nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒02‒08
twenty-two papers chosen by
Stan Miles
Thompson Rivers University

  1. A Two-Population Mortality Model to Assess Longevity Basis Risk By Selin \"Ozen; \c{S}ule \c{S}ahin
  2. Extremile Regression By Daouia, Abdelaati; Gijbels, Irene; Stupfler, Gilles
  3. Testing and Modelling Time Series with Time Varying Tails By Palumbo, D.
  4. Asset Liability Management: Evidence from the Banco de Portugal defined benefit pension fund By Maria Teresa Medeiros Garcia; Liane Costa Gabriel
  5. Diagnosis of systemic risk and contagion across financial sectors By Sayuj Choudhari; Richard Licheng Zhu
  6. Climate Finance By Stefano Giglio; Bryan T. Kelly; Johannes Stroebel
  7. Finding the Bad Apples in the Barrel: Using the Market Value of Equity to Signal Banking Sector Vulnerabilities By Will Kerry
  8. To VaR, or Not to VaR, That is the Question By Olkhov, Victor
  9. Marking to Market Corporate Debt By Lorenzo Bretscher; Peter Feldhütter; Andrew Kane; Lukas Schmid
  10. On the RND under Heston's stochastic volatility model By Ben Boukai
  11. Measuring and Managing COVID-19 Model Risk By Mark J. Jensen
  12. High-frequency dynamics of the implied volatility surface By Bastien Baldacci
  13. International Financial Connection and Stock Return Comovement By Sakai Ando
  14. A $C^{0,1}$-functional It\^o's formula and its applications in mathematical finance By Bruno Bouchard; Gr\'egoire Loeper; Xiaolu Tan
  15. The financial fragility of European households in the time of COVID-19 By Maria Demertzis; Marta Domínguez-Jiménez; Annamaria Lusardi; Bruegel
  16. Firms' Exposures to Geographic Risks By Bernard Dumas; Tymur Gabuniya; Richard C. Marston
  17. Economic uncertainty, macroprudential policies and bank risk: Evidence from emerging Asian economies By Jeon, Bang; Yao, Yao; Chen, Minghua; Wu, Ji
  18. Uncertainty and Monetary Policy During Extreme Events By Giovanni Pellegrino; Efrem Castelnuovo; Giovanni Caggiano
  19. The 'COVID' Crash of the 2020 U.S. Stock Market By Min Shu; Ruiqiang Song; Wei Zhu
  20. The aggregate consequences of default risk: evidence from firm-level data By Besley, Timothy; Roland, Isabelle; Van Reenen, John
  21. "OEconomicae et pecuniariae quaestiones" and Catholic Finance : A reading attempt from Merton's functions By Lemeunier, Sébastien
  22. Estimation of future discretionary benefits in traditional life insurance By Florian Gach; Simon Hochgerner

  1. By: Selin \"Ozen; \c{S}ule \c{S}ahin
    Abstract: Index-based hedging solutions are used to transfer the longevity risk to the capital markets. However, mismatches between the liability of the hedger and the hedging instrument cause longevity basis risk. Therefore, an appropriate two-population model to measure and assess the longevity basis risk is required. In this paper, we aim to construct a two-population mortality model to provide an effective hedge against the longevity basis risk. The reference population is modelled by using the Lee-Carter model with the renewal process and exponential jumps proposed by \"Ozen and \c{S}ahin (2020) and the dynamics of the book population are specified. The analysis based on the UK mortality data indicates that the proposed model for the reference population and the common age effect model for the book population provide a better fit compared to the other models considered in the paper. Different two-population models are used to investigate the impact of the sampling risk on the index-based hedge as well as to analyse the risk reduction regarding hedge effectiveness. The results show that the proposed model provides a significant risk reduction when mortality jumps and the sampling risk are taken into account.
    Date: 2021–01
  2. By: Daouia, Abdelaati; Gijbels, Irene; Stupfler, Gilles
    Abstract: Regression extremiles define a least squares analogue of regression quantiles.They are determined by weighted expectations rather than tail probabilities. Of special interest is their intuitive meaning in terms of expected minima and maxima. Their use appears naturally in risk management where, in contrast to quantiles, they fulfill the coherency axiom and take the severity of tail losses into account. In addition, they are comonotonically additive and belong to both the families of spec- tral risk measures and concave distortion risk measures. This paper provides the first detailed study exploring implications of the extremile terminology in a general setting of presence of covariates. We rely on local linear (least squares) check func- tion minimization for estimating conditional extremiles and deriving the asymptotic normality of their estimators. We also extend extremile regression far into the tails of heavy-tailed distributions. Extrapolated estimators are constructed and their asymptotic theory is developed. Some applications to real data are provided.
    Date: 2021–01–18
  3. By: Palumbo, D.
    Abstract: The occurrence of extreme observations in a time series depends on the heaviness of the tails of its distribution. The paper proposes a dynamic conditional score model (DCS) for modelling dynamic shape parameters that govern the tail index. The model is based on the Generalised t family of conditional distributions, allowing for the presence of asymmetric tails and therefore the possibility of specifying different dynamics for the left and right tail indices. The paper examines through simulations both the convergence properties of the model and the implications of the link functions used. In addition the paper introduces and studies the size and power properties of a new Lagrange Multiplier (LM) test based on fitted scores to detect the presence of dynamics in the tail index parameter. The paper also shows that the novel LM test is more effective than existing tests based on fitted scores. The model is fitted to Equity Indices and Credit Default Swaps returns. It is found that the tail index for equities has dynamics driven mainly by either the upper or lower tail depending if leverage is taken or not into account. In the case of Credit Default Swaps the test identifies very persistent dynamics for both the tails. Finally the implications of dynamic tail indices for the estimated conditional distribution are assessed in terms of conditional distribution forecasting showing that the novel model predicts more accurately expected shortfalls and value-at-risk than existing models.
    Keywords: Heavy Tailed Distributions, Extreme Events, Score-Driven Models, Tail Index, Lagrange Multiplier Test, Financial Markets
    JEL: C12 C18 C51 C52 C46 C58 G12
    Date: 2021–01–29
  4. By: Maria Teresa Medeiros Garcia; Liane Costa Gabriel
    Abstract: The level of financing of pension fundsand the inherent risk of default is an issue which has assumed increasing relevance, due to the difficulties that pension funds have beenfacingover recent years, which mainly result from changes in demographic conditions, such as the ageingof the populationand increasing longevity, compounded by the 2008 financial crisis and the Great Recession. Asset Liability Management (ALM) models can be employed to optimiseassetsandliabilities, and at the same time minimisethe risks of a fund, whereby the choice of the best model for a fund dependson the fund’sspecificcharacteristics and risk-return profile. This paper is mainly a theoretical study, where a literature reviewis first carried out both on pension plans and pension funds and alsoon the importance of ALM.This is followed by an analysis of the evolution of this risk management instrument and a description of the selected modelsis then presented. To conclude, an analysis of the application of ALM fora pension fund, the Banco de Portugal defined benefit pension fund, is carried out.
    Keywords: Pension Funds, Pension Plans, Asset Liability Management, Risk Management, Funding Ratio
    Date: 2021–01
  5. By: Sayuj Choudhari; Richard Licheng Zhu
    Abstract: In normal times, it is assumed that financial institutions operating in non-overlapping sectors have complementary and distinct outcomes, typically reflected in mostly uncorrelated outcomes and asset returns. Such is the reasoning behind common "free lunches" to be had in investing, like diversifying assets across equity and bond sectors. Unfortunately, the recurrence of crises like the Great Financial Crisis of 2007-2008 demonstrate that such convenient assumptions often break down, with dramatic consequences for all financial actors. In hindsight, the emergence of systemic risk (as exemplified by failure in one part of a system spreading to ostensibly unrelated parts of the system) has been explained by narratives such as deregulation and leverage. But can we diagnose and quantify the ongoing emergence of systemic risk in financial systems? In this study, we focus on two previously-documented measures of systemic risk that require only easily available time series data (eg monthly asset returns): cross-correlation and principal component analysis. We apply these tests to daily and monthly returns on hedge fund indexes and broad-based market indexes, and discuss their results. We hope that a frank discussion of these simple, non-parametric measures can help inform legislators, lawmakers, and financial actors of potential crises looming on the horizon.
    Date: 2021–01
  6. By: Stefano Giglio; Bryan T. Kelly; Johannes Stroebel
    Abstract: We review the literature studying interactions between climate change and financial markets. We first discuss various approaches to incorporating climate risk in macro-finance models. We then review the empirical literature that explores the pricing of climate risks across a large number of asset classes including real estate, equities, and fixed income securities. In this context, we also discuss how investors can use these assets to construct portfolios that hedge against climate risk. We conclude by proposing several promising directions for future research in climate finance.
    JEL: G0
    Date: 2020–12
  7. By: Will Kerry
    Abstract: This paper measures the performance of different metrics in assessing banking system vulnerabilities. It finds that metrics based on equity market valuations of bank capital are better than regulatory capital ratios, and other metrics, in spotting banks that failed (bad apples). This paper proposes that these market-based ratios could be used as a surveillance tool to assess vulnerabilities in the banking sector. While the measures may provide a somewhat fuzzy signal, it is better to have a strategy for identifying bad apples, even if sometimes the apples turn out to be fine, than not being able to spot any bad apples before the barrel has been spoiled.
    Keywords: Banking;Capital adequacy requirements;Credit default swap;Distressed institutions;Stock markets;WP,bank,market,problem bank
    Date: 2019–08–16
  8. By: Olkhov, Victor
    Abstract: This paper discusses the value-at-risk (VaR) concept and assesses the financial adequacy of the price probability determined by frequency of trades at price p. We take the price definition as the ratio of executed trade value to volume and show that it leads to price statistical moments, which differ from those, generated by frequency price probability. We derive the price n-th statistical moments as ratio of n-th statistical moments of the value and the volume of executed transactions. We state that the price probability determined by frequency of trades at price p doesn’t describe probability of executed trade prices and VaR based on frequency price probability may be origin of unexpected and excessive losses. We explain the need to replace frequency price probability by frequency probabilities of the value and the volume of executed transactions and derive price characteristic function. After 50 years of the VaR usage main problems of the VaR concept are still open. We believe that VaR commitment to forecast the price probability for the time horizon T seems to be one of the most tough and expensive puzzle of modern finance.
    Keywords: value-at-risk; risk measure; price probability; market trades
    JEL: C02 D46 D81 G1 G11 G12 G17
    Date: 2021–01–21
  9. By: Lorenzo Bretscher (University of Lausanne and Swiss Finance Institute); Peter Feldhütter (Copenhagen Business School); Andrew Kane (Duke University, Fuqua School of Business, Students); Lukas Schmid (University of Southern California - Marshall School of Business)
    Abstract: Models of capital structure and credit risk make predictions about market valuations of debt, but are routinely tested on the basis of book debt from common data sources. In this paper, we propose to close this gap. We construct a rich data set on firm level debt market valuations by carefully matching data on corporate bond and loan secondary market transactions. We document significant discrepancies between market and book values, especially for distressed firms. We use our dataset to i) provide novel rules of thumb on how to adjust leverage and unlever returns using standard datasets, and ii) to revisit a number of prominent empirical patterns involving corporate debt. Using a market-based measure of Tobin's Q, we find little evidence for investment cash-flow sensitivity in our data. We find that using market debt values significantly improves default prediction, and do not detect a credit spread puzzle. In asset pricing tests, we find a leverage premium, but no evidence for a value premium after controlling for market leverage. Moreover, a novel measure of financial distress, namely market-to-book debt, predicts stock returns positively in the cross-section, inconsistent with a financial distress puzzle.
    Keywords: Corporate Debt Valuations, Tobin's Q, Leverage Premium
    Date: 2021–01
  10. By: Ben Boukai
    Abstract: We consider Heston's (1993) stochastic volatility model for valuation of European options to which (semi) closed form solutions are available and are given in terms of characteristic functions. We prove that the class of scale-parameter distributions with mean being the forward spot price satisfies Heston's solution. Thus, we show that any member of this class could be used for the direct risk-neutral valuation of the option price under Heston's SV model. In fact, we also show that any RND with mean being the forward spot price that satisfies Hestons' option valuation solution, must be a member of a scale-family of distributions in that mean. As particular examples, we show that one-parameter versions of the {\it Log-Normal, Inverse-Gaussian, Gamma, Weibull} and the {\it Inverse-Weibull} distributions are all members of this class and thus provide explicit risk-neutral densities (RND) for Heston's pricing model. We demonstrate, via exact calculations and Monte-Carlo simulations, the applicability and suitability of these explicit RNDs using already published Index data with a calibrated Heston model (S\&P500, Bakshi, Cao and Chen (1997), and ODAX, Mr\'azek and Posp\'i\v{s}il (2017)), as well as current option market data (AMD).
    Date: 2021–01
  11. By: Mark J. Jensen
    Abstract: One of the many lessons learned from the financial crisis is the increased awareness of model risk. In this article, I apply the best practices of model risk management found in SR 11-7 (which offers regulatory guidance on the best practices for managing model risk) to COVID-19 models. In particular, I investigate the Institute of Health Metrics and Evaluation’s (IHME) model to see if it has been effectively challenged with a critical assessment of its conceptual soundness, ongoing monitoring, and outcomes analysis.
    Keywords: COVID-19; model risk management; SR 11-7
    JEL: C1 C11 C52
    Date: 2020–06–18
  12. By: Bastien Baldacci
    Abstract: We present a Hawkes modeling of the volatility surface's high-frequency dynamics and show how the Hawkes kernel coefficients govern the surface's skew and convexity. We provide simple sufficient conditions on the coefficients to ensure no-arbitrage opportunities of the surface. Moreover, these conditions reduce the number of the kernel's parameters to estimate. Finally, we show that at the macroscopic level, the surface is driven by a sum of risk factors whose volatility processes are rough.
    Date: 2020–12
  13. By: Sakai Ando
    Abstract: This paper studies whether bilateral international financial connection data help predict bilateral stock return comovement. It is shown that, when the United States is chosen as the benchmark, a larger U.S. portfolio investment asset position on the destination economy predicts a stronger stock return comovement between them. For large economies such as the United States and Germany, the portfolio investment position is also the best predictor among other connection variables. The paper discusses with a simple general equilibrium portfolio model that the empirical pattern is consistent with the behavior of index investors who trade in response to risk-on/risk-off shocks.
    Keywords: Stocks;Portfolio investment;Stock markets;Foreign direct investment;Bond yields;WP,risk tolerance
    Date: 2019–08–22
  14. By: Bruno Bouchard; Gr\'egoire Loeper; Xiaolu Tan
    Abstract: Using Dupire's notion of vertical derivative, we provide a functional (path-dependent) extension of the It\^o's formula of Gozzi and Russo (2006) that applies to C^{0,1}-functions of continuous weak Dirichlet processes. It is motivated and illustrated by its applications to the hedging or superhedging problems of path-dependent options in mathematical finance, in particular in the case of model uncertainty
    Date: 2021–01
  15. By: Maria Demertzis; Marta Domínguez-Jiménez; Annamaria Lusardi; Bruegel
    Abstract: • The concept of household financial fragility emerged in the United States after the 2007-2008 financial crisis. It grew out of the need to understand whether households’ lack of capacity to face shocks could itself become a source of financial instability, in addition to risks to the stability of banks and the greater financial system. The concept goes beyond assessing the level of assets and encompasses the state of household...
    Date: 2020–07
  16. By: Bernard Dumas; Tymur Gabuniya; Richard C. Marston
    Abstract: The distinction between domicile and place of business is becoming more and more relevant as a growing number of firms have activities abroad. In most statistical studies of international stock returns, a firm is included in a country’s index if its headquarters are located in that country. This classification scheme ignores the operations of the firm. We propose, instead, to measure the firms’ exposures to “geographic zones” according to the place where they conduct business. As a representation of “geographic risks”, we synthesize zone factors from all firms in the dataset, be they domestic firms or multinationals. And we show the properties of the exposures to the zone factors.
    JEL: G32
    Date: 2020–12
  17. By: Jeon, Bang (School of Economics); Yao, Yao (Research Institute of Economics and Management); Chen, Minghua (Research Institute of Economics and Management); Wu, Ji (Research Institute of Economics and Management)
    Abstract: This paper examines the impact of macroprudential policies on bank risk under economic uncertainty in emerging Asian economies. By using bank-level panel data for selected emerging Asian economies during the period 2000-2016, we present consistent evidence that bank risk increases with economic uncertainty, while macroprudential measures play an ameliorative role to the uncertainty-induced bank risk. We confirm that these findings are robust against a series of alternative measures of economic uncertainty and bank risk, and alternative econometric tools to address possible endogeneity concerns.
    Keywords: Economic uncertainty; bank risk; macroprudential policy; emerging Asia
    JEL: G15 G21
    Date: 2021–01–16
  18. By: Giovanni Pellegrino (Aarhus University); Efrem Castelnuovo (University of Melbourne and University of Padova); Giovanni Caggiano (Monash University and University of Padova)
    Abstract: How damaging are uncertainty shocks during extreme events such as the great recession and the Covid-19 outbreak? Can monetary policy limit output losses in such situations? We use a nonlinear VAR framework to document the large response of real activity to a financial uncertainty shock during the great recession. We replicate this evidence with an estimated DSGE framework featuring a concept of uncertainty comparable to that in our VAR. We employ the DSGE model to quantify the impact on real activity of an uncertainty shock under different Taylor rules estimated with normal times vs. great recession data (the latter associated with a stronger response to output). We find that the uncertainty shock-induced output loss experienced during the 2007-09 recession could have been twice as large if policymakers had not responded aggressively to the abrupt drop in output in 2008Q3. Finally, we use our estimated DSGE framework to simulate different paths of uncertainty associated to different hypothesis on the evolution of the coronavirus pandemic. We find that: i) Covid-19-induced uncertainty could lead to an output loss twice as large as that of the great recession; ii) aggressive monetary policy moves could reduce such loss by about 50%.
    Keywords: Uncertainty shock, nonlinear IVAR, nonlinear DSGE framework, minimum-distance estimation, great recession, Covid-19
    JEL: C22 E32 E52
    Date: 2020–08
  19. By: Min Shu; Ruiqiang Song; Wei Zhu
    Abstract: We employed the log-periodic power law singularity (LPPLS) methodology to systematically investigate the 2020 stock market crash in the U.S. equities sectors with different levels of total market capitalizations through four major U.S. stock market indexes, including the Wilshire 5000 Total Market index, the S&P 500 index, the S&P MidCap 400 index, and the Russell 2000 index, representing the stocks overall, the large capitalization stocks, the middle capitalization stocks and the small capitalization stocks, respectively. During the 2020 U.S. stock market crash, all four indexes lost more than a third of their values within five weeks, while both the middle capitalization stocks and the small capitalization stocks have suffered much greater losses than the large capitalization stocks and stocks overall. Our results indicate that the price trajectories of these four stock market indexes prior to the 2020 stock market crash have clearly featured the obvious LPPLS bubble pattern and were indeed in a positive bubble regime. Contrary to the popular belief that the COVID-19 led to the 2020 stock market crash, the 2020 U.S. stock market crash was endogenous, stemming from the increasingly systemic instability of the stock market itself. We also performed the complementary post-mortem analysis of the 2020 U.S. stock market crash. Our analyses indicate that the 2020 U.S. stock market crash originated from a bubble which began to form as early as September 2018; and the bubbles in stocks with different levels of total market capitalizations have significantly different starting time profiles. This study not only sheds new light on the making of the 2020 U.S. stock market crash but also creates a novel pipeline for future real-time crash detection and mechanism dissection of any financial market and/or economic index.
    Date: 2021–01
  20. By: Besley, Timothy; Roland, Isabelle; Van Reenen, John
    Abstract: This paper studies the implications of perceived default risk for aggregate output and productivity. Using a model of credit contracts with moral hazard, we show that a firm’s probability of default is a sufficient statistic for capital allocation. The theoretical framework suggests an aggregate measure of the impact of credit market frictions based on firm-level probabilities of default which can be applied using data on firmlevel employment and default risk. We obtain direct estimates of firm-level default probabilities using Standard and Poor’s PD Model to capture the expectations that lenders were forming based on their historical information sets. We implement the method on the UK, an economy that was strongly exposed to the global financial crisis and where we can match default probabilities to administrative data on the population of 1.5 million firms per year. As expected, we find a strong correlation between default risk and a firm’s future performance. We estimate that credit frictions (i) cause an output loss of around 28% per year on average; (ii) are much larger for firms with under 250 employees and (iii) that losses are overwhelmingly due to a lower overall capital stock rather than a misallocation of credit across firms with heterogeneous productivity. Further, we find that these losses accounted for over half of the productivity fall between 2008 and 2009, and persisted for smaller (although not larger) firms.
    Keywords: productivity; default risk; credit frictions; misallocation
    JEL: D24 E32 L11 O47
    Date: 2020–01
  21. By: Lemeunier, Sébastien
    Abstract: The Church has published a text of reference related to finance titled "OEconomicae et pecuniariae quaestiones," which aims to point out the weaknesses of the financial system and propose solutions. In order to test these remarks, we distributed them in Merton’s (1995) functions for the financial system and verified whether these critiques actually challenge them. We found that the Church focuses its criticisms on the functions of risk management and conflicts of interest, and recommends being vigilant about the information function. Finally, by grouping the solutions, there emerges a coherent and complementary approach to the financial system based on the transparency of information.
    Keywords: Financial Economics ; Religion ; Welfare economics Code JEL : P 43 ; G18 ; Z12 ; D6
    JEL: D6 D64 G28 P43 Z12
    Date: 2021–01–13
  22. By: Florian Gach; Simon Hochgerner
    Abstract: In the context of traditional life insurance, the future discretionary benefits ($FDB$), which are a central item for Solvency~II reporting, are generally calculated by computationally expensive Monte Carlo algorithms. We derive analytic formulas for lower and upper bounds for the $FDB$. This yields an estimation interval for the $FDB$, and the average of lower and upper bound is a simple estimator. These formulae are designed for real world applications, and we compare the results to publicly available reporting data.
    Date: 2021–01

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