nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒01‒31
twenty papers chosen by
Stan Miles
Thompson Rivers University

  1. Optimal Portfolio Choice and Stock Centrality for Tail Risk Events By Christis Katsouris
  2. A Survey of Hedge and Safe Havens Assets against G-7 Stock Markets before and during the COVID-19 Pandemic By Ozdemir, Huseyin; Ozdemir, Zeynel Abidin
  3. Foreign Vulnerabilities, Domestic Risks: The Global Drivers of GDP-at-Risk By Lloyd, S.; Manuel, E.; Panchev, K.
  4. Derivatives-based portfolio decisions. An expected utility insight By Marcos Escobar-Anel; Matt Davison; Yichen Zhu
  5. Hedging Cryptocurrency Options By Jovanka Lili Matic; Natalie Packham; Wolfgang Karl H\"ardle
  6. Unification of different systemic risk measures and Aumann-Shapley allocations By Ludger Overbeck; Florian Schindler
  7. How Does Risk Hedging Impact Operations? Insights from a Price-Setting Newsvendor Model By Liao Wang; Jin Yao; Xiaowei Zhang
  8. A Welfare Analysis on Start-Up Decisions under Default Risk By Nicola Comincioli; Paolo Panteghini; Sergio Vergalli
  9. The Financial Origins of Non-fundamental Risk By Sushant Acharya; Keshav Dogra; Sanjay Singh
  10. Compensatory model for quantile estimation and application to VaR By Shuzhen Yang
  11. Conditional capital surplus and shortfall across renewable and non-renewable resource firms By Denny Irawan; Tatsuyoshi Okimoto
  12. Modeling Time-to-Event Contingent Cash Flows: A Discrete-Time Survival Analysis Approach By Jackson P. Lautier; Vladimir Pozdnyakov; Jun Yan
  13. Modelling the volatility of Bitcoin returns using Nonparametric GARCH models By Mestiri, Sami
  14. Tenant Industry Sector and European Listed Real Estate Performance By Jan Muckenhaupt; Martin Hoesli; Bing Zhu
  15. On the decomposition of an insurer's profits and losses By Marcus C. Christiansen
  16. Conclusions of the Second European Conference on Risk Perception, Behaviour, Management and Response - ENCORE 2021 By Samuel Rufat; Odile Plattard; Alexander Fekete; Ludivine Gilli; Paul Hudson; Victor Santoni
  17. Rational expectations as a tool for predicting failure of weighted k-out-of-n reliability systems By Jorgen Vitting Andersen; Roy Cerqueti; Jessica Riccioni
  18. Deep Partial Hedging By Songyan Hou; Thomas Krabichler; Marcus Wunsch
  19. Accelerated American Option Pricing with Deep Neural Networks By David Anderson; Urban Ulrych
  20. Rainbow Options under Bayesian MS-VAR Process By Battulga Gankhuu

  1. By: Christis Katsouris
    Abstract: We propose a novel risk matrix to characterize the optimal portfolio choice of an investor with tail concerns. The diagonal of the matrix contains the Value-at-Risk of each asset in the portfolio and the off-diagonal the pairwise Delta-CoVaR measures reflecting tail connections between assets. First, we derive the conditions under which the associated quadratic risk function has a closed-form solution. Second, we examine the relationship between portfolio risk and eigenvector centrality. Third, we show that portfolio risk is not necessarily increasing with respect to stock centrality. Forth, we demonstrate under certain conditions that asset centrality increases the optimal weight allocation of the asset to the portfolio. Overall, our empirical study indicates that a network topology which exhibits low connectivity is outperformed by high connectivity based on a Sharpe ratio test.
    Date: 2021–12
  2. By: Ozdemir, Huseyin (Gazi University); Ozdemir, Zeynel Abidin (Ankara HBV University)
    Abstract: We propose a new Sharpe ratio index obtained from return and volatility spillover indices to individual assets from the whole financial system. We use our new approach to shed light on a new perspective on a hot topic examining the safe-haven assets after Covid-19. To do that, we compare both hedge and safe-haven properties of gold, Bitcoin, and crude oil against G-7 stock markets by using daily return and volatility data from September 2013 to October 2021. Our empirical findings show that the hedging effectiveness of gold, Bitcoin, and crude oil varies overtime before the Covid-19 pandemic. Furthermore, according to our analysis results, only Bitcoin acts as a safe haven against G-7 stock markets during most of the Covid-19 pandemic time.
    Keywords: sharpe ratio, safe haven, hedge, spillover effect, G-7 countries
    JEL: C58 G10
    Date: 2021–11
  3. By: Lloyd, S.; Manuel, E.; Panchev, K.
    Abstract: We study how foreign financial developments influence the conditional distribution of domestic GDP growth. Within a quantile regression setup, we propose a method to parsimoniously account for foreign vulnerabilities using bilateral-exposure weights when assessing downside macroeconomic risks. Using a panel dataset of advanced economies, we show that tighter foreign financial conditions and faster foreign credit-to-GDP growth are associated with a more severe left tail of domestic GDP growth, even when controlling for domestic indicators. The inclusion of foreign indicators significantly improves estimates of ‘GDP-at-Risk’, a summary measure of downside risks. In turn, this yields time-varying estimates of higher GDP growth moments that are interpretable and provide advanced warnings of crisis episodes. Decomposing historical estimates of GDP-at-Risk into domestic and foreign sources, we show that foreign shocks are a key driver of domestic macroeconomic tail risks.
    Keywords: Financial stability, GDP-at-Risk, International spillovers, Local projections, Quantile regression, Tail risk
    JEL: E44 E58 F30 F41 F44 G01
    Date: 2021–07–30
  4. By: Marcos Escobar-Anel; Matt Davison; Yichen Zhu
    Abstract: This paper challenges the use of stocks in portfolio construction, instead we demonstrate that Asian derivatives, straddles, or baskets could be more convenient substitutes. Our results are obtained under the assumptions of the Black--Scholes--Merton setting, uncovering a hidden benefit of derivatives that complements their well-known gains for hedging, risk management, and to increase utility in market incompleteness. The new insights are also transferable to more advanced stochastic settings. The analysis relies on the infinite number of optimal choices of derivatives for a maximized expected utility (EUT) agent; we propose risk exposure minimization as an additional optimization criterion inspired by regulations. Working with two assets, for simplicity, we demonstrate that only two derivatives are needed to maximize utility while minimizing risky exposure. In a comparison among one-asset options, e.g. American, European, Asian, Calls and Puts, we demonstrate that the deepest out-of-the-money Asian products available are the best choices to minimize exposure. We also explore optimal selections among straddles, which are better practical choices than out-of-the-money Calls and Puts due to liquidity and rebalancing needs. The optimality of multi-asset derivatives is also considered, establishing that a basket option could be a better choice than one-asset Asian call/put in many realistic situations.
    Date: 2022–01
  5. By: Jovanka Lili Matic; Natalie Packham; Wolfgang Karl H\"ardle
    Abstract: The cryptocurrency (CC) market is volatile, non-stationary and non-continuous. This poses unique challenges for pricing and hedging CC options. We study the hedge behaviour and effectiveness for a wide range of models. First, we calibrate market data to SVI-implied volatility surfaces to price options. To cover a wide range of market dynamics, we generate price paths using two types of Monte Carlo simulations. In the first approach, price paths follow an SVCJ model (stochastic volatility with correlated jumps). The second approach simulates paths from a GARCH-filtered kernel density estimation. In these two markets, options are hedged with models from the class of affine jump diffusions and infinite activity L\'evy processes. Including a wide range of market models allows to understand the trade-off in the hedge performance between complete, but overly parsimonious models, and more complex, but incomplete models. Dynamic Delta, Delta-Gamma, Delta-Vega and minimum variance hedge strategies are applied. The calibration results reveal a strong indication for stochastic volatility, low jump intensity and evidence of infinite activity. With the exception of short-dated options, a consistently good performance is achieved with Delta-Vega hedging in stochastic volatility models. Judging on the calibration and hedging results, the study provides evidence that stochastic volatility is the driving force in CC markets.
    Date: 2021–11
  6. By: Ludger Overbeck; Florian Schindler
    Abstract: We study two different contributions to the theory of systemic risk measures. It turns out, that crucial properties are shared by both types and that in most relevant cases both can be included in the axiomatic approach. Moreover, a capital allocation rule (CAR) in the spirit of Aumann-Shapley is introduced which gives us the opportunity to compute systemic capital allocations regardless of the risk measurement approach. Additionally, this CAR yields an alternative approach to find the corresponding counterpart in the axiomatic approach.
    Date: 2021–12
  7. By: Liao Wang; Jin Yao; Xiaowei Zhang
    Abstract: Financial asset price movement impacts product demand and thus influences operational decisions of a firm. We develop and solve a general model that integrates financial risk hedging into a price-setting newsvendor. The optimal hedging strategy is found analytically, which leads to an explicit objective function for optimization of pricing and service levels. We find that, in general, the presence of hedging reduces the optimal price. It also reduces the optimal service level when the asset price trend positively impacts product demand ("asset price benefits demand"), while it may increase the optimal service level by a small margin when the impact is negative ("asset price hurts demand"). We construct the mean-variance efficient frontier that characterizes the risk-return trade-off, and we quantify the risk reduction achieved by the hedging strategy. Our numerical case study using real data of Ford Motor Company shows that the markdown in price and decrease in service level are small under our model, and the hedging strategy substantially reduces risk without materially reducing operational profit.
    Date: 2022–01
  8. By: Nicola Comincioli; Paolo Panteghini; Sergio Vergalli
    Abstract: This short article studies the tax effects on a start-up investment decision under uncertainty. Since the representative firm can decide both when to invest and how much to borrow, the distortive effects are twofold. We thus show that the deadweight loss (namely, the ratio between the welfare loss and tax revenue) ranges from 25 to 32%, whereas mature firms face a lower distortion (as shown by Comincioli et al. (2021) the maximum deadweight loss is about 25%).
    Keywords: real options, business taxation, default risk
    JEL: H25 G33 G38
    Date: 2021
  9. By: Sushant Acharya; Keshav Dogra; Sanjay Singh
    Abstract: We formalize the idea that the financial sector can be a source of non-fundamental risk. Households’ desire to hedge against price volatility can generate price volatility in equilibrium, even absent fundamental risk. Fearing that asset prices may fall, risk-averse households demand safe assets from leveraged intermediaries, whose issuance of safe assets exposes the economy to self-fulfilling fire sales. Policy can eliminate non-fundamental risk by (i) increasing the supply of publicly backed safe assets, through issuing government debt or bailing out intermediaries, or (ii) reducing the demand for safe assets, through social insurance or by acting as a market maker of last resort.
    Keywords: Business fluctuations and cycles; Inflation and prices; Monetary policy
    JEL: D52 D84 E62 G12
    Date: 2022–01
  10. By: Shuzhen Yang
    Abstract: In contrast to the usual procedure of estimating the distribution of a time series and then obtaining the quantile from the distribution, we develop a compensatory model to improve the quantile estimation under a given distribution estimation. A novel penalty term is introduced in the compensatory model. We prove that the penalty term can control the convergence error of the quantile estimation of a given time series, and obtain an adaptive adjusted quantile estimation. Simulation and empirical analysis indicate that the compensatory model can significantly improve the performance of the value at risk (VaR) under a given distribution estimation.
    Date: 2021–12
  11. By: Denny Irawan; Tatsuyoshi Okimoto
    Abstract: This study examines the conditional capital surplus and shortfall dynamics of renewable and non-renewable resource firms. To this end, this study uses the systemic risk index by Brownlees and Engle (2017) and considers two conditional systemic events, namely, the stock market crash and the commodity price crash. The results indicate that generally, companies in the resource sector tend to have conditional capital shortfall before 2000 and conditional capital surplus after 2000 owing to the boom of the commodity sector stock and the moderate-to-careful capital structure management adopted by these companies. This finding is especially valid for resource firms from developed countries, whose observations dominate the dataset used in this study. Furthermore, the analysis using the panel vector autoregressive model indicates a positive influence of commodity price, geopolitical, and economic policy uncertainties on the conditional capital shortfall. These uncertainties have also been proven to increase the conditional failure probability of firms in the sample. Lastly, the performance analysis shows that potential capital shortfall is positively related to market return, reflecting a high-risk high-return trade-off for this sector.
    Keywords: Systematic Risk Index, Commodity Prices, Macroeconomic Uncertainties, Panel Vector Autoregression
    JEL: E32 G32
    Date: 2021–08
  12. By: Jackson P. Lautier; Vladimir Pozdnyakov; Jun Yan
    Abstract: Actuaries must often come up with risk estimates from incomplete data rapidly and accurately. One such example is predicting and pricing cash flows from a trust of individual contingent risks, such as an automobile lease consumer asset-backed security. We find that using a discrete-time product-limit estimator modified for random truncation and censoring to estimate a survival distribution for consumer automobile lease contracts along with our proposed cash flow model can effectively predict future cash flows. Furthermore, the combination of this lifetime estimator and our cash flow model allows for the derivation of direct formulas to consistently estimate the actuarial present value, its associated variance, and the conditional-tail-expectation of the full pool of contingent risks at a given point in time without the need for simulation. We also prove the modified discrete-time product-limit-estimator yields an asymptotically multivariate normal estimation vector with independent components, which may be of use for small samples. The cash flow model and formulaic results perform well when applied to the Mercedes-Benz Auto Lease Trust (MBALT) 2017-A securitized bond.
    Date: 2022–01
  13. By: Mestiri, Sami
    Abstract: Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. The use of parametric GARCH models to characterise the volatility of Bitcoin returns is widely observed in the empirical literature. In this paper, we consider an alternative approach involving non-parametric method to model and forecast Bitcoin return volatility. We show that the out-of-sample volatility forecast of the non-parametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. The improvement in forecasting accuracy of Bitcoin return volatility based on the non-parametric GARCH model suggests that this method offers an attractive and viable alternative to the commonly used parametric GARCH models.
    Keywords: Bitcoin; volatility; GARCH; Nonparametric; Forecasting.
    JEL: C14 C53 C58
    Date: 2021–12–13
  14. By: Jan Muckenhaupt (Technische Universität München (TUM)); Martin Hoesli (University of Geneva - Geneva School of Economics and Management (GSEM); Swiss Finance Institute; University of Aberdeen - Business School); Bing Zhu (Technische Universität München (TUM))
    Abstract: This paper is the first to examine the relationship between the performance of public real estate companies (PRECs) and the industrial sector of their tenants. By investigating the performance of a large sample of European real estate firms from 2010 to 2019 and information pertaining to the firms' tenants, we find that the systematic risk in the tenants' industry sectors is priced in real estate company equity returns. Our results stay robust after correcting for selection bias, stock beta modifications, tenant sector alpha, and tenant anchor effects. We propose a long-short hedging strategy that buys the stocks with high tenant sector risk and sells the stocks with low tenant sector risk, which can earn a non-market return of 3.53% annually.
    Keywords: Public Real Estate Companies, Listed Real Estate, Tenants, Industry Sector, Systematic Risk
    JEL: R33 G12 G11
    Date: 2022–01
  15. By: Marcus C. Christiansen
    Abstract: Current reporting standards for insurers require a decomposition of observed profits and losses in such a way that changes in the insurer's balance sheet can be attributed to specified risk factors. Generating such a decomposition is a nontrivial task because balance sheets generally depend on the risk factors in a non-linear way. This paper starts from an axiomatic perspective on profit and loss decompositions and finds that the axioms necessarily lead to infinitesimal sequential updating (ISU) decompositions, provided that the latter exist and are stable, whereas the current practice is rather to use sequential updating (SU) decompositions. The generality of the axiomatic approach makes the results useful also beyond insurance applications wherever profits and losses shall be additively decomposed in a risk-oriented manner.
    Date: 2021–12
  16. By: Samuel Rufat (IUF - Institut Universitaire de France - M.E.N.E.S.R. - Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche, MRTE - EA 4112 - Laboratoire Mobilités, Réseaux, Territoires, Environnements - CY - CY Cergy Paris Université); Odile Plattard (MRTE - EA 4112 - Laboratoire Mobilités, Réseaux, Territoires, Environnements - CY - CY Cergy Paris Université); Alexander Fekete (THK - Institute of Rescue Engineering and Civil Protection, University of Applied Sciences Cologne); Ludivine Gilli (IRSN - Institut de Radioprotection et de Sûreté Nucléaire); Paul Hudson (University of York [York, UK]); Victor Santoni (MRTE - EA 4112 - Laboratoire Mobilités, Réseaux, Territoires, Environnements - CY - CY Cergy Paris Université)
    Date: 2021–10
  17. By: Jorgen Vitting Andersen; Roy Cerqueti; Jessica Riccioni
    Abstract: Here we introduce the idea of using rational expectations, a core concept in economics and finance, as a tool to predict the optimal failure time for a wide class of weighted k-out-of-n reliability systems. We illustrate the concept by applying it to systems which have components with heterogeneous failure times. Depending on the heterogeneous distributions of component failure, we find different measures to be optimal for predicting the failure time of the total system. We give examples of how, as a given system deteriorates over time, one can issue different optimal predictions of system failure by choosing among a set of time-dependent measures.
    Date: 2021–11
  18. By: Songyan Hou; Thomas Krabichler; Marcus Wunsch
    Abstract: Using techniques from deep learning (cf. [B\"uh+19]), we show that neural networks can be trained successfully to replicate the modified payoff functions that were first derived in the context of partial hedging by [FL00]. Not only does this approach better accommodate the realistic setting of hedging in discrete time, it also allows for the inclusion of transaction costs as well as general market dynamics.
    Date: 2021–12
  19. By: David Anderson (University of Zurich); Urban Ulrych (University of Zurich - Department of Banking and Finance; Swiss Finance Institute)
    Abstract: Given the competitiveness of a market-making environment, the ability to speedily quote option prices consistent with an ever-changing market environment is essential. Thus, the smallest acceleration or improvement over traditional pricing methods is crucial to avoid arbitrage. We propose a novel method for accelerating the pricing of American options to near-instantaneous using a feed-forward neural network. This neural network is trained over the chosen (e.g., Heston) stochastic volatility specification. Such an approach facilitates parameter interpretability, as generally required by the regulators, and establishes our method in the area of eXplainable Artificial Intelligence (XAI) for finance. We show that the proposed deep explainable pricer induces a speed accuracy trade-off compared to the typical Monte Carlo or Partial Differential Equation-based pricing methods. Moreover, the proposed approach allows for pricing derivatives with path dependent and more complex payoffs and is, given the sufficient accuracy of computation and its tractable nature, applicable in a market-making environment.
    Keywords: American Option Pricing, Deep Neural Networks, Explainable Artificial Intelligence, Speed-Accuracy Trade-Off, Market Making, Heston Model, Computational Finance.
    JEL: C45 C63 G13
    Date: 2022–01
  20. By: Battulga Gankhuu
    Abstract: This paper presents pricing and hedging methods for rainbow options and lookback options under Bayesian Markov-Switching Vector Autoregressive (MS--VAR) process. Here we assumed that a regime-switching process is generated by a homogeneous Markov process. An advantage of our model is it depends on economic variables and simple as compared with previous existing papers.
    Date: 2021–12

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