nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒02‒13
twenty papers chosen by

  1. Diversification quotients based on VaR and ES By Xia Han; Liyuan Lin; Ruodu Wang
  2. Pricing and hedging of longevity basis risk through securitization By Zeddouk, Fadoua; Devolder, Pierre
  3. A fractional Hawkes process for illiquidity modeling By Dupret, Jean-Loup; Hainaut, Donatien
  4. High-frequency realized stochastic volatility model By Watanabe, Toshiaki; Nakajima, Jouchi
  5. Liquidity Regulation and Bank Risk Taking on the Horizon By Joshua Bosshardt; Ali Kakhbod; Farzad Saidi
  6. Nowcasting Stock Implied Volatility with Twitter By Thomas Dierckx; Jesse Davis; Wim Schoutens
  7. Stochastic Modellization of Hybrid Public Pension Plans (PAYG) under Demographic Risks with Application to the Belgian Case By Al-Hassan, Hassana; Devolder, Pierre
  8. Bank manager sentiment, loan growth and bank risk By Brückbauer, Frank; Cezanne, Thibault
  9. Tenant Riskiness, Contract Length, and the Term Structure of Commercial Leases By Jan K. Brueckner; Stuart S. Rosenthal
  10. Achieving a Given Financial Goal with Optimal Deferred Term Insurance Purchasing Policy By Yuqi Li; Lihua Zhang
  11. Penerapan Manajemen Risiko Operasional Unit Customer Service PT. Bank Nagari Cabang Alahan Panjang By Putri, Dini Fonika; Afriyeni, Afriyeni; fernos, jhon
  12. Risk Amplification Macro Model (RAMM) By Kerem Tuzcuoglu
  13. Inequality and Risk Preference By Pickard, Harry; Dohmen, Thomas; van Landeghem, Bert
  14. Resiko Operasional Unit Teller Dan Customer Service Pada PT. BPR Ophir Pasaman Barat By Wulandari, Ratna; Afriyeni, Afriyeni; fernos, jhon
  15. Hedging of European type contingent claims in discrete time binomial market models By Jarek K\k{e}dra; Assaf Libman; Victoria Steblovskaya
  16. Artificial Intelligence & Machine Learning in Finance: A literature review By Wassima Lakhchini; Rachid Wahabi; Mounime El Kabbouri; Casa Bp; Settat Hassan
  17. Three layers of uncertainty By Ilke Aydogan; Loïc Berger; Valentina Bosetti; Ning Liu
  18. Subjective Risk Valuation and Behavioral Change : Evidence from COVID-19 in the U.K. and Japan By Masayuki SATO; Shin KINOSHITA; Takanori IDA
  19. Collateral Cycles By Evangelos Benos; Gerardo Ferrara; Angelo Ranaldo
  20. Autocalibration by balance correction in nonlife insurance pricing By Denuit, Michel; Trufin, Julien

  1. By: Xia Han; Liyuan Lin; Ruodu Wang
    Abstract: The diversification quotient (DQ) is a recently introduced tool for quantifying the degree of diversification of a stochastic portfolio model. It has an axiomatic foundation and can be defined through a parametric class of risk measures. Since the Value-at-Risk (VaR) and the Expected Shortfall (ES) are the most prominent risk measures widely used in both banking and insurance, we investigate DQ constructed from VaR and ES in this paper. In particular, for the popular models of multivariate elliptical and multivariate regular varying (MRV) distributions, explicit formulas are available. The portfolio optimization problems for the elliptical and MRV models are also studied. Our results further reveal favourable features of DQ, both theoretically and practically, compared to traditional diversification indices based on a single risk measure.
    Date: 2023–01
  2. By: Zeddouk, Fadoua (Université catholique de Louvain, LIDAM/ISBA, Belgium); Devolder, Pierre (Université catholique de Louvain, LIDAM/ISBA, Belgium)
    Abstract: Hedging the basis risk is a challenging issue for pension funds and insurers, who can be interested in longevity-linked securities to transfer their longevity risk. These derivatives are based on a given population data rather than their own policy data, which may lead to a potential loss due to data mismatch. In this paper we propose a pricing approach under Solvency II to evaluate the longevity basis risk through securitization, by associating this risk to the payo􏰀 of a longevity derivative. This method is then compared to other classical pricing methods used in finance. We assess and analyze di􏰀erent hedging strategies for 􏰁rms facing basis risk, using a multipopulation model based on a two-dimensional Hull and White model to represent the evolution of mortality over time.
    Keywords: Stochastic longevity risk ; longevity-linked securities ; Cost of Capital ; basis risk ; Solvency Capital Requirement ; multi-population mortality model
    Date: 2022–12–13
  3. By: Dupret, Jean-Loup (Université catholique de Louvain, LIDAM/ISBA, Belgium); Hainaut, Donatien (Université catholique de Louvain, LIDAM/ISBA, Belgium)
    Abstract: The Amihud illiquidity measure has proven to be very popular in the empirical literature for measuring the illiquidity process of stocks and indices. Many econometric models in discrete time have then been proposed for this Amihud measure. Such models are however not adapted for reproduc- ing peaks of illiquidity with long-memory, for risk management and for pricing liquidity-related derivatives. This paper therefore proposes a new paradigm for modeling illiquidity via a continuous- time process with jumps exhibiting long-range dependence. More precisely, we first introduce a new fractional Hawkes process in which the intensity process is ruled by a modified Mittag-Leffler excitation function. Working with a mean-reverting jump model for the (log-)Amihud measure where jumps follow this modified fractional Hawkes process then allows to easily reproduce the observed peaks of illiquidity in financial markets while introducing long-range dependence and tractability in the model. Indeed, thanks to this modified Mittag-Leffler kernel, we show that our model for the (log)-Amihud measure admits a characteristic function in semi-closed form while having a long-memory of past events, which is not achievable with the existing Hawkes processes. We can therefore use this model to perform risk management on illiquidity as well as to introduce and price illiquidity derivatives on the Amihud measure. We hence provide with this paper new tools for a better understanding and management of the illiquidity risk in financial markets.
    Keywords: Illiquidity modeling ; Amihud measure ; Hawkes process ; Mittag-Leffler function ; Illiquidity derivatives ; Risk management
    Date: 2023–01–01
  4. By: Watanabe, Toshiaki; Nakajima, Jouchi
    Abstract: A new high-frequency realized stochastic volatility model is proposed. Apart from the standard daily-frequency stochastic volatility model, the high-frequency stochastic volatility model is fit to intraday returns by extensively incorporating intraday volatility patterns. The daily realized volatility calculated using intraday returns is incorporated into the high-frequency stochastic volatility model by considering the bias in the daily realized volatility caused by microstructure noise. The volatility of intraday returns is assumed to consist of the autoregressive process, the seasonal component of the intraday volatility pattern, and the announcement component responding to macroeconomic announcements. A Bayesian method via Markov chain Monte Carlo is developed for the analysis of the proposed model. The empirical analysis using the 5-minute returns of E-mini S&P 500 futures provides evidence that our high-frequency realized stochastic volatility model improves in-sample model fit and volatility forecasting over the existing models.
    Keywords: Bayesian analysis, High-frequency data, Markov chain Monte Carlo, Realized volatility, Stochastic volatility model, Volatility forecasting
    JEL: C22 C53 C58 G17
    Date: 2023–01
  5. By: Joshua Bosshardt; Ali Kakhbod; Farzad Saidi
    Abstract: We examine how banks’ liquidity requirements affect their incentives to take risk with their remaining illiquid assets. Our model predicts that banks with more stable liabilities are more likely to engage in risk taking in response to tighter liquidity requirements. This prediction is borne out in transaction-level data on corporate and mortgage loans for U.S. banks subject to the liquidity coverage ratio (LCR). For identification, we exploit variation in long-term bank bonds held by insurance companies that are not affected by the LCR. Our results point to a trade-off between bank risk taking and ensuring funding resilience over different horizons.
    Keywords: Liquidity Regulation, Bank Risk Taking, Insurance Sector, LCR, NSFR
    JEL: G20 G21 G22 G28
    Date: 2023–01
  6. By: Thomas Dierckx; Jesse Davis; Wim Schoutens
    Abstract: In this study, we predict next-day movements of stock end-of-day implied volatility using random forests. Through an ablation study, we examine the usefulness of different sources of predictors and expose the value of attention and sentiment features extracted from Twitter. We study the approach on a stock universe comprised of the 165 most liquid US stocks diversified across the 11 traditional market sectors using a sizeable out-of-sample period spanning over six years. In doing so, we uncover that stocks in certain sectors, such as Consumer Discretionary, Technology, Real Estate, and Utilities are easier to predict than others. Further analysis shows that possible reasons for these discrepancies might be caused by either excess social media attention or low option liquidity. Lastly, we explore how our proposed approach fares throughout time by identifying four underlying market regimes in implied volatility using hidden Markov models. We find that most added value is achieved in regimes associated with lower implied volatility, but optimal regimes vary per market sector.
    Date: 2022–12
  7. By: Al-Hassan, Hassana (Univerisity of Mines and Technology); Devolder, Pierre (Université catholique de Louvain, LIDAM/ISBA, Belgium)
    Abstract: Aging is an important challenge for pension schemes, especially for social security plans mainly 􏰂nanced by PAYG (Pay as you go) and based on a DB formula (De􏰂ned Bene􏰂t). In particular, demographic risks induce important increases of the contributions and threaten the 􏰂nancial sustainability of such schemes. On the other hand, switching to De􏰂ned Contribution plans can be a solution in terms of funding but introduce signi􏰂cant risks in terms of social adequacy. The purpose of this paper is to study hybrid solutions between DB and DC in a stochastic environment. In particular, we simulate for various risk sharing strategies, the evolution of contributions and bene􏰂ts by introducing risk factors in􏰃uencing the demographic dependence ratio (fertility, longevity, baby boom). We study the mean evolution of these processes as well as their value at risk.
    Keywords: PAYG Pensions ; Dependency Ratio ; Musgrave ; Convex Combination ; Value at Risk
    Date: 2022–12–20
  8. By: Brückbauer, Frank; Cezanne, Thibault
    Abstract: We build a textual score measuring the tone of bank earnings press release documents. We use this measure to define bank manager sentiment as the variation in the textual tone score which is orthogonal to bank-specific and macroeconomic fundamentals. Using this definition of sentiment, we present evidence on how bank managers' systematic overoptimism affects the amount of credit that they supply to the real sector. Our empirical evidence suggests that decisions on the volume of new loans partially depend on past realizations of economic fundamentals, implying that loan growth and contemporaneous economic fundamentals might be systematically disconnected. Furthermore, we show that over-optimism on the part of bank managers spills over to their equity investors, who seem to perceive banks with high bank manager sentiment as having a lower systemic risk.
    Keywords: sentiment, text data, extrapolation, loan growth, systemic risk
    JEL: G00 G10 G21 G41
    Date: 2022
  9. By: Jan K. Brueckner; Stuart S. Rosenthal
    Abstract: This paper explores the connection between tenant riskiness, commercial lease length and the term structure of lease contracts. Theory shows that the possibility of default on a long-term lease generates a risk/lease-length connection. The empirical work uses a large CompStak lease dataset combined with tenant characteristics (including risk) from Dun & Bradstreet. Regressions show that lease length is inversely related to the D&B risk measures, as predicted, and that risky tenants pay a higher rent premium for long-term contracts than low-risk tenants. The presence of such tenants thus raises the slope of the term structure of commercial rents.
    Keywords: lease length, tenant riskiness, rent term structure
    JEL: R30 M20
    Date: 2022
  10. By: Yuqi Li; Lihua Zhang
    Abstract: This paper researches the problem of purchasing deferred term insurance in the context of financial planning to maximize the probability of achieving a personal financial goal. Specifically, our study starts from the perspective of hedging death risk and longevity risk, and considers the purchase of deferred term life insurance and deferred term pure endowment to achieve a given financial goal for the first time in both deterministic and stochastic framework. In particular, we consider income, consumption and risky investment in the stochastic framework, extending previous results in \cite{Bayraktar2016}. The time cutoff m and n make the work more difficult. However, by establishing new controls, ``\emph{quasi-ideal value}" and``\emph{ideal value}", we solve the corresponding ordinary differential equations or stochastic differential equations, and give the specific expressions for the maximum probability. Then we provide the optimal life insurance purchasing strategies and the optimal risk investment strategies. In general, when m \geqslant 0, n>0, deferred term insurance or term life insurance is a better choice for those who want to achieve their financial or bequest goals but are not financially sound. In particular, if m >0, n \rightarrow \infty, our viewpoint also sheds light on reaching a bequest goal by purchasing deferred whole life insurance. It is worth noting that when m=0, n \rightarrow \infty, our problem is equivalent to achieving the just mentioned bequest goal by purchasing whole life insurance, at which point the maximum probability and the life insurance purchasing strategies we provide are consistent with those in \cite{Bayraktar2014, Bayraktar2016}.
    Date: 2022–12
  11. By: Putri, Dini Fonika; Afriyeni, Afriyeni; fernos, jhon
    Abstract: This research was conducted to find out how the implementation of operational risk management in the customer service unit at PT Bank Nagari Cabang Alahan Panjang. The method used in this research is the interview method with the customer service unit and the qualitative method by systematically parsing the data from the facts that occurred. based on the results of this study it was found that PT. Bank Nagari Cabang Alahan Panjang has implemented operational risk management of the customer service unit, to prevent complaints from customers, errors in inputting customer data, errors in printing customer passbooks which resulted in damaged books.
    Date: 2022–12–11
  12. By: Kerem Tuzcuoglu
    Abstract: The Risk Amplification Macro Model (RAMM) is a new nonlinear two-country dynamic model that captures rare but severe adverse shocks. Tail risk arises from heightened financial stress abroad or in Canada that triggers a regime change with a negative feedback loop to the real economy. We rely on a combination of sign, zero and elasticity restrictions to identify structural shocks. The foreign block (global and US variables) impacts the domestic block (a large number of Canadian macrofinancial variables), but not vice-versa. Simulations suggest that tighter financial conditions in the United States can spill over to Canada, and a regime change in macrofinancial elasticities provides a good replication of economic downturns. The RAMM can be used to assess the financial stability implications of both domestic and foreign-originated risk scenarios.
    Keywords: Business fluctuations and cycles; Econometric and statistical methods; Financial stability; Monetary policy transmission
    JEL: C51 E37 E44 F44
    Date: 2023
  13. By: Pickard, Harry (Newcastle University); Dohmen, Thomas (University of Bonn and IZA); van Landeghem, Bert (University of Sheffield)
    Abstract: This paper studies the relationship between income inequality and risk taking. Increased income inequality is likely to enlarge the scope for upward comparisons and, in the presence of reference-dependent preferences, to increase willingness to take risks. Using a globally representative dataset on risk preference in 76 countries, we empirically document that the distribution of income in a country has a positive and significant link with the preference for risk. This relationship is remarkably precise and holds across countries and individuals, as well as alternate measures of inequality. We find evidence that individuals who are more able to understand inequality and individuals who fall behind their inherent point of reference increase their preference for risk. Two complementary instrumental variable approaches support a causal interpretation of our results.
    Keywords: income inequality, risk preference, risk sensitivity
    JEL: D91 O15 D81 D01
    Date: 2023–01
  14. By: Wulandari, Ratna; Afriyeni, Afriyeni; fernos, jhon
    Abstract: The purpose of this study is to find out how the implementation of operational risk that occurs in the teller and customer service units at PT.BPR Ophir Pasaman Barat. The method used in this study is the interview method with the teller and customer service units with a qualitative method by systematically parsing the data from the facts that occurred. Based on the results of this study it was found that PT. Bank BPR Simpang 3 Ophir Pasaman Barat has implemented operational risk management for teller units and customer service to prevent complaints from customers, errors in inputting customer data, mistakes in printing customer passbooks which resulted in the book being damaged
    Date: 2022–12–12
  15. By: Jarek K\k{e}dra; Assaf Libman; Victoria Steblovskaya
    Abstract: We consider a discrete-time binomial model of a market consisting of $m\geq 1$ risky securities and one bond. For a European type contingent claim we give an explicit formula for the minimum-cost maximal hedging strategy.
    Date: 2023–01
  16. By: Wassima Lakhchini (Université Hassan 1er [Settat], ENCGS - Ecole Nationale de Commerce et de Gestion de SETTAT); Rachid Wahabi (Université Hassan 1er [Settat]); Mounime El Kabbouri (Université Hassan 1er [Settat]); Casa Bp; Settat Hassan
    Abstract: In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers' innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.'s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.
    Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market
    Date: 2022–12–18
  17. By: Ilke Aydogan (IÉSEG School Of Management [Puteaux]); Loïc Berger (CNRS - Centre National de la Recherche Scientifique, IÉSEG School Of Management [Puteaux], EIEE - European Institute on Economics and the Environment, CMCC - Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna]); Valentina Bosetti (Bocconi University [Milan, Italy], EIEE - European Institute on Economics and the Environment, CMCC - Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna]); Ning Liu (BUAA - Beihang University)
    Abstract: We explore decision-making under uncertainty using a framework that decom- poses uncertainty into three distinct layers: (1) risk, which entails inherent random- ness within a given probability model; (2) model ambiguity, which entails uncertainty about the probability model to be used; and (3) model misspecification, which en- tails uncertainty about the presence of the correct probability model among the set of models considered. Using a new experimental design, we isolate and measure attitudes towards each layer separately. We conduct our experiment on three different subject pools and document the existence of a behavioral distinction between the three layers. In addition to providing new insights into the underlying processes behind ambiguity aversion, we provide the first empirical evidence of the role of model misspecification in decision-making under uncertainty.
    Keywords: Ambiguity aversion, model uncertainty, model misspecification, non-expected utility, reduction of compound lotteries
    Date: 2023
  18. By: Masayuki SATO; Shin KINOSHITA; Takanori IDA
    Abstract: This study analyzed people's behavioral changes in the early stages of COVID-19 expansion in relation to subjective probability, and clarified the effect of risk perception on behavioral changes, such as outbound restriction. We conducted a social survey using an Internet survey in the U.K. and Japan in the fall of 2020 and found that the percentage of those who evaluated risk optimistically was higher in the U.K. than in Japan. In addition, we applied seemingly unrelated regression (SUR) for the bivariate ordinal probit model to the association between desired and actual infection prevention behavior and found that a pessimistic bias is likely to lead to behavioral change, whereas an optimistic one is not. These results suggest that when pessimistic bias is strong, measures that respect people’s rights, such as freedom of action, while leaving people to be autonomous, can be effective to some extent. In contrast, when optimistic bias is strong, the use of a certain degree of coercive force may be unavoidable from the standpoint of public interest.
    Keywords: COVID-19, Bayesian Inference, Subjective probability, Seemingly Unrelated Regression
    Date: 2022–11
  19. By: Evangelos Benos (University of Nottingham); Gerardo Ferrara (Bank of England); Angelo Ranaldo (University of St. Gallen; Swiss Finance Institute)
    Abstract: Using supervisory data from UK clearinghouses (CCPs), we document the presence of a collateral cycle in which cash goes back and forth from financial markets to CCPs. In the onward phase, clearing members provide cash to CCPs to meet margin requirements. This pattern is procyclical as the pledged collateral increases with market volatility and puts upward pressure on repurchase agreement (repo) rates. In the backward phase, CCPs return the cash to the financial markets via reverse repos and bond purchases, in compliance with regulation that requires them to collateralise their cash holdings. The cash given back by CCPs generates downward pressure on repo rates in a counter-cyclical manner.
    Keywords: Central clearing, margin procyclicality, repo rates
    JEL: G10 G12 G14
    Date: 2022–12
  20. By: Denuit, Michel (Université catholique de Louvain, LIDAM/ISBA, Belgium); Trufin, Julien (ULB)
    Abstract: By exploiting massive amounts of data, machine learning techniques provide actuaries with predictors exhibiting high correlation with claim frequencies and severities. However, these predictors generally fail to achieve financial equilibrium and thus do not qualify as pure premiums. Autocalibration effectively addresses this issue since it ensures that every group of policyholders paying the same premium is on average self-financing, as demonstrated by Denuit et al. (2021), Ciatto et al. (2022), Lindholm et al. (2022) and Wüthrich (2022). These authors proposed balance correction as a way to make any candidate premium autocalibrated. The present paper further studies the effect of balance correction on resulting pure premiums. It is shown that this method is also beneficial in terms of out-of-sample, or predictive Tweedie deviance, Bregman divergence as well as concentration curves. The paper then derives conditions ensuring that the initial predictor and its balance-corrected version are ordered in Lorenz order. Finally, criteria are proposed to rank the balance-corrected versions of two competing predictors in the convex order.
    Keywords: Tweedie deviance ; Bregman divergence ; financial equilibrium ; convex order ; Lorenz order
    Date: 2022–12–15

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.