nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒08‒22
twelve papers chosen by
Stan Miles
Thompson Rivers University

  1. A multivariate semi-parametric portfolio risk optimization and forecasting framework By Giuseppe Storti; Chao Wang
  2. Modeling S&P500 returns with GARCH models By Rodrigo Alfaro; Alejandra Inzunza
  3. Pricing for a vulnerable bull spread options using a mixed modified fractional Hull-White-Vasicek model By Eric Djeutcha; Jules Sadefo Kamdem
  4. Macroprudential Regulation and Sector-Specific Default Risk By Sami Ben Naceur; Bertrand Candelon; Mohamed Belkhir; Jean-Charles Wijnandts
  5. Optimal investment strategy to maximize the expected utility of an insurance company under Cramer Lundberg dynamic By J. Cerda-Hernandez; A. Sikov
  6. Rough-Heston Local-Volatility Model By Enrico Dall'Acqua; Riccardo Longoni; Andrea Pallavicini
  7. Deep Bellman Hedging By Hans Buehler; Phillip Murray; Ben Wood
  8. Aggregate skewness and the business cycle By Martin Iseringhausen; Ivan Petrella; Konstantinos Theodoridis
  9. May Bad Luck Be Without You: The Effect of CEO Luck on Strategic Risk-taking By Pascal Flurin Meier; Raphael Flepp; David Oesch
  10. Estimating the Mortgage Default Probability in Cyprus: Evidence using micro data By Savvas Antoniou; Ioanna Evangelou; Theodosis Kallenos; Nektarios A. Michail
  11. A multi-task network approach for calculating discrimination-free insurance prices By Mathias Lindholm; Ronald Richman; Andreas Tsanakas; Mario V. W\"uthrich
  12. Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach By Menna Hassan; Nourhan Sakr; Arthur Charpentier

  1. By: Giuseppe Storti; Chao Wang
    Abstract: A new multivariate semi-parametric risk forecasting framework is proposed, to enable the portfolio Value-at-Risk (VaR) and Expected Shortfall (ES) optimization and forecasting. The proposed framework accounts for the dependence structure among asset returns, without assuming the distribution of returns. A simulation study is conducted to evaluate the finite sample properties of the employed estimator for the proposed model. An empirically motivated portfolio optimization method, that can be utilized to optimize the portfolio VaR and ES, is developed. A forecasting study on 2.5% level evaluates the performance of the model in risk forecasting and portfolio optimization, based on the components of the Dow Jones index for the out-of-sample period from December 2016 to September 2021. Comparing to the standard models in the literature, the empirical results are favourable for the proposed model class, in particular the effectiveness of the proposed framework in portfolio risk optimization is clearly demonstrated.
    Date: 2022–07
  2. By: Rodrigo Alfaro; Alejandra Inzunza
    Abstract: This paper provides several estimates of the parameters of a GARCH model for the S&P500 index, based on: (i) returns, (ii) returns and CBOE VIX, and (iii) returns, CBOE VIX, and other option-based indexes reported by the Federal Reserve of Minneapolis. We conclude that by including option-based indexes alternative calibrations are obtained, which can be used to compute improved tail-risk statistics under the risk neutral measure, providing a better assessment of the occurrence of extreme events.
    Date: 2022–05
  3. By: Eric Djeutcha (UMa - University of Maroua); Jules Sadefo Kamdem (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier)
    Abstract: In this paper, in order to serve credit risk management, we introduce a pricing model for a vulnerable Bull Spread options in a Mixed Modified Fractional Hull-White-Vasicek stochastic volatility and stochastic interest rate model. We use Milstein scheme to find the sample paths of asset price and its volatility, and the sample paths of interest rates of asset price movement. We use the double Mellin transform to obtain an analytical vulnerable bull spread call option formula and an analytical vulnerable bull spread put option formula under fractional stochastic volatility and fractional stochastic interest rates.
    Keywords: Bull spread option,Hull-White-Vasicek model,Double Mellin transform
    Date: 2022
  4. By: Sami Ben Naceur; Bertrand Candelon; Mohamed Belkhir; Jean-Charles Wijnandts
    Abstract: This paper studies the transmission of macroprudential policies across both financial and non financial sectors of the economy. It first documents that tighter macroprudential regulations implemented in Europe over the period 2008–2017 lowered default risk not only in the financial, but also in non-financial sectors. Second, the paper analyzes the impact of two reforms in the macroprudential framework. Higher capital requirements improve the long-run resilience of the financial sector but at the cost of raising long-term default risk in non-financial sectors. Strengthening the resolution framework for failing banks has beneficial long-run effects on the default risks of the financial and non-financial sectors. Our results concur with the literature documenting how banks adjust their balance sheet composition and credit supply in reaction to changes in their regulatory environment.
    Keywords: Macroprudential regulation; Default risk; Capital requirements; Bank bail-in
    Date: 2022–07–15
  5. By: J. Cerda-Hernandez; A. Sikov
    Abstract: In this work, we examine the combined problem of optimal portfolio selection rules for an insurer in a continuous time model where the surplus of an insurance company is modelled as a compound Poisson process. The company can invest its surplus in a risk free asset and in a risky asset, governed by the Black-Scholes equation. According to utility theory, in a financial market where investors are facing uncertainty, an investor is not concerned with wealth maximization per se but with utility maximization. It is therefore possible to introduce an increasing and concave utility function $\phi(x,t)$ representing the expected utility of a risk averse investor (insurance company). Therefore, the goal of this work is not anymore to maximize the expected portfolio value or minimize the ruin probability or maximizing the expectation of the present value of all dividends paid to the shareholders up to the ruin, but to maximize the expected utility stemming from the wealth during the life contract [0,T]. In this direction, using the Dynamic Programming Principle of the problem, we obtain the Hamilton-Jacobi-Bellman equation by our optimization problem (HJB). Finally, we present numerical solutions in some cases, obtaining as optimal strategy the well known Merton's strategy.
    Date: 2022–07
  6. By: Enrico Dall'Acqua; Riccardo Longoni; Andrea Pallavicini
    Abstract: In industrial applications it is quite common to use stochastic volatility models driven by semi-martingale Markov volatility processes. However, in order to fit exactly market volatilities, these models are usually extended by adding a local volatility term. Here, we consider the case of singular Volterra processes, and we extend them by adding a local-volatility term to their Markov lift by preserving the stylized results implied by these models on plain-vanilla options. In particular, we focus on the rough-Heston model, and we analyze the small time asymptotics of its implied local-volatility function in order to provide a proper extrapolation scheme to be used in calibration.
    Date: 2022–06
  7. By: Hans Buehler; Phillip Murray; Ben Wood
    Abstract: We present an actor-critic-type reinforcement learning algorithm for solving the problem of hedging a portfolio of financial instruments such as securities and over-the-counter derivatives using purely historic data. The key characteristics of our approach are: the ability to hedge with derivatives such as forwards, swaps, futures, options; incorporation of trading frictions such as trading cost and liquidity constraints; applicability for any reasonable portfolio of financial instruments; realistic, continuous state and action spaces; and formal risk-adjusted return objectives. Most importantly, the trained model provides an optimal hedge for arbitrary initial portfolios and market states without the need for re-training. We also prove existence of finite solutions to our Bellman equation, and show the relation to our vanilla Deep Hedging approach
    Date: 2022–07
  8. By: Martin Iseringhausen (ESM); Ivan Petrella (University of Warwick, CEPR); Konstantinos Theodoridis (ESM)
    Abstract: We develop a data-rich measure of expected macroeconomic skewness in the US economy. Expected macroeconomic skewness is strongly procyclical, mainly reflects the cyclicality in the skewness of real variables, is highly correlated with the cross-sectional skewness of firm-level employment growth and is distinct from financial market skewness. Revisions in expected skewness deliver dynamics that are nearly indistinguishable from those produced by the main business cycle shock of Angeletos et al. (2020). This result is robust to controlling for macroeconomic volatility and uncertainty, and alternative macroeconomic shocks. Our findings highlight the importance of higher-order dynamics for business cycle theories.
    Keywords: Business cycles, downside risk, skewness
    JEL: C22 C38 E32
    Date: 2022–07–20
  9. By: Pascal Flurin Meier (Department of Business Administration, University of Zurich); Raphael Flepp (Department of Business Administration, University of Zurich); David Oesch (Department of Business Administration, University of Zurich)
    Abstract: We investigate how luck, namely, changes in a firm’s performance beyond the CEO’s control, affects strategic risk-taking. Fusing upper echelons theory with insights from psychology and behavioral strategy research, we hypothesize that there is a positive association between luck and strategic risk-taking and that this effect is stronger for bad luck than for good luck. We further argue that these effects vary depending on whether CEOs have experienced negative events earlier in their professional careers. Measuring luck as the exogenous component of recent firm performance, we show empirically that CEOs react to bad luck by adopting more conservative risk-taking policies while showing no reactions to good luck. This effect predictably varies with the strength of bad luck signals, and it is stronger for CEOs who have experienced negative events during their professional careers. We contribute to the literature by providing the first evidence on the role of luck in corporate strategic risk-taking.
    Keywords: Strategic Risk-Taking; Chief Executive Offers; Luck; Upper Echelons; Behavioral Strategy
    JEL: D22 D91 G30 M10 L20
    Date: 2022–06
  10. By: Savvas Antoniou (Central Bank of Cyprus); Ioanna Evangelou (Central Bank of Cyprus); Theodosis Kallenos; Nektarios A. Michail (Central Bank of Cyprus)
    Abstract: As financial institutions are exposed to the mortgage market, the identification of the characteristics associated with high default risk is crucial for the economy’s financial stability and growth. In this paper, we examine for the determinants of mortgage default for households, using both their economic and socio-demographic characteristics. Using panel data from the Eurosystem Household Finance and Consumption Survey from 2009 to 2017, we find that the mortgage debt service to income ratio, as well as the debt to total household wealth ratio, are positively related with a higher mortgage default probability. In addition, salaried employment reduces such probability and households with more than four members are more prone to mortgage arrears.
    Keywords: Eurosystem HFCS, survey, defaults, probability, households
    JEL: G21 C83 C51
    Date: 2022–06
  11. By: Mathias Lindholm; Ronald Richman; Andreas Tsanakas; Mario V. W\"uthrich
    Abstract: In applications of predictive modeling, such as insurance pricing, indirect or proxy discrimination is an issue of major concern. Namely, there exists the possibility that protected policyholder characteristics are implicitly inferred from non-protected ones by predictive models, and are thus having an undesirable (or illegal) impact on prices. A technical solution to this problem relies on building a best-estimate model using all policyholder characteristics (including protected ones) and then averaging out the protected characteristics for calculating individual prices. However, such approaches require full knowledge of policyholders' protected characteristics, which may in itself be problematic. Here, we address this issue by using a multi-task neural network architecture for claim predictions, which can be trained using only partial information on protected characteristics, and it produces prices that are free from proxy discrimination. We demonstrate the use of the proposed model and we find that its predictive accuracy is comparable to a conventional feedforward neural network (on full information). However, this multi-task network has clearly superior performance in the case of partially missing policyholder information.
    Date: 2022–07
  12. By: Menna Hassan; Nourhan Sakr; Arthur Charpentier
    Abstract: This paper designs a sequential repeated game of a micro-founded society with three types of agents: individuals, insurers, and a government. Nascent to economics literature, we use Reinforcement Learning (RL), closely related to multi-armed bandit problems, to learn the welfare impact of a set of proposed policy interventions per $1 spent on them. The paper rigorously discusses the desirability of the proposed interventions by comparing them against each other on a case-by-case basis. The paper provides a framework for algorithmic policy evaluation using calibrated theoretical models which can assist in feasibility studies.
    Date: 2022–07

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