nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒10‒31
twenty-two papers chosen by
Stan Miles
Thompson Rivers University

  1. Business Applications and State-Level Stock Market Realized Volatility: A Forecasting Experiment By Matteo Bonato; Oguzhan Cepni; Rangan Gupta; Christian Pierdzioch
  2. A Posteriori Risk Classification and Ratemaking with Random Effects in the Mixture-of-Experts Model By Spark C. Tseung; Ian Weng Chan; Tsz Chai Fung; Andrei L. Badescu; X. Sheldon Lin
  3. Understanding the Needs of Civil Protection Agencies and Opportunities for Scaling up Disaster Risk Management Investments By World Bank
  4. The Philippines Parametric Catastrophe Risk Insurance Program Pilot By World Bank
  5. Fractal analysis of Dow Jones Industrial Index returns By Desogus, Marco; Conversano, Claudio; Pili, Ambrogio; Venturi, Beatrice
  6. Climate Risks and State-Level Stock-Market Realized Volatility By Matteo Bonato; Oguzhan Cepni; Rangan Gupta; Christian Pierdzioch
  7. A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks By Jaydip Sen; Abhishek Dutta
  8. Risk adjustment under IFRS 17: An adaptation of Solvency 2 one-year aggregation into an ultimate view framework By Tachfine El Alami; Laurent Devineau; Stéphane Loisel
  9. The Sign of Risk for Present Value of Future Losses By Brian P. Hanley; Steve Keen
  10. A general firm value model under partial information By Mbaye, Cheikh; Sagna, Abass; Vrins, Frédéric
  11. Efficient Wrong-Way Risk Modelling for Funding Valuation Adjustments By T. van der Zwaard; L. A. Grzelak; C. W. Oosterlee
  12. On the optimal combination of naive and mean-variance portfolio strategies By Lassance, Nathan; Vanderveken, Rodolphe; Vrins, Frédéric
  13. Comprehensive empirical assessment of nuclear power risks By Ali Ayoub; Didier Sornette
  14. Financial Risk and Opportunities to Build Resilience in Europe By World Bank
  15. Detecting asset price bubbles using deep learning By Francesca Biagini; Lukas Gonon; Andrea Mazzon; Thilo Meyer-Brandis
  16. Does limited liability reduce leveraged risk?: The case of loan portfolio management By Deb Narayan Barik; Siddhartha P. Chakrabarty
  17. On Conditional Chisini Means and Risk Measures By Alessandro Doldi; Marco Maggis
  18. With big data come big problems: pitfalls in measuring basis risk for crop index insurance By Matthieu Stigler; Apratim Dey; Andrew Hobbs; David Lobell
  19. Quasi-Monte Carlo methods for calculating derivatives sensitivities on the GPU By Paul Bilokon; Sergei Kucherenko; Casey Williams
  20. "Dynamic connectedness between credit and liquidity risks in EMU sovereign debt markets". By Marta Gómez-Puig; Mary Pieterse-Bloem; Simón Sosvilla-Rivero
  21. Chaotic Hedging with Iterated Integrals and Neural Networks By Ariel Neufeld; Philipp Schmocker
  22. Sovereign Risk and Financial Risk By Simon Gilchrist; Bin Wei; Vivian Z. Yue; Egon Zakrajšek

  1. By: Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France); Oguzhan Cepni (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)
    Abstract: We analyze the predictive value of state-level business applications, as a proxy of local investor sentiment, for the state-level realized US stock-market volatility. We use highfrequency data for the period from September, 2011 to October, 2021 to compute realized volatility. We show, using an extended version of the popular heterogenous autoregressive realized volatility model, that business applications have predictive value at intermediate and long forecast horizons, after controlling for realized moments (realized skewness, realized kurtosis, realized tail risks), for realized state-level stock-market volatility, and for upside (``good") and downside (``bad") realized volatility.
    Keywords: State-level stock markets, State-level investor sentiment, Business applications, Realized volatility, Forecasting
    JEL: C22 C53 G10 G17 G41
    Date: 2022–10
  2. By: Spark C. Tseung; Ian Weng Chan; Tsz Chai Fung; Andrei L. Badescu; X. Sheldon Lin
    Abstract: A well-designed framework for risk classification and ratemaking in automobile insurance is key to insurers' profitability and risk management, while also ensuring that policyholders are charged a fair premium according to their risk profile. In this paper, we propose to adapt a flexible regression model, called the Mixed LRMoE, to the problem of a posteriori risk classification and ratemaking, where policyholder-level random effects are incorporated to better infer their risk profile reflected by the claim history. We also develop a stochastic variational Expectation-Conditional-Maximization algorithm for estimating model parameters and inferring the posterior distribution of random effects, which is numerically efficient and scalable to large insurance portfolios. We then apply the Mixed LRMoE model to a real, multiyear automobile insurance dataset, where the proposed framework is shown to offer better fit to data and produce posterior premium which accurately reflects policyholders' claim history.
    Date: 2022–09
  3. By: World Bank
    Keywords: Conflict and Development - Disaster Management Urban Development - Hazard Risk Management
    Date: 2021
  4. By: World Bank
    Keywords: Environment - Natural Disasters Finance and Financial Sector Development - Insurance & Risk Mitigation Urban Development - Hazard Risk Management
    Date: 2020–12
  5. By: Desogus, Marco; Conversano, Claudio; Pili, Ambrogio; Venturi, Beatrice
    Abstract: The Dow Jones Industrial Average 30 (DJIA30) Index was analyzed to show that models based on the Fractal Market Hypothesis (FMH) are preferable to those based on the Efficient Market Hypothesis (EMH). In a first step, Rescaled Range Analysis was applied to search for long term dependence between index returns. The Hurst coefficient was computed as a measure of persistence in the trend of the observed time series. A Monte Carlo simulation based on both Geometric Brownian Motion (GBM) and Fractional Brownian Motion (FBM) models was used in the second step to investigate the forecasting ability of each model in a situation where information about future prices is lacking. In the third step, the volatility of the index returns obtained from the simulated GBM and FBM was considered together with that produced by a GARCH(1,1) model in order to determine the approach that minimizes the Value at Risk (VaR) and the Conditional Value at Risk (CVaR) of one asset portfolio where the DJIA30 index underlies an Exchange Traded Commodity (ETC). In the case observed returns could either follow a gaussian distribution or a Pareto distribution with a scale parameter equal to the inverse of the Hurst coefficient determined in the first step.
    Keywords: Fractal Analysis; Rescaled Range Analysis; Pareto distribution; Hurst coefficient; Geometric Brownian Motion; Fractional Brownian Motion; Value at Risk (VaR); Conditional Value at Risk (CVaR); Efficient Market Hypothesis; Fractal Market Hypothesis; Dow Jones Industrial Average Index.
    JEL: C1 C63
    Date: 2022
  6. By: Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France); Oguzhan Cepni (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)
    Abstract: We analyze the predictive value of climate risks for state-level realized stock-market volatility, computed, along with other realized moments, based on high-frequency intra-day U.S. data (September, 2011 to October, 2021). A model-based bagging algorithm recovers that climate risks have predictive value for realized volatility at intermediate and long (one and two months) forecast horizons. This finding also holds for upside (``good" ) and downside (``bad" ) realized volatility. The benefits of using climate risks for forecasting state-level realized stock-market volatility depend on the shape and (as-)symmetry of a forecaster's loss function.
    Keywords: State-level data, Realized stock-market volatility, Climate-related predictors, Forecasting
    JEL: C22 C53 G10 G17 Q54
    Date: 2022–09
  7. By: Jaydip Sen; Abhishek Dutta
    Abstract: Portfolio optimization has been an area of research that has attracted a lot of attention from researchers and financial analysts. Designing an optimum portfolio is a complex task since it not only involves accurate forecasting of future stock returns and risks but also needs to optimize them. This paper presents a systematic approach to portfolio optimization using two approaches, the hierarchical risk parity algorithm and the Eigen portfolio on seven sectors of the Indian stock market. The portfolios are built following the two approaches to historical stock prices from Jan 1, 2016, to Dec 31, 2020. The portfolio performances are evaluated on the test data from Jan 1, 2021, to Nov 1, 2021. The backtesting results of the portfolios indicate that the performance of the HRP portfolio is superior to that of its Eigen counterpart on both training and test data for the majority of the sectors studied.
    Date: 2022–10
  8. By: Tachfine El Alami (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon, ADDACTIS France); Laurent Devineau (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Stéphane Loisel (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)
    Abstract: The standard IFRS 17 introduces a risk adjustment (RA) to reflect the compensation the insurance entity requires for bearing the uncertainty associated with nonfinancial risks. The risk adjustment is one of the main components in IFRS 17 disclosures and is a factor that impacts strongly IFRS 17 P&L and balance sheet as well as their evolution over a time horizon. IFRS 17 does not prescribe any specific techniques for calculation methodologies; insurance entities are free to adopt their own assessment while meeting several qualitative rules to ensure their consistency. This paper focuses on the recommendations of paragraph §B88 stating that the risk adjustment is required to reflect the diversification benefit of bearing the risk. We suggest a method for aggregating elementary RA (per risk and/or per Line of Business) based on the Solvency 2 elliptic aggregation. We introduce the concept of ultimate correlation as opposed to Solvency 2 one-year correlation and provide a theoretical bridge between both depending on a time diversification parameter. We explore correlation structures involving this time diversification and discuss analytical properties in terms of possible correlations values and the resulting impact on the aggregated RA features.
    Keywords: IFRS 17,Solvency 2,Risk Adjustment,Risk Aggregation,Correlation,Time diversification,Ultimate view
    Date: 2022–08–29
  9. By: Brian P. Hanley; Steve Keen
    Abstract: In the ongoing debate over discount rates and climate change, William Nordhaus has championed a higher discount rate to account for risk. Nicholas Stern has championed a lower rate. Here we prove that in the case of a stream of future losses, risk can only be represented by a lower discount rate, never a higher one.
    Date: 2022–08
  10. By: Mbaye, Cheikh (Université catholique de Louvain, LIDAM/LFIN, Belgium); Sagna, Abass; Vrins, Frédéric (Université catholique de Louvain, LIDAM/LFIN, Belgium)
    Abstract: We introduce a new structural default model which purpose is to combine enhanced economic relevance and affordable computational complexity. Our approach exploits the information conveyed by a noisy observation of the firm value combined with the firm’s actual default state. Moreover, it is rather general since any diffusion can be used to depict the firm’s dynamics. However, this realistic setup comes at the expense of important computational challenges. To mitigate them, we propose an implementation based on recursive quantization. A thorough analysis of the approximation error resulting from our numerical procedure is provided. The power of our method is illustrated on the pricing of CDS options. This analysis reveals that the observation noise has a significant impact on the credit spreads’ implied volatility.
    Keywords: Finance ; credit risk ; structural model ; noisy information ; non-linear filtering
    Date: 2022–07–28
  11. By: T. van der Zwaard; L. A. Grzelak; C. W. Oosterlee
    Abstract: Wrong-Way Risk (WWR) is an important component in Funding Valuation Adjustment (FVA) modelling. Yet, it can be challenging to compute WWR efficiently. We propose to split the relevant exposure profile into two parts: an independent part and a WWR-driven part. For the first part, already available exposures can be used where correlations between the funding spread and market risks are ignored. We express the second part of the exposure profile in terms of the stochastic drivers and approximate these by a common Gaussian stochastic factor. The proposed approximation is generic, is an add-on to the existing xVA calculations and provides an efficient and robust way to include WWR in FVA modelling. Furthermore, the approximation provides some intuition on WWR. Case studies are presented for an interest rate swap and a representative multi-currency portfolio of swaps. They illustrate that the approximation method is applicable in a practical setting due to its generic nature. We analyze the approximation error and illustrate how the approximation can be used to compute WWR sensitivities, which are needed for risk management.
    Date: 2022–09
  12. By: Lassance, Nathan (Université catholique de Louvain, LIDAM/LFIN, Belgium); Vanderveken, Rodolphe (Université catholique de Louvain, LIDAM/LFIN, Belgium); Vrins, Frédéric (Université catholique de Louvain, LIDAM/LFIN, Belgium)
    Abstract: A disheartening fact in portfolio choice is that the naive equally weighted portfoliooften outperforms the estimated optimal mean-variance portfolio out of sample. In an influential paper, Tu and Zhou (2011) reaffirm the value of portfolio theory by combining the two portfolios to optimize out-of-sample performance. They achieve this under a seemingly natural convexity constraint: the two combination coefficients must sum to one. We show that this constraint is unnecessary in theory and has several undesirable consequences relative to the unconstrained portfolio combination we derive. In particular, it leads to an overinvestment in the sample mean-variance portfolio, and a worse performance than the risk-free asset for sufficiently risk-averse investors. However, although wrong in theory, we demonstrate that the convexity constraint acts as a bound constraint on combination coefficients and thus can help improve performance when they are estimated. Our empirical analysis shows that the Tu and Zhou rule performs well for investors with small risk aversion, but quickly deteriorates as risk aversion increases. In contrast, our portfolio rules perform consistently well. Finally, we show theoretically and empirically that there are larger out-of-sample diversification gains from combining the sample mean-variance portfolio with the equally weighted portfolio instead of the minimum-variance portfolio.
    Keywords: Portfolio optimization ; parameter uncertainty ; estimation risk ; equally weighted portfolio ; portfolio constraints
    JEL: G11 G12
    Date: 2022–07–13
  13. By: Ali Ayoub (Massachusetts Institute of Technology (MIT)); Didier Sornette (ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Swiss Finance Institute; Southern University of Science and Technology; Tokyo Institute of Technology)
    Abstract: The opposition in a number of countries to the inclusion of nuclear energy in a sustainable energy portfolio, in part due to the dread of what the “nuclear” word inspires, has limited quantitative scientific foundation of the real benefits and risks. This has been amplified by the lack of a sound operational risk estimate due to the scarcity of the relevant empirical data. Using what is by far the largest - recently constructed - open database on accident precursors, and using our in-house generic probabilistic safety assessment (PSA) models, we provide the first comprehensive statistical study of the operational risks in the civil nuclear sector. We quantify a Pareto distribution of precursor severities as well as a special runaway Dragon Kings regime for the largest events. With respect to risk assessment, our main finding is that risk is dominated by exogenous factors (95%). We calculate that, by focusing on these factors in new design concepts, the frequency of accidents of the Fukushima scale can be brought down to about one per 300 years of operation of the worldwide fleet. Our results also demonstrate the need for an international cooperation focused on the construction of full blockchains of the cascades of accident precursors.
    Keywords: nuclear energy, probabilistic safety assessment, accident precursors, dragon kings, operational risks, open-source database
    JEL: D81 K32 Q4 C8
    Date: 2022–09
  14. By: World Bank
    Keywords: Public Sector Development - Public Financial Management Environment - Natural Disasters Finance and Financial Sector Development - Insurance & Risk Mitigation
    Date: 2021
  15. By: Francesca Biagini; Lukas Gonon; Andrea Mazzon; Thilo Meyer-Brandis
    Abstract: In this paper we employ deep learning techniques to detect financial asset bubbles by using observed call option prices. The proposed algorithm is widely applicable and model-independent. We test the accuracy of our methodology in numerical experiments within a wide range of models and apply it to market data of tech stocks in order to assess if asset price bubbles are present. In addition, we provide a theoretical foundation of our approach in the framework of local volatility models. To this purpose, we give a new necessary and sufficient condition for a process with time-dependent local volatility function to be a strict local martingale.
    Date: 2022–10
  16. By: Deb Narayan Barik; Siddhartha P. Chakrabarty
    Abstract: Return-risk models are the two pillars of modern portfolio theory, which are widely used to make decisions in choosing the loan portfolio of a bank. Banks and other financial institutions are subjected to limited liability protection. However, in most of the model formulation, limited liability is not taken into consideration. Accordingly, to address this, we have, in this article, analyzed the effect of including it in the model formulation. We formulate four models, two of them are maximizing the expected return with risk constraint, including and excluding limited-liability, and other two are minimization of risk with threshold level of return with and without limited-liability. Our theoretical results show that the solutions of the models with limited-liability produce better results than the others, in both minimizing risk and maximizing expected return. It has less risky investment than the other portfolio that solves the other model. Finally, an illustrative example is presented to support the theoretical results obtained.
    Date: 2022–09
  17. By: Alessandro Doldi; Marco Maggis
    Abstract: Given a real valued functional T on the space of bounded random variables, we investigate the problem of the existence of a conditional version of nonlinear means. We follow a seminal idea by Chisini (1929), defining a mean as the solution of a functional equation induced by T. We provide sufficient conditions which guarantee the existence of a (unique) solution of a system of infinitely many functional equations, which will provide the so called Conditional Chisini mean. We apply our findings in characterizing the scalarization of conditional Risk Measures, an essential tool originally adopted by Detlefsen and Scandolo (2005) to deduce the robust dual representation.
    Date: 2022–09
  18. By: Matthieu Stigler; Apratim Dey; Andrew Hobbs; David Lobell
    Abstract: New satellite sensors will soon make it possible to estimate field-level crop yields, showing a great potential for agricultural index insurance. This paper identifies an important threat to better insurance from these new technologies: data with many fields and few years can yield downward biased estimates of basis risk, a fundamental metric in index insurance. To demonstrate this bias, we use state-of-the-art satellite-based data on agricultural yields in the US and in Kenya to estimate and simulate basis risk. We find a substantive downward bias leading to a systematic overestimation of insurance quality. In this paper, we argue that big data in crop insurance can lead to a new situation where the number of variables $N$ largely exceeds the number of observations $T$. In such a situation where $T\ll N$, conventional asymptotics break, as evidenced by the large bias we find in simulations. We show how the high-dimension, low-sample-size (HDLSS) asymptotics, together with the spiked covariance model, provide a more relevant framework for the $T\ll N$ case encountered in index insurance. More precisely, we derive the asymptotic distribution of the relative share of the first eigenvalue of the covariance matrix, a measure of systematic risk in index insurance. Our formula accurately approximates the empirical bias simulated from the satellite data, and provides a useful tool for practitioners to quantify bias in insurance quality.
    Date: 2022–09
  19. By: Paul Bilokon; Sergei Kucherenko; Casey Williams
    Abstract: The calculation of option Greeks is vital for risk management. Traditional pathwise and finite-difference methods work poorly for higher-order Greeks and options with discontinuous payoff functions. The Quasi-Monte Carlo-based conditional pathwise method (QMC-CPW) for options Greeks allows the payoff function of options to be effectively smoothed, allowing for increased efficiency when calculating sensitivities. Also demonstrated in literature is the increased computational speed gained by applying GPUs to highly parallelisable finance problems such as calculating Greeks. We pair QMC-CPW with simulation on the GPU using the CUDA platform. We estimate the delta, vega and gamma Greeks of three exotic options: arithmetic Asian, binary Asian, and lookback. Not only are the benefits of QMC-CPW shown through variance reduction factors of up to $1.0 \times 10^{18}$, but the increased computational speed through usage of the GPU is shown as we achieve speedups over sequential CPU implementations of more than $200$x for our most accurate method.
    Date: 2022–09
  20. By: Marta Gómez-Puig (Department of Economics and Riskcenter, Universitat de Barcelona. 08034 Barcelona, Spain.); Mary Pieterse-Bloem (Section Finance in Business Economics, Erasmus School of Economics, 3062 PA, Rotterdam, and Rabobank**, 3521 CB, Utrecht, the Netherlands. Phone: +316-5136 5132.); Simón Sosvilla-Rivero (Complutense Institute for Economic Analysis, Universidad Complutense de Madrid. 28223 Madrid, Spain.)
    Abstract: We examine the dynamic interconnection between sovereign credit and liquidity risks in ten euro area countries at the 5-year maturity with high-frequency data from MTS over the period January 2008-December 2018 using the extension of the TVP-VAR connectedness approach of Antonakakis et al. (2020). Our results indicate that for most periods net connectedness is from credit risk to liquidity risk, but this indicator is time-dependent, detecting some episodes where it goes from liquidity risk to credit risk. We set up an event study and find that the latter episodes can be related to several unconventional monetary policy measures of the ECB. Then, we examine the drivers of the connectedness indicator by means of a Probit model. Our results suggest that monetary policy shocks and economic policy uncertainty increase the probability of risk transmission from liquidity to credit, while global funding liquidity, tensions in financial markets and surprises in inflation and GDP are factors that reduce such probability.
    Keywords: Liquidity risk, Credit risk, Eurozone sovereign bonds, MTS bond market, Dynamic connectedness, Time-varying parameters. JEL classification: C22, C53, G12, G14, G15.
    Date: 2022–10
  21. By: Ariel Neufeld; Philipp Schmocker
    Abstract: In this paper, we extend the Wiener-Ito chaos decomposition to the class of diffusion processes, whose drift and diffusion coefficient are of linear growth. By omitting the orthogonality in the chaos expansion, we are able to show that every $p$-integrable functional, for $p \in [1,\infty)$, can be represented as sum of iterated integrals of the underlying process. Using a truncated sum of this expansion and (possibly random) neural networks for the integrands, whose parameters are learned in a machine learning setting, we show that every financial derivative can be approximated arbitrarily well in the $L^p$-sense. Moreover, the hedging strategy of the approximating financial derivative can be computed in closed form.
    Date: 2022–09
  22. By: Simon Gilchrist; Bin Wei; Vivian Z. Yue; Egon Zakrajšek
    Abstract: In this paper, we study the interplay between sovereign risk and global financial risk. We show that a substantial portion of the comovement among sovereign spreads is accounted for by changes in global financial risk. We construct bond-level sovereign spreads for dollar-denominated bonds issued by more than 50 countries from 1995 to 2020 and use various indicators to measure global financial risk. Through panel regressions and local projection analysis, we find that an increase in global financial risk causes a large and persistent widening of sovereign bond spreads. These effects are strongest when measuring global risk using the excess bond premium, which is a measure of the risk-bearing capacity of US financial intermediaries. The spillover effects of global financial risk are more pronounced for speculative-grade sovereign bonds.
    Keywords: sovereign bonds; CDS; global financial risk; excess bond premium; global financial cycle
    JEL: E43 E44 F33 G12
    Date: 2021–11–24

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