nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒07‒11
fifteen papers chosen by

  1. On the skew and curvature of implied and local volatilities By Elisa Al\`os; David Garc\'ia-Lorite; Makar Pravosud
  2. Credit market concentration and systemic risk in Europe By Merike Kukk; Alari Paulus; Nicolas Reigl
  3. Tsallis Relative entropy from asymmetric distributions as a risk measure for financial portfolios By Sandhya Devi; Sherman Page
  4. Financial-market volatility prediction with multiplicative Markov-switching MIDAS components By Bjoern Schulte-Tillman; Mawuli Segnon; Bernd Wilfling
  5. Nonparametric Value-at-Risk via Sieve Estimation By Philipp Ratz
  6. Extremes of Markov random fields on block graphs By Asenova, Stefka; Segers, Johan
  7. Extreme value statistics using related variables By Ahmed, Hanan
  8. Boom-bust cycles and asset market participation waves: Momentum, value, risk and herding By Dieci, Roberto; Schmitt, Noemi; Westerhoff, Frank H.
  9. Tail inference using extreme U-statistics By Oorschot, Jochem; Segers, Johan; Zhou, Chen
  10. Latin American Falls, Rebounds and Tail By Luciano Campos; Danilo Leiva-León; Steven Zapata
  11. Fifty years since Altman (1968): Performance of financial distress prediction models By Surbhi Bhatia; Manish K. Singh
  12. Volatility-inspired $\sigma$-LSTM cell By German Rodikov; Nino Antulov-Fantulin
  13. Public and private risk sharing: friends or foes? The interplay between different forms of risk sharing By Giovannini, Alessandro; Ioannou, Demosthenes; Stracca, Livio
  14. A shot in the arm: stimulus packages and firm performance during Covid-19 By Deniz Igan; Ali Mirzaei; Tomoe Moore
  15. Optimal double stopping problems for maxima and minima of geometric Brownian motions By Gapeev, Pavel V.; Kort, Peter M.; Lavrutich, Maria N.; Thijssen, Jacco J. J.

  1. By: Elisa Al\`os; David Garc\'ia-Lorite; Makar Pravosud
    Abstract: In this paper, we study the relationship between the short-end of the local and the implied volatility surfaces. Our results, based on Malliavin calculus techniques, recover the recent $\frac{1}{H+3/2}$ rule (where $H$ denotes the Hurst parameter of the volatility process) for rough volatilitites (see Bourgey, De Marco, Friz, and Pigato (2022)), that states that the short-time skew slope of the at-the-money implied volatility is $\frac{1}{H+3/2}$ the corresponding slope for local volatilities. Moreover, we see that the at-the-money short-end curvature of the implied volatility can be written in terms of the short-end skew and curvature of the local volatility and viceversa, and that this relationship depends on $H$.
    Date: 2022–05
  2. By: Merike Kukk; Alari Paulus; Nicolas Reigl
    Abstract: We assess empirically the relationship between credit market concentration and a novel country-level systemic risk indicator that has been developed at the European Central Bank. We find a weakly U-shaped relationship between market concentration and systemic risk for Western European countries, where very low and high levels of market concentration are associated with higher systemic risk. Cumulative estimates with dynamic models show that systemic risk has a persistent negative response to an increase in market concentration from low and median levels of concentration. Local projection estimates for the period preceding the global financial crisis also suggest that an increase in market concentration may have further added to systemic risk at a time when it was building up in countries with high banking concentration, demonstrating the complexity of the relationship between systemic risk and market concentration
    Keywords: systemic risk, financial stability, credit institutions, credit growth, market concentration
    JEL: G10 G21 E58 C22 C54
    Date: 2022–03–24
  3. By: Sandhya Devi; Sherman Page
    Abstract: In an earlier study, we showed that Tsallis relative entropy (TRE), which is the generalization of Kullback-Leibler relative entropy (KLRE) to non-extensive systems, can be used as a possible risk measure in constructing risk optimal portfolios whose returns beat market returns. Over a long term (> 10 years), the risk-return profiles from TRE as the risk measure show a more consistent behavior than those from the commonly used risk measure 'beta' of the Capital Asset Pricing Model (CAPM). In these investigations, the model distributions derived from TRE are symmetric. However, observations show that distributions of the returns of financial markets and equities are in general asymmetric in positive and negative returns. In this work, we generalize TRE for the asymmetric case (ATRE) by considering the data distribution as a linear combination of two independent normalized distributions - one for negative returns and one for positive returns. Each of these two independent distributions are half q-Gaussians with different non-extensivity parameter q and temperature parameter b. The risk-return (in excess of market returns) patterns are investigated using ATRE as the risk measure. The results are compared with those from two other risk measures: TRE and the Tsallis relative entropy S- derived from the negative returns only. Tests on data, which include the dot-com bubble, the 2008 crash, and COVID periods, for both long (20 years) and shorter terms (10 years), show that a linear fit can be obtained for the risk-excess return profiles of all three risk measures. However, the fits for portfolios created during the chaotic market conditions (crashes) using S- as the risk show a much higher slope pointing to higher returns for a given risk value. Further, in this case, the excess returns of even short-term portfolios remain positive irrespective of the market behavior.
    Date: 2022–05
  4. By: Bjoern Schulte-Tillman; Mawuli Segnon; Bernd Wilfling
    Abstract: We propose four multiplicative-component volatility MIDAS models to disentangle short- and long-term volatility sources. Three of our models specify short-term volatility as Markov-switching processes. We establish statistical properties, covariance-stationarity conditions, and an estimation framework using regime-switching filter techniques. A simulation study shows the robustness of the estimates against several mis-specifications. An out-of-sample forecasting analysis with daily S&P500 returns and quarterly-sampled (macro)economic variables yields two major results. (i) Specific long-term variables in the MIDAS models significantly improve forecast accuracy (over the non-MIDAS benchmarks). (ii) We robustly find superior performance of one Markov-switching MIDAS specification (among a set of competitor models) when using the 'Term structure' as the long-term variable.
    Keywords: MIDAS volatility modeling, Hierarchical hidden Markov models, Markov-switching, Forecasting, Model conï¬ dence sets
    JEL: C51 C53 C58 E44
    Date: 2022–06
  5. By: Philipp Ratz
    Abstract: Artificial Neural Networks (ANN) have been employed for a range of modelling and prediction tasks using financial data. However, evidence on their predictive performance, especially for time-series data, has been mixed. Whereas some applications find that ANNs provide better forecasts than more traditional estimation techniques, others find that they barely outperform basic benchmarks. The present article aims to provide guidance as to when the use of ANNs might result in better results in a general setting. We propose a flexible nonparametric model and extend existing theoretical results for the rate of convergence to include the popular Rectified Linear Unit (ReLU) activation function and compare the rate to other nonparametric estimators. Finite sample properties are then studied with the help of Monte-Carlo simulations to provide further guidance. An application to estimate the Value-at-Risk of portfolios of varying sizes is also considered to show the practical implications.
    Date: 2022–05
  6. By: Asenova, Stefka (Université catholique de Louvain, LIDAM/ISBA, Belgium); Segers, Johan (Université catholique de Louvain, LIDAM/ISBA, Belgium)
    Abstract: We study the joint occurrence of large values of a Markov random field or undirected graphical model associated to a block graph. On such graphs, containing trees as specialcases, we aim to generalize recent results for extremes of Markov trees. Every pair ofnodes in a block graph is connected by a unique shortest path. These paths are shownto determine the limiting distribution of the properly rescaled random field given that a fixed variable exceeds a high threshold. When the sub-vectors induced by the blocks follow Hüsler–Reiss extreme value copulas, the global Markov property of the original field induces a particular structure on the parameter matrix of the limiting max-stable Hüsler–Reiss distribution. The multivariate Pareto version of the latter turns out to be an extremal graphical model according to the original block graph. Moreover, thanks to these algebraic relations, the parameters are still identifiable even if some variables are latent.
    Keywords: Markov random field ; graphical model ; block graph ; multivariate extremes ; tail dependence ; latent variable ; Hüsler–Reiss distribution ; conditional independence
    Date: 2022–01–01
  7. By: Ahmed, Hanan (Tilburg University, School of Economics and Management)
    Date: 2022
  8. By: Dieci, Roberto; Schmitt, Noemi; Westerhoff, Frank H.
    Abstract: We develop an asset market participation model in which investors base their market entry decisions on the momentum, value and risk of the market. Despite our behavioral framework, the model's fundamental steady state is characterized by standard present-value relations between expected future payouts and the model-implied risk-adjusted return. We derive conditions under which endogenous asset market participation waves and co-evolving boom-bust cycles emerge. Moreover, we show that the asset market may display spontaneous, sharp and permanent downturns if investors react sensitively to risk, an outcome that goes hand in hand with low asset market participation rates and excess volatility.
    Keywords: boom-bust cycles,asset market participation waves,momentum, value and risk,herding behavior,feedback loops
    JEL: D84 G12 G41
    Date: 2022
  9. By: Oorschot, Jochem; Segers, Johan (Université catholique de Louvain, LIDAM/ISBA, Belgium); Zhou, Chen
    Abstract: Extreme U-statistics arise when the kernel of a U-statistic has a high degree but depends only on its arguments through a small number of top order statistics. As the kernel degree of the U-statistic grows to infinity with the sample size, estimators built out of such statistics form an intermediate family in between those constructed in the block maxima and peaks-over-threshold frameworks in extreme value analysis. The asymptotic normality of extreme U-statistics based on location-scale invariant kernels is established. Although the asymptotic variance corresponds with the one of the Hájek projection, the proof goes beyond considering the first term in Hoeffding’s variance decomposition; instead, a growing number of terms needs to be incorporated in the proof. To show the usefulness of extreme U-statistics, we propose a kernel depending on the three highest order statistics leading to an unbiased estimator of the shape parameter of the generalized Pareto distribution. When applied to samples in the max-domain of attraction of an extreme value distribution, the extreme U-statistic based on this kernel produces a locationscale invariant estimator of the extreme value index which is asymptotically normal and whose finite-sample performance is competitive with that of the pseudo-maximum likelihood estimator.
    Keywords: U-statistic ; Generalized Pareto distribution ; Hájek projection ; Extreme value index
    Date: 2022–03–16
  10. By: Luciano Campos (Universidad de Alcalá); Danilo Leiva-León (Banco de España); Steven Zapata (Banco de la República)
    Abstract: This paper proposes comprehensive measures of the Latin American business cycle that help to infer the expected deepness of recessions, and strength of expansions, asthey unfold in real time. These measures are based on the largest country economies in the region by accounting for intrinsic features of real activity, such as comovement,nonlinearities, asymmetries, and are also robust to unprecedented shocks, like the COVID-19 pandemics. The proposed measures provide timely updates on (i) inferences on the state of the regional economy, (ii) the underlying momentum embedded in short-term fluctuations of real activity, and (iii) the quantification of macroeconomic tail risks. We evaluate as well the time-varying effects of U.S. financial conditions on the Latin American economy by employing the proposed measures, and identify periods of persistent international spillovers.
    Keywords: Business Cycles, Factor Model, Nonlinear, Latin America
    JEL: E32 C22 E27
    Date: 2022–05
  11. By: Surbhi Bhatia (Independent Researcher); Manish K. Singh (Department of Humanities and Social Sciences Indian Institute of Technology Roorkee and XKDR Forum)
    Abstract: Using bankruptcy filings under the new Insolvency and Bankruptcy Code (2016), we investigate the effect of firm characteristics and balance sheet variables on the forecast of one-year-ahead default for Indian manufacturing firms. We compare traditional discriminant analysis and logistic regression models with state-of-the-art variable selection technique-the least absolute shrinkage and selection operator, and the unsupervised techniques of variable selection-to identify key predictive variables. Our findings suggest that the ratios considered as important by Altman (1968) still hold relevance for the prediction of default, no matter the technique applied for variables selection. We find cash to current liability (a liquidity measure) as an additional robust and significant predictor of default. In terms of predictive accuracy, the reduced-form multivariate discriminant analysis model used in Altman (1968) performs at par with the more advanced econometric specification for both in-sample and full-sample default prediction.
    JEL: C53 G17 G32 G33
    Date: 2022–06
  12. By: German Rodikov; Nino Antulov-Fantulin
    Abstract: Volatility models of price fluctuations are well studied in the econometrics literature, with more than 50 years of theoretical and empirical findings. The recent advancements in neural networks (NN) in the deep learning field have naturally offered novel econometric modeling tools. However, there is still a lack of explainability and stylized knowledge about volatility modeling with neural networks; the use of stylized facts could help improve the performance of the NN for the volatility prediction task. In this paper, we investigate how the knowledge about the "physics" of the volatility process can be used as an inductive bias to design or constrain a cell state of long short-term memory (LSTM) for volatility forecasting. We introduce a new type of $\sigma$-LSTM cell with a stochastic processing layer, design its learning mechanism and show good out-of-sample forecasting performance.
    Date: 2022–05
  13. By: Giovannini, Alessandro; Ioannou, Demosthenes; Stracca, Livio
    Abstract: Well-functioning risk-sharing arrangements are essential for the shock absorbing capacity and resilience of an economy, even more so for countries in a monetary union where the single monetary policy is unable to address asymmetric shocks. The common shocks that euro area member states have been facing over the past years are just that: common. Yet their impacts are far from equal across countries, implying that risk sharing remains an important issue. This paper discusses the different forms and channels of risk sharing and reviews the main arguments in favour and against the development of different forms of public and private risk sharing in the euro area, focusing in particular on whether they act as complements or substitutes. It proposes a stylised theoretical model of a monetary union to test the complementarity or substitutability between public and private risk sharing. While the model calibration finds that substitutability prevails, the model also contains an interesting complementarity whereby a central fiscal capacity makes private risk sharing more efficient, especially in crisis times. Our findings are relevant for the ongoing policy discussion on EMU deepening as the provision of public risk sharing as well as the overall degree of risk sharing are still comparatively low in the euro area. JEL Classification: C23, E62, G11, G15
    Keywords: Economic and Monetary Union, monetary union, Risk sharing
    Date: 2022–06
  14. By: Deniz Igan; Ali Mirzaei; Tomoe Moore
    Abstract: We use firm-level data to provide some early evidence on the effectiveness of COVID-19 economic policy packages. Our empirical strategy relies on the varying degree of vulnerability to the pandemic across industries. We find a robust association of fiscal stimulus with changes in firm performance indicators (as measured by sales-to-assets ratio, profit margin, interest coverage ratio as well as probability of default) in pandemic-prone sectors. We also observe marginal effects of monetary policy on the sales-to-assets ratio and of foreign exchange intervention on the interest coverage ratio in the hardest-hit firms. These results broadly survive a battery of exercises to address endogeneity. Additionally, we show that firms with a better financial position are more likely to take advantage of the stimulus packages to withstand the pandemic shock. Overall, these provide preliminary evidence suggesting that policy interventions have bought time for the hardest-hit industries, by supporting turnover and improving liquidity.
    Keywords: economic stimulus, pandemic-prone, COVID-19, policy effectiveness
    JEL: G01 G14 G28 E65
    Date: 2022–05
  15. By: Gapeev, Pavel V.; Kort, Peter M.; Lavrutich, Maria N.; Thijssen, Jacco J. J.
    Abstract: We present closed-form solutions to some double optimal stopping problems with payoffs representing linear functions of the running maxima and minima of a geometric Brownian motion. It is shown that the optimal stopping times are th first times at which the underlying process reaches some lower or upper stochastic boundaries depending on the current values of its running maximum or minimum. The proof is based on the reduction of the original double optimal stopping problems to sequences of single optimal stopping problems for the resulting three-dimensional continuous Markov process. The latter problems are solved as the equivalent free-boundary problems by means of the smooth-fit and normal-reflection conditions for the value functions at the optimal stopping boundaries and the edges of the three-dimensional state space. We show that the optimal stopping boundaries are determined as the extremal solutions of the associated first-order nonlinear ordinary differential equations. The obtained results are related to the valuation of perpetual real double lookback options with floating sunk costs in the Black-Merton-Scholes model.
    Keywords: perpetual real double lookback options; the Black-Merton-Scholes model; geometric Brownian motion; double optimal stopping problem; first hitting time; free-boundary problem; instantaneous stopping and smooth fit; normal reflection; a change-of-variable formula with local time on surfaces; Springer deal
    JEL: G13
    Date: 2022–05–14

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