minimum before drawdown, the surplus before drawdown and the surplus at drawdown (may not be deficit in this case), which generalizes the known results on the classical expected discounted penalty function in Gerber and Shiu (1998). The results have semi-explicit expressions in terms of the $q$-scale functions and the L\'{e}vy measure associated with the L\'{e}vy process. As applications, the obtained result is applied to recover results in the literature and to obtain new results for the Gerber-Shiu function at ruin for risk processes embedded with a loss-carry-forward taxation system or a barrier dividend strategy. Moreover, numerical examples are provided to illustrate the results.
    Date: 2019–06
  7. By: Di Wang; Qi Wu; Wen Zhang
    Abstract: This paper takes a deep learning approach to understand consumer credit risk when e-commerce platforms issue unsecured credit to finance customers' purchase. The "NeuCredit" model can capture both serial dependences in multi-dimensional time series data when event frequencies in each dimension differ. It also captures nonlinear cross-sectional interactions among different time-evolving features. Also, the predicted default probability is designed to be interpretable such that risks can be decomposed into three components: the subjective risk indicating the consumers' willingness to repay, the objective risk indicating their ability to repay, and the behavioral risk indicating consumers' behavioral differences. Using a unique dataset from one of the largest global e-commerce platforms, we show that the inclusion of shopping behavioral data, besides conventional payment records, requires a deep learning approach to extract the information content of these data, which turns out significantly enhancing forecasting performance than the traditional machine learning methods.
    Date: 2019–06
  8. By: Dimitrios Anastasiou (Athens University of Economics and Business and Alpha Bank); Zacharias Bragoudakis (Bank of Greece); Ioannis Malandrakis (Athens University of Economics and Business)
    Abstract: In this study we propose a new determinant of non-performing loans for the case of the Greek banking sector. We employ aggregate yearly data for the period 1996-2016 and we conduct a Principal Component Analysis for all the Worldwide Governance Indicators (WGI) for Greece, aiming to isolate the common component and thus to create the GOVERNANCE indicator. We find that the GOVERNANCE indicator is a significant determinant of Greek banks’ non-performing loans indicating that both political and governance factors impact on the level of the Greek non-performing loans. An additional variable that also has a statistically significant impact on the level of Greek non-performing loans, when combined with WGI in the dynamic specification of our model, is systemic liquidity risk. Our results could be of interest to policy makers and regulators as a macro prudential policy tool.
    Keywords: Credit risk; Greek banking sector; Non-performing loans; Systemic liquidity risk; Worldwide Governance Indicators.
    JEL: C51 G21 G2 G38
    Date: 2019–05
  9. By: Jin Sun; Kevin Fergusson; Eckhard Platen; Pavel V. Shevchenko
    Abstract: In this paper we consider the pricing of variable annuities (VAs) with guaranteed minimum withdrawal benefits. We consider two pricing approaches, the classical risk-neutral approach and the benchmark approach, and we examine the associated static and optimal behaviors of both the investor and insurer. The first model considered is the so-called minimal market model, where pricing is achieved using the benchmark approach. The benchmark approach was introduced by Platen in 2001 and has received wide acceptance in the finance community. Under this approach, valuing an asset involves determining the minimum-valued replicating portfolio, with reference to the growth optimal portfolio under the real-world probability measure, and it both subsumes classical risk-neutral pricing as a particular case and extends it to situations where risk-neutral pricing is impossible. The second model is the Black-Scholes model for the equity index, where the pricing of contracts is performed within the risk-neutral framework. Crucially, we demonstrate that when the insurer prices and reserves using the Black-Scholes model, while the insured employs a dynamic withdrawal strategy based on the minimal market model, the insurer may be underestimating the value and associated reserves of the contract.
    Date: 2019–06
  10. By: Mohamed Chikhi; Claude Diebolt; Tapas Mishra
    Abstract: Stock price forecasting, a popular growth-enhancing exercise for investors, is inherently complex – thanks to the interplay of financial economic drivers which determine both the magnitude of memory and the extent of non-linearity within a system. In this paper, we accommodate both features within a single estimation framework to forecast stock prices and identify the nature of market efficiency commensurate with the proposed model. We combine a class of semiparametric autoregressive fractionally integrated moving average (SEMIFARMA) model with asymmetric exponential generalized autoregressive score (AEGAS) errors to design a SEMIRFARMA-AEGAS framework based on which predictive performance of this model is tested against competing methods. Our conditional variance includes leverage effects, jumps and fat tail-skewness distribution, each of which affects magnitude of memory in a stock price system. A true forecast function is built and new insights into stock price forecasting are presented. We estimate several models using the Skewed Student-t maximum likelihood and find that the informational shocks have permanent effects on returns and the SEMIFARMA-AEGAS is appropriate for capturing volatility clustering for both negative (long Value-at-Risk) and positive returns (short Value-at-Risk). We show that this model has better predictive performance over competing models for both long and/or some short time horizons. The predictions from SEMIRFARMA-AEGAS model beats comfortably the random walk model. Our results have implications for market-efficiency: the weak efficiency assumption of financial markets stands violated for all stock price returns studied over a long period.
    Keywords: Stock price forecasting; SEMIFARMA model; AEGAS model; Skewed Student-t maximum likelihood; Asymmetry; Jumps.
    JEL: C14 C58 C22 G17
    Date: 2019
  11. By: Shihao Zhu; Jingtao Shi
    Abstract: This paper is concerned with an optimal reinsurance and investment problem for an insurance firm under the criterion of mean-variance. The driving Brownian motion and the rate in return of the risky asset price dynamic equation cannot be directly observed. And the short-selling of stocks is prohibited. The problem is formulated as a stochastic linear-quadratic (LQ) optimal control problem where the control variables are constrained. Based on the separation principle and stochastic filtering theory, the partial information problem is solved. Efficient strategies and efficient frontier are presented in closed forms via solutions to two extended stochastic Riccati equations. As a comparison, the efficient strategies and efficient frontier are given by the viscosity solution for the Hamilton-Jacobi-Bellman (HJB) equation in the full information case. Some numerical illustrations are also provided.
    Date: 2019–06
  12. By: Li, Z. M.; Laeven, R. J. A.; Vellekoop, M. H.
    Abstract: In this paper, we develop econometric tools to analyze the integrated volatility (IV) of the efficient price and the dynamic properties of microstructure noise in high-frequency data under general dependent noise. We first develop consistent estimators of the variance and autocovariances of noise using a variant of realized volatility. Next, we employ these estimators to adapt the pre-averaging method and derive consistent estimators of the IV, which converge stably to a mixed Gaussian distribution at the optimal rate n1/4. To improve the finite sample performance, we propose a multi-step approach that corrects the finite sample bias, which turns out to be crucial in applications. Our extensive simulation studies demonstrate the excellent performance of our multi-step estimators. In an empirical study, we analyze the dependence structures of microstructure noise and provide intuitive economic interpretations; we also illustrate the importance of accounting for both the serial dependence in noise and the finite sample bias when estimating IV.
    Keywords: Dependent microstructure noise, realized volatility, bias correction, integrated volatility, mixing sequences, pre-averaging method
    JEL: C13 C14 C58
    Date: 2019–06–14
  13. By: Pagano, Marco; Sánchez Serrano, Antonio; Zechner, Jozef
    Date: 2019–06
  14. By: Yukihiro Nishimura (Graduate School of Economics, Osaka University); Pierre Pestieau (CREPP, Universite de Li`ege, CORE)
    Abstract: We consider a society where individuals differ according to their productivity and their risk of mortality and dependency. We show that ac-cording to the most reasonable estimates of correlations among these threecharacteristics, if one had to choose between a public pension system anda long-term care social insurance, the latter should be chosen by a utili-tarian social planner. With a Rawlsian planner, the balance between thetwo schemes does depend on the comparison between the probabilities ofthe worst off individual and the probabilities of the rest of society.
    Keywords: long term care, pension, mortality risk, optimal taxation,liquidity constraints
    JEL: H2 H5
    Date: 2019–04
  15. By: Bonga, Wellington Garikai
    Abstract: Understanding the pattern of stock market volatility is important to investors as well as for investment policy. Volatility is directly associated with risks and returns, higher the volatility the more financial market is unstable. The volatility of the Zimbabwean stock market is modeled using monthly return series consisting of 109 observations from January 2010 to January 2019. ARCH effects test confirmed the use of GARCH family models. Symmetric and asymmetric models were used namely: GARCH(1,1), GARCH-M(1,1), IGARCH(1,1) and EGARCH(1,1). Post-estimation test for further ARCH effects were done for each model to confirm its efficiency for policy. EGARCH(1,1) turned to be the best model using both the AIC and SIC criterions; with the presence of asymmetry found to be significant. The study concludes that positive and negative shocks have different effects on the stock market returns series. Bad and good news will increase volatility of stock market returns in different magnitude. This simply imply that investors on the Zimbabwean stock exchange react differently to information depending be it positive or negative in making investment decisions.
    Keywords: Stock Market, Volatility, ARCH, GARCH, IGARCH, GARCH-M, EGARCH, Risk Premium, Zimbabwe
    JEL: C22 C58 D81 D82 E22 E44 E47 G02 G14 G15 N27 O16 R53
    Date: 2019–05–30
  16. By: M. Dashti Moghaddam; Jiong Liu; R. A. Serota
    Abstract: We study distributions of realized variance (squared realized volatility) and squared implied volatility, as represented by VIX and VXO indices. We find that Generalized Beta distribution provide the best fits. These fits are much more accurate for realized variance than for squared VIX and VXO -- possibly another indicator that the latter have deficiencies in predicting the former. We also show that there are noticeable differences between the distributions of the 1970-2017 realized variance and its 1990-2017 portion, for which VIX and VXO became available. This may be indicative of a feedback effect that implied volatility has on realized volatility. We also discuss the distribution of the difference between squared implied volatility and realized variance and show that, at the basic level, it is consistent with Pearson's correlations obtained from linear regression.
    Date: 2019–06
  17. By: Efthymios Pavlidis; Ivan Paya; Alex Skouralis
    Abstract: This is the first paper to examine the role of the real estate sector and housing unaffordability in the determination of systemic risk. We measure the systemic risk of the UK by employing the CoVaR method developed by Adrian and Brunnermeier (2011, 2016), and we explore both its cross-sectional and time series behaviour. Regarding the former, we show that when the real estate sector is under distress the tail risk of the entire financial system increases significantly. With respect to the latter, the findings of our dynamic model suggest that sustainable house prices positively contribute to the stability of the financial sector; whilst house price exuberance and rapid increases in housing unaffordability amplify systemic risk. Finally, we examine the conjecture that the banking sector comprises a transmission channel from the housing market to the systemic risk of the financial system. Our empirical results are in line with this argument and highlight the key role of housing unaffordability.
    Keywords: affordability, real estate sector, systemic risk
    JEL: C21 C23 E44
    Date: 2019
  18. By: Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management); Zhou, Xiaozhou (Université du Québec à Montréal (UQAM))
    Abstract: Using high-frequency transaction and Limit Order Book (LOB) data, we extend the identification dimensions of High Price Impact Trades (HPITs) by using LOB matchedness. HPITs are trades associated with disproportionately large price changes relative to their proportion of volume. We nd that a higher presence of HPITs leads to a decline in volatility due to more contrarian trades against uninformed traders, but this decline varies with information environments and liquidity levels. Further, we show that more HPITs lead to higher price eciency for stocks with greater public disclosure and higher liquidity. Our empirical results provide evidence that HPITs mainly reect fundamental-based information in a high public information environment, and belief-based information in a low public information environment.
    Keywords: Price eciency; Price discovery; Limit Order Book; Trade size clustering; Stealth trading.
    JEL: C22 C41 C53 G11
    Date: 2019–06–18

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