nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒02‒06
thirteen papers chosen by
Stan Miles
Thompson Rivers University

  1. The Information Value of Past Losses in Operational Risk By Filippo Curti; Marco Migueis
  2. Analisis Penerapan Manajemen Risiko Kredit Pada PT. Pegadaian (Persero) UPC Belimbing Padang By fernos, jhon; efrinaldo, Ihwalia
  3. Analisis Manajemen Risiko Produk Kredit Pemilikan Rumah Pada PT. Bank Tabungan Negara (Persero) Tbk. Kantor Cabang Padang By fernos, jhon; Itra, Nelgia
  4. Demand for index-based flood insurance in Jakarta, Indonesia By Jose Cobian; Budy P. Resosudarmo; Alin Halimatussadiah; Susan Olivia
  5. The limitations of comonotonic additive risk measures: a literature review By Samuel Solgon Santos; Marcelo Brutti Righi; Eduardo de Oliveira Horta
  6. Joint SPX-VIX calibration with Gaussian polynomial volatility models: deep pricing with quantization hints By Eduardo Abi Jaber; Camille Illand; Shaun Xiaoyuan Li
  7. The impact of risk cycles on business cycles: a historical view By Danielsson, Jon; Valenzuela, Marcela; Zer, Ilknur
  8. Dynamic portfolio optimization with inverse covariance clustering By Wang, Yuanrong; Aste, Tomaso
  9. Statistical Properties of Two Asymmetric Stochastic Volatility in Mean Models By Antonis Demos
  10. Robustifying Markowitz By Wolfgang Karl H\"ardle; Yegor Klochkov; Alla Petukhina; Nikita Zhivotovskiy
  11. Unpleasant Actuarial Arithmetic: Fair Contribution Rates for Defined Benefit Pension Schemes By Kenjiro Hori; Stephen Wright
  12. Target Retirement Fund: A Variant on Target Date Funds that uses Deferred Life Annuities rather than Bonds to Reduce Risk as Retirement Approaches By John B. Shoven; Daniel B. Walton
  13. Assessment of creditworthiness models privacy-preserving training with synthetic data By Ricardo Mu\~noz-Cancino; Cristi\'an Bravo; Sebasti\'an A. R\'ios; Manuel Gra\~na

  1. By: Filippo Curti; Marco Migueis
    Abstract: Operational risk is a substantial source of risk for US banks. Improving the performance of operational risk models allows banks’ management to make more informed risk decisions by better matching economic capital and risk appetite, and allows regulators to enhance their understanding of banks’ operational risk. We show that past operational losses are informative of future losses, even after controlling for a wide range of financial characteristics. We propose that the information provided by past losses results from them capturing hard to quantify factors such as the quality of operational risk controls, the risk culture, and the risk appetite of the bank.
    Keywords: Banking; Operational risk; Risk management
    JEL: G15 G18 G19 G21 G32
    Date: 2023–01–06
  2. By: fernos, jhon; efrinaldo, Ihwalia
    Abstract: The purpose of this study was to find out how to analyze the application of credit risk management at PT. Pegadaian (Persero) UPC Belimbing Padang. The research method used is descriptive method, which describes and explains the application of credit risk management at PT. Pegadaian (Persero) UPC Belimbing Padang. The result of this research is the application of credit risk management at PT. Pegadaian starfruit branch Padang is not running well or effectively because it is still experiencing an increase and decrease in the percentage of bad loans.
    Date: 2022–12–20
  3. By: fernos, jhon; Itra, Nelgia
    Abstract: The purpose of this study was to determine how the risk management analysis of home ownership credit products at PT. Bank Tabungan Negara (Persero) Tbk. Cabang Padang. This writing uses data analysis methods, namely qualitative and quantitative as research methods that describe descriptively about the risk management analysis of BTN KPR Credit Products. The results of the BTN KPR credit data can be said to have been running well from year to year because they have implemented credit policies in accordance with the precautionary principle.
    Date: 2022–12–21
  4. By: Jose Cobian; Budy P. Resosudarmo; Alin Halimatussadiah; Susan Olivia
    Abstract: Most megacities in developing countries are constantly exposed to flood risk, with a clear lack of understanding of insurance leading to poor risk management by urban populations. This paper analyses the demand for a hypothetical index-based flood insurance product among households in Jakarta, Indonesia. An expected utility framework is used to test whether this demand is significantly determined by the basis risk component of the insurance. The paper investigates the effects on insurance uptake of premium discounts, and risk aversion. Using distance of a house to the reference floodgate station (a proxy for basis risk), we find demand falls as basis risk increases. Additionally, the uptake decreases with price and risk aversion. We recommend further investment in floodgate stations to reduce basis risk, complemented with subsidies to encourage demand for this product. However, the level of discount offered to urban households is inconclusive and constitutes an important topic for future research.
    Keywords: index insurance, basis risk, disasters, floods, Indonesia
    JEL: D81 G22 Q54
    Date: 2022
  5. By: Samuel Solgon Santos; Marcelo Brutti Righi; Eduardo de Oliveira Horta
    Abstract: The theory of risk measures has grown enormously in the last twenty years. In particular, risk measures satisfying the axiom of comonotonic additivity were extensively studied, arguably because of the affluence of results indicating interesting aspects of such risk measures. Recent research, however, has shown that this axiom is incompatible with properties that are central in specific contexts. In this paper we present a literature review of these incompatibilities. As a secondary contribution, we show that the comonotonic additivity axiom conflicts with the property of excess invariance for risk measures and, in a milder form, with the property of surplus invariance for acceptance sets.
    Date: 2022–12
  6. By: Eduardo Abi Jaber (X - École polytechnique); Camille Illand (AXA Investment Managers, Multi Asset Client Solutions, Quantitative Research - AXA); Shaun Xiaoyuan Li (UP1 - Université Paris 1 Panthéon-Sorbonne, AXA Investment Managers, Multi Asset Client Solutions, Quantitative Research - AXA)
    Abstract: We consider the joint SPX-VIX calibration within a general class of Gaussian polynomial volatility models in which the volatility of the SPX is assumed to be a polynomial function of a Gaussian Volterra process defined as a stochastic convolution between a kernel and a Brownian motion. By performing joint calibration to daily SPX-VIX implied volatility surface data between 2012 and 2022, we compare the empirical performance of different kernels and their associated Markovian and non-Markovian models, such as rough and non-rough pathdependent volatility models. In order to ensure an efficient calibration and a fair comparison between the models, we develop a generic unified method in our class of models for fast and accurate pricing of SPX and VIX derivatives based on functional quantization and Neural Networks. For the first time, we identify a conventional one-factor Markovian continuous stochastic volatility model that is able to achieve remarkable fits of the implied volatility surfaces of the SPX and VIX together with the term structure of VIX futures. What is even more remarkable is that our conventional one-factor Markovian continuous stochastic volatility model outperforms, in all market conditions, its rough and non-rough path-dependent counterparts with the same number of parameters.
    Keywords: SPX and VIX modeling, Stochastic volatility, Gaussian Volterra processes, Quantization, Neural Networks
    Date: 2022–12–16
  7. By: Danielsson, Jon; Valenzuela, Marcela; Zer, Ilknur
    Abstract: We investigate the effects of financial risk cycles on business cycles, using a panel spanning 73 countries since 1900. Agents use a Bayesian learning model to form their beliefs on risk. We construct a proxy of these beliefs and show that perceived low risk encourages risk-taking, augmenting growth at the cost of accumulating financial vulnerabilities, and therefore, a reversal in growth follows. The reversal is particularly pronounced when the low-risk environment persists and credit growth is excessive. Global-risk cycles have a stronger effect on growth than local-risk cycles via their impact on capital flows, investment, and debt-issuer quality.
    Keywords: stock market volatility; uncertainty; monetary policy independance; financial instability; risk-taking; global financial cycles; ES/K002309/1; OUP deal
    JEL: F30 G15 G18 N10 N20
    Date: 2022–12–13
  8. By: Wang, Yuanrong; Aste, Tomaso
    Abstract: Market conditions change continuously. However, in portfolio investment strategies, it is hard to account for this intrinsic non-stationarity. In this paper, we propose to address this issue by using the Inverse Covariance Clustering (ICC) method to identify inherent market states and then integrate such states into a dynamic portfolio optimization process. Extensive experiments across three different markets, NASDAQ, FTSE and HS300, over a period of ten years, demonstrate the advantages of our proposed algorithm, termed Inverse Covariance Clustering-Portfolio Optimization (ICC-PO). The core of the ICC-PO methodology concerns the identification and clustering of market states from the analytics of past data and the forecasting of the future market state. It is therefore agnostic to the specific portfolio optimization method of choice. By applying the same portfolio optimization technique on a ICC temporal cluster, instead of the whole train period, we show that one can generate portfolios with substantially higher Sharpe Ratios, which are statistically more robust and resilient with great reductions in the maximum loss in extreme situations. This is shown to be consistent across markets, periods, optimization methods and selection of portfolio assets.
    Keywords: covariance structure; dynamic portfolio optimization; financial market states; information filtering networks; market regimes; portfolio management; temporal clustering; ES/K002309/1; EP/P031730/1; H2020-ICT-2018-2 825215
    JEL: J1
    Date: 2023–03–01
  9. By: Antonis Demos (
    Abstract: Here we investigate the statistical properties of two normal asymmetric SV models with possibly time varying risk premia. In fact, we investigate two popular autoregressive stochastic volatility specifications. These, although they seem very similar, it turns out, that they possess quite different statistical properties. The derived properties can be employed to develop tests or to check stationarity of various orders, something important for the asymptotic properties of various estimators.
    Date: 2023–01–19
  10. By: Wolfgang Karl H\"ardle; Yegor Klochkov; Alla Petukhina; Nikita Zhivotovskiy
    Abstract: Markowitz mean-variance portfolios with sample mean and covariance as input parameters feature numerous issues in practice. They perform poorly out of sample due to estimation error, they experience extreme weights together with high sensitivity to change in input parameters. The heavy-tail characteristics of financial time series are in fact the cause for these erratic fluctuations of weights that consequently create substantial transaction costs. In robustifying the weights we present a toolbox for stabilizing costs and weights for global minimum Markowitz portfolios. Utilizing a projected gradient descent (PGD) technique, we avoid the estimation and inversion of the covariance operator as a whole and concentrate on robust estimation of the gradient descent increment. Using modern tools of robust statistics we construct a computationally efficient estimator with almost Gaussian properties based on median-of-means uniformly over weights. This robustified Markowitz approach is confirmed by empirical studies on equity markets. We demonstrate that robustified portfolios reach the lowest turnover compared to shrinkage-based and constrained portfolios while preserving or slightly improving out-of-sample performance.
    Date: 2022–12
  11. By: Kenjiro Hori (Birkbeck, University of London); Stephen Wright (Birkbeck, University of London)
    Abstract: We derive key properties of the actuarially fair contribution rate for defined benefit (DB) schemes, that equates scheme assets to liabilities for any given scheme member. The unpleasant actuarial arithmetic of both increased life expectancy and (especially) negative real yields has resulted in a massive rise in the fair contribution rate over recent decades. At present there appears to be little prospect of these rises being reversed. We analyse the implications for the viability of DB schemes, and consider the (potentially significant) impact of incorporating systematic risk into benefits.
    Keywords: defined benefit, pension contribution rate
    JEL: J32
    Date: 2022–02–22
  12. By: John B. Shoven; Daniel B. Walton
    Abstract: This paper evaluates a new variant of the popular target date funds used in employer-based retirement savings plans. We call this new variant a “target retirement plan.” Instead of increasing the allocation to bond funds as retirement approaches, a target retirement fund gradually purchases deferred life annuities beginning at age 50. In the particular straw model target retirement fund examined in the paper, the defined contribution participant makes deferred life annuity purchases at ages 50, 52, 54, 56, 58, 60 and 62. We compare how a target retirement fund participant would fare compared with someone who stays with a traditional TDF until retirement and then buys an immediate life annuity. We examine 1, 000 possible 30-year futures for stock returns, bond fund returns and Treasury interest rates. The main result from this paper is that buying a retirement annuity in advance (by accumulating deferred life annuities) is superior to sticking with a Target Date Fund until retirement and then buying an immediate annuity in most scenarios of future stock returns, interest rates and bond returns.
    JEL: G11 G22 J14 J26
    Date: 2023–01
  13. By: Ricardo Mu\~noz-Cancino; Cristi\'an Bravo; Sebasti\'an A. R\'ios; Manuel Gra\~na
    Abstract: Credit scoring models are the primary instrument used by financial institutions to manage credit risk. The scarcity of research on behavioral scoring is due to the difficult data access. Financial institutions have to maintain the privacy and security of borrowers' information refrain them from collaborating in research initiatives. In this work, we present a methodology that allows us to evaluate the performance of models trained with synthetic data when they are applied to real-world data. Our results show that synthetic data quality is increasingly poor when the number of attributes increases. However, creditworthiness assessment models trained with synthetic data show a reduction of 3\% of AUC and 6\% of KS when compared with models trained with real data. These results have a significant impact since they encourage credit risk investigation from synthetic data, making it possible to maintain borrowers' privacy and to address problems that until now have been hampered by the availability of information.
    Date: 2022–12

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