nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒02‒28
seventeen papers chosen by
Stan Miles
Thompson Rivers University

  2. Model Aggregation for Risk Evaluation and Robust Optimization By Tiantian Mao; Ruodu Wang; Qinyu Wu
  3. Migration to the PRIIPs framework: what impact on the European risk indicator of UCITS funds ? By Herr, Donovan; Clausse, Emilien; Vrins, Frédéric
  4. Credit Default Swaps and Credit Risk Reallocation By Dorian Henricot; Thibaut Piquard
  5. Multinomial Backtesting of Distortion Risk Measures By S\"oren Bettels; Sojung Kim; Stefan Weber
  6. Optimal Portfolio Diversification via Independent Component Analysis By DeMiguel, Victor; Lassance, Nathan; Vrins, Frédéric
  7. Conditional Heteroskedasticity in the Volatility of Asset Returns By Ding, Y.
  8. Portfolio Selection: A Target-Distribution Approach By Lassance, Nathan; Vrins, Frédéric
  9. Précisions importantes sur le backtesting comparatif de la VaR By Saissi Hassani, Samir
  10. Review of International Supply Chain Risk Within Banking Regulations in Asia, US and EU Including Proposals to Improve Cost Efficiency by Meeting Regulatory Compliance By Seipp, Vanessa; Michel, Alex; Siegfried, Patrick
  11. Optimal trend following portfolios By Sebastien Valeyre
  12. The Rising Interconnectedness of the Insurance Sector By Tristan Jourde
  13. Maximizing the Out-of-Sample Sharpe Ratio By Lassance, Nathan
  14. Return and volatility spillovers between Chinese and U.S. Clean Energy Related Stocks: Evidence from VAR-MGARCH estimations By Karel Janda; Ladislav Kristoufek; Binyi Zhang
  15. Forecasting Returns of Major Cryptocurrencies: Evidence from Regime-Switching Factor Models By Elie Bouri; Christina Christou; Rangan Gupta
  16. Cryptocurrencies, Diversification and the COVID-19 Pandemic By Allen, David
  17. Complete Markets with Bankruptcy Risk and Pecuniary Default Penalties By Victor Filipe Martins da Rocha; Rafael Mouallem Rosa

  1. By: Mustafa Hakan Eratalay; Ariana Paola Cortés à ngel
    Abstract: There are diverging results in the literature on whether engaging in ESG related activities increases or decreases the financial and systemic risks of firms. In this paper we explore whether maintaining higher ESG ratings would reduce the systemic risks of firms in a stock market context. For this purpose we analyse the systemic risk indicators of the constituent stocks of S&P Europe 350 for the period of January 2016 - September 2020, which also partly covers the Covid-19 period. We apply a VAR-MGARCH model to extract the volatilities and correlations of the return shocks of these stocks. Then we obtain the systemic risk indicators by applying a principle components approach to the estimated volatilities and correlations. Our focus is on the impact of ESG ratings on systemic risk indicators, while we consider network centralities, volatilities and financial performance ratios as control variables. We use fixed effects and OLS methods for our regressions. Our results indicate that (1) the volatility of a stock’s returns and its centrality measures in the stock network are the main sources contributing to the systemic risk measure (2) firms with higher ESG ratings face up to 7.3% less systemic risk contribution and exposure compared to firms with lower ESG ratings, (3) Covid-19 augmented the partial effects of volatility, centrality measures and some financial performance ratios. When considering only the Covid-19 period, we found that social and governance factors have statistically significant impacts on systemic risk.
    Keywords: systemic risk, network centrality, sustainable, ESG, volatility, principal components, Covid-19
    Date: 2022
  2. By: Tiantian Mao; Ruodu Wang; Qinyu Wu
    Abstract: We introduce a new approach for prudent risk evaluation based on stochastic dominance, which will be called the model aggregation (MA) approach. In contrast to the classic worst-case risk (WR) approach, the MA approach produces not only a robust value of risk evaluation but also a robust distributional model which is useful for modeling, analysis and simulation, independent of any specific risk measure. The MA approach is easy to implement even if the uncertainty set is non-convex or the risk measure is computationally complicated, and it provides great tractability in distributionally robust optimization. Via an equivalence property between the MA and the WR approaches, new axiomatic characterizations are obtained for a few classes of popular risk measures. In particular, the Expected Shortfall (ES, also known as CVaR) is the unique risk measure satisfying the equivalence property for convex uncertainty sets among a very large class. The MA approach for Wasserstein and mean-variance uncertainty sets admits explicit formulas for the obtained robust models, and the new approach is illustrated with various risk measures and examples from portfolio optimization.
    Date: 2022–01
  3. By: Herr, Donovan; Clausse, Emilien; Vrins, Frédéric (Université catholique de Louvain, LIDAM/LFIN, Belgium)
    Abstract: Since 2011, managers of European UCITS funds are required to publish a risk indicator, called SRRI, in order to communicate the risk of their investment fund to retail investors in an understandable way. However, as of mid-2022, the implementation of the new PRIIPs regulation will lead to a completereview of the calculation methodology employed to determine this risk indicator. The latter, formerly based on a traditional measure of standard deviation, will now be determined from a more sophisticated tail-risk measure, namely Value-at-Risk (or, more precisely, the modified VaR, which is an approximation based on the first four moments of the fund returns). Additional changes deal with the data frequency and history used in the estimation procedure. In this article, we break down the changes brought by the regulation and analyze them through an empirical study in order to take a critical look on the new PRIIPs methodology, that will impact a substantial portion of the 4 500 asset management companies active in Europe1 . Our results, built from a random selection of 200 funds, show that the impact of the change in the risk measure is not as significant as expected. By contrast, the impact resulting from the changes in the chosen frequency and length of returns history seems material. Secondly, the redefinition of volatility buckets used to map the risk measure to the risk indicator has a side effect : a loss of granularity for non-extreme funds, which are now crowded in classes 2 to 4.
    Date: 2021–01–01
  4. By: Dorian Henricot; Thibaut Piquard
    Abstract: We use data on granular holdings of debt and Credit Default Swaps (CDS) referencing non-financial corporations across financial investors, to investigate how CDS reallocate credit risk and whether this increases investor-level riskiness. To guide our investigation, we propose a methodology to disentangle CDS positions between three strategies: hedging, speculation, and arbitrage. In our dataset, arbitrage remains anecdotal. We find that CDS reduce exposure concentration, as hedgers shed off their most concentrated exposures, while speculators substitute debt for CDS. CDS also facilitate risk-taking by speculators. Overall, CDS increase portfolio risk metrics, due to a limited effect of hedging strategies compared to speculative ones.
    Keywords: Credit Default Swaps, Credit Risk
    JEL: E44 G11 G20 G23
    Date: 2022
  5. By: S\"oren Bettels; Sojung Kim; Stefan Weber
    Abstract: We extend the scope of risk measures for which backtesting models are available by proposing a multinomial backtesting method for general distortion risk measures. The method relies on a stratification and randomization of risk levels. We illustrate the performance of our methods in numerical case studies.
    Date: 2022–01
  6. By: DeMiguel, Victor; Lassance, Nathan (Université catholique de Louvain, LIDAM/LFIN, Belgium); Vrins, Frédéric (Université catholique de Louvain, LIDAM/LFIN, Belgium)
    Abstract: A natural approach to enhance portfolio diversification is to rely on factor-risk parity, which yields the portfolio whose risk is equally spread among a set of uncorrelated factors. The standard choice is to take the variance as risk measure, and the principal components (PCs) of asset returns as factors. Although PCs are unique and useful for dimension reduction, they are an arbitrary choice: any rotation of the PCs results in uncorrelated factors. This is problematic because we demonstrate that any portfolio is a factor-varianceparity portfolio for some rotation of the PCs. More importantly, choosing the PCs does not account for the higher moments of asset returns. To overcome these issues, we propose to use the independent components (ICs) as factors, which are the rotation of the PCs that are maximally independent, and care about higher moments of asset returns. We demonstrate that using the IC-variance-parity portfolio helps to reduce the return kurtosis. We also show how to exploit the near independence of the ICs to parsimoniously estimate the factor-risk-parity portfolio based on Value-at-Risk. Finally, we empirically demonstrate that portfolios based on ICs outperform those based on PCs, and several state-of-the-art benchmarks.
    Keywords: portfolio selection ; risk parity ; factor analysis ; principal component analysis ; higher moments
    Date: 2021–06–01
  7. By: Ding, Y.
    Abstract: We propose a new class of conditional heteroskedasticity in the volatility (CHV) models which allows for time-varying volatility of volatility in the volatility of asset returns. This class nests a variety of GARCH-type models and the SHARV model of Ding (2021). CH-V models can be seen as a special case of the stochastic volatility of volatility model. We then introduce two examples of CH-V in which we specify a GJR-GARCH and an E-GARCH processes for the volatility of volatility, respectively. We also show a novel way of introducing the leverage effect of negative returns on the volatility through the volatility of volatility process. Empirical study confirms that CH-V models have better goodness-of-fit and out-of-sample volatility and Value-at-Risk forecasts than common GARCH-type models.
    Keywords: forecasting, GARCH, SHARV, volatility, volatility of volatility
    JEL: C22 C32 C53 C58 G17
    Date: 2021–11–09
  8. By: Lassance, Nathan (Université catholique de Louvain, LIDAM/LFIN, Belgium); Vrins, Frédéric (Université catholique de Louvain, LIDAM/CORE, Belgium)
    Abstract: We introduce a general framework to the portfolio-selection problem in which investors aim at targeting a distribution of returns, which can accommodate a wide range of preferences. The resulting optimal portfolio has a return density that is as close as possible to the target-return density. We study the theoretical properties of this approach for two classes of target distribution that allow for different first four moments. Three results that stand out are, first, that the fit to higher moments is controlled by the entropy of standardized portfolio returns when targeting a Gaussian distribution. Second, when targeting a specific Dirac-delta distribution, no norm-constrained portfolio can stochastically dominate the proposed optimal portfolio. Third, if the target-return mean and variance are located on or above the efficient frontier, the optimal portfolio is mean-variance efficient when asset returns are Gaussian. For non-Gaussian returns, the optimal portfolio may move away from the frontier to better fit the higher moments of the target distribution. The empirical analysis illustrates that the proposed framework helps the investor obtain portfolio returns in line with her preferences.
    Keywords: portfolio selection, higher moments, tail risk, Kullback-Leibler divergence
    JEL: G11 G12
    Date: 2021–07–26
  9. By: Saissi Hassani, Samir (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: VaR remains important in market risk management as Basel keeps most of the backtesting based on 1% VaR. Comparative backtesting as practiced in the current literature suffers from a major double problem. On the one hand, the score functions, although strictly consistent, may assign very good or even the best scores to clearly failing models. On the other hand, the DM test (Diebold and Mariano, 1995), as widely used to validate the score functions, fails to detect these "false best" models. We document these facts with concrete cases. To correct this issue, we propose to build the DM test using the identification functions associated with the score functions rather than on the score functions themselves. In addition, we provide a key improvement to the conditional calibration test of Nolde and Ziegel (2017). This allows for additional model validation via score functions, while providing a novel way to test the conditional coverage assumption on the standard backtesting side. The parallel and collaborative approach of the two VaR backtesting components, the standard and the comparative one, allows for increased conceptual richness and robustness of the overall backtesting. Finally, given the conceptual similarities, the points addressed regarding VaR should also concern the comparative backtesting of CVaR.
    Keywords: Value at Risk; market risk; Basel settlements; standard backtesting; comparative backtesting; score functions; unconditional coverage; conditional coverage
    JEL: C44 C46 C52 G21 G24 G28 G32
    Date: 2022–02–24
  10. By: Seipp, Vanessa; Michel, Alex; Siegfried, Patrick
    Abstract: Major financial institutions operate in different regions of the world facing different regulatory landscapes for Supply Chain risks. In this environment, the optimization issue arises how to best comply with the different regulations and reach cost efficiency at the same time. In this research, the international regulatory landscape for Supply Chain risks of Financial Institutions is introduced and compared internationally. It is understood as an integral part of the Supply Chain Risk Management of Financial Institutions, yet the latter is analysed as the research background. Additionally, expert interviews are conducted in order to link the regulation analysis to the current challenges that Financial Institutions face. Finally, recommendations are developed on how banks can be cost-efficient, while remaining regulatory compliant, facing in-creased international regulation in the area of Supply Chain Risk Management. The outcome of the underlying research shows that banking regulation in the area of Sup-ply Chain risks is an important lever in the banking sector to secure customers and financial markets. However, the regulatory landscape is heterogeneous and not consistent on an international scale. Regulation in Asia is highly diverse across different countries due to different states of economic development. The US applies a rather pragmatical approach towards supply chain risk regulation applying different standards of standard-setting institutions. Lastly, the EU is very restrictive and strives to unify regulation across member states. Banks should follow a consistent management approach keeping in mind international locations and the strictest regulatory environment they are operating in, to improve cost efficiency yet be regulatory compliant. Also, collaboration with and amongst regulators and other banks internationally is recommended for improved cost efficiency.
    Keywords: International Banking Regulation, Supply Chain Risk Management, Outsourcing
    JEL: G24 R41
    Date: 2020–09–24
  11. By: Sebastien Valeyre
    Abstract: This paper derives an optimal portfolio that is based on trend-following signal. Building on an earlier related article, it provides a unifying theoretical setting to introduce an autocorrelation model with the covariance matrix of trends and risk premia. We specify practically relevant models for the covariance matrix of trends. The optimal portfolio is decomposed into four basic components that yield four basic portfolios: Markowitz, risk parity, agnostic risk parity, and trend following on risk parity. The overperformance of the proposed optimal portfolio, applied to cross-asset trading universe, is confirmed by empirical backtests. We provide thus a unifying framework to describe and rationalize earlier developed portfolios.
    Date: 2022–01
  12. By: Tristan Jourde
    Abstract: This paper examines the long-term evolution of the linkages of the insurance sector with financial and non-financial companies. We develop a measure of connectedness using a multifactor model of weekly equity returns. The empirical analysis is conducted from 1973 to 2018, for 16 developed countries, at both the sectoral and institution levels. The results indicate that, unlike other sectors, the connectedness level of the insurance industry has strengthened over time. We also find that the linkages of the largest insurance companies with financial and non-financial firms are structurally different but as high as those of the largest banks.
    Keywords: Comovements, Insurance Sector, Interconnectedness, Macroprudential Regulation, Systemic Risk
    JEL: G22 G15
    Date: 2022
  13. By: Lassance, Nathan (Université catholique de Louvain, LIDAM/LFIN, Belgium)
    Abstract: Maximizing the out-of-sample Sharpe ratio is an important objective for investors. To achieve this, we characterize optimal portfolio combinations maximizing expected out-of-sample Sharpe ratio. When investing in the risk-free asset is allowed and combination coefficients are unconstrained, as in Kan and Zhou (2007), we uncover that combining portfolios to maximize expected out-of-sample utility optimizes expected outof-sample Sharpe ratio as well. However, the two criteria are not equivalent for portfolios fully invested in risky assets, and in this case we show how to adapt the optimal portfolio combination of Kan, Wang, and Zhou (2021) to maximize expected out-of-sample Sharpe ratio. We find that the proposed mean-variance portfolio combinations calibrated to maximize expected out-of-sample Sharpe ratio generally outperform the considered benchmarks. Relative to the minimum-variance portfolio estimated with a nonlinearly shrunk covariance matrix, the annualized out-of-sample Sharpe ratio increases from 0.988 to 1.110 before transaction costs, and from 0.914 to 1.007 net of transaction costs, on average across four typical datasets.
    Keywords: Mean-variance portfolio ; parameter uncertainty ; estimation risk ; out-of-sample performance
    Date: 2021–12–23
  14. By: Karel Janda; Ladislav Kristoufek; Binyi Zhang
    Abstract: Objective of this paper is to empirically investigate the dynamic connectedness between oil prices and stock returns of clean energy related and technology companies in China and U.S. financial markets. Three different multivariate Generalised Autoregression Conditional Heteroscedasticity (VAR-MGARCH) model specifications are used to investigate the return and volatility spillovers among series. By comparing these three models, we find that the VAR(1)-DCC(1,1) model with the skewed Student t distribution fits the data the best. The results of DCC estimation reveal that, on average, a $1 long position in Chinese clean energy companies in the Chinese financial market can be hedged for 18 cents with a short position in clean energy index in the U.S market. Our empirical findings provide investors and policymakers with the systematic understanding of spillover effects between China and U.S. clean energy stock markets.
    Keywords: Clean energy, Oil, Technology, Stock prices, VAR-MGARCH
    JEL: G11 Q20
    Date: 2021–11–16
  15. By: Elie Bouri (School of Business, Lebanese American University, Lebanon); Christina Christou (School of Economics and Management, Open University of Cyprus, 2252, Latsia, Cyprus); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: The returns of cryptocurrencies tend to co-move, with their degree of co-movement being contingent on the (bullish- or bearish-) states. Given this, we use standard factor models and regime-switching factor loadings to forecast the returns of a specific cryptocurrency based on its lagged information and informational contents of 14 other cryptocurrencies, with these 15 together constituting 65% of the market capitalization. Considering top five cryptocurrencies namely, Bitcoin, Ethereum, Ripple, Dogecoin, and Litecoin, we find significant forecastability and evidence that factor models, in general, outperform the benchmark random-walk model, with the regime-switching versions standing out in the majority of the cases.
    Keywords: Cryptocurrencies, Factor Model, Markov-switching, Forecasting
    JEL: C22 C53 G15
    Date: 2022–02
  16. By: Allen, David
    Abstract: The paper features an analysis of cryptocurrencies and the impact of the COVID- 19 pandemic on their effectiveness as a portfolio diversification tool. It does so by exploring the correlations between the continuously compounded returns on Bitcoin, Ethereum and the S&P500 Index, using a variety of parametric and non-parametric techniques. These methods include linear standard metrics such as the application of ordinary least squares regression (OLS) and the Pearson, Spearman, and Kendall's tau measures of association. In addition, nonlinear, non-parametric measures such as the Generalised Measure of Correlation (GMC), and non-parametric copula estimates are applied. The results across this range of measures are consistent. The metrics suggest that whilst the shock of the COVID-18 pandemic does not appear to have increased the correlations between the crypto currency series, it does appear to have increased the correlations between the returns on crypto currencies and those on the S&P500 Index. This suggests that investment in cryptocurrencies is not likely to offer key diversification strategies in times of crisis, on the basis of evidence provided by this crisis
    Keywords: Bitcoin, Ethereum, Copula, kernel estimation, non-parametric, GMC
    JEL: C19 C65 G01 G11
    Date: 2021–12–31
  17. By: Victor Filipe Martins da Rocha (LEDa - Laboratoire d'Economie de Dauphine - IRD - Institut de Recherche pour le Développement - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique, EESP - Sao Paulo School of Economics - FGV - Fundacao Getulio Vargas [Rio de Janeiro]); Rafael Mouallem Rosa (EESP - Sao Paulo School of Economics - FGV - Fundacao Getulio Vargas [Rio de Janeiro])
    Abstract: For an infinite horizon economy with complete contingent markets, bankruptcy risk, and linear utility penalties for default, Araujo and Sandroni (1999) and Araujo, Da Silva, and Faro (2016) show that if agents have heterogeneous beliefs, then a competitive equilibrium without bankruptcy does not exist. The first contribution of this paper is to show that even if all agents have homogenous beliefs, the existence of an equilibrium is guaranteed only under stringent conditions on default penalty rates. In order to discourage agents from making promises that they know in advance they will not be able to honor, default penalty rates must be large enough. Are the "real-life" default penalties sufficiently harsh? Since utility penalties are difficult to measure in practice, we propose to address this question by replacing the "reduced-form" linear default penalties with pecuniary punishments in the line of Kehoe and Levine (1993). We show that, independently of the severity of the pecuniary punishment, an equilibrium without bankruptcy never exists.
    Date: 2022

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