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on Risk Management |
| By: | Shu Ling Chiang; Ming Shann Tsai |
| Keywords: | Idiosyncratic risk; Mortgage Insurance; Systematic Risk; Valuation |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_165&r=rmg |
| By: | Nuscheler, Robert; Vaclahovsky, Simon |
| JEL: | D43 D82 H51 I11 I13 I18 L51 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc23:277663&r=rmg |
| By: | Gloria Gonzalez-Rivera (Department of Economics, University of California Riverside); Vladimir Rodriguez-Caballero (ITAM (Mexico)); Esther Ruiz (Universidad Carlos III de Madrid (Spain)) |
| Abstract: | We propose the construction of conditional growth densities under stressed factor scenarios to assess the level of exposure of an economy to small probability but potentially catastrophic economic and/or fi nancial scenarios, which can be either domestic or international. The choice of severe yet plausible stress scenarios is based on the joint probability distribution of the underlying factors driving growth, which are extracted with a multi-level Dynamic Factor Model (DFM) from a wide set of domestic/worldwide and/or macroeconomic/fi nancial variables. All together, we provide a risk management tool that allows for a complete visualization of the dynamics of the growth densities under average scenarios and extreme scenarios. We calculate Growth-in-Stress (GiS) measures, defi ned as the 5% quantile of the stressed growth densities, and show that GiS is a useful and complementary tool to Growth-at-Risk (GaR) when policymakers wish to carry out a multi-dimensional scenario analysis. The unprecedented economic shock brought by the COVID19 pandemic provides a natural environment to assess the vulnerability of US growth with the proposed methodology. |
| Keywords: | Growth vulnerability, Multi-level factor model, Scenario analysis, Stressed factors |
| JEL: | C32 C55 E32 E44 F44 F47 O41 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:ucr:wpaper:202314&r=rmg |
| By: | Kostic, Natalija; Muthsam, Viktoria; Laux, Christian |
| JEL: | G21 G28 M41 M48 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc23:277694&r=rmg |
| By: | Roberto Daluiso |
| Abstract: | Credit Valuation Adjustment is a balance sheet item which is nowadays subject to active risk management by specialized traders. However, one of the most important risk factors, which is the vector of default intensities of the counterparty, affects in a non-differentiable way the most general Monte Carlo estimator of the adjustment, through simulation of default times. Thus the computation of first and second order (pure and mixed) sensitivities involving these inputs cannot rely on direct path-wise differentiation, while any approach involving finite differences shows very high statistical noise. We present ad hoc analytical estimators which overcome these issues while offering very low runtime overheads over the baseline computation of the price adjustment. We also discuss the conversion of the so-obtained sensitivities to model parameters (e.g. default intensities) into sensitivities to market quotes (e.g. Credit Default Swap spreads). |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.11672&r=rmg |
| By: | Michele Azzone; Maria Chiara Pocelli; Davide Stocco |
| Abstract: | Sustainable investing refers to the integration of environmental and social aspects in investors' decisions. We propose a novel methodology based on the Triangulated Maximally Filtered Graph and node2vec algorithms to construct an hedging portfolio for climate risk, represented by various risk factors, among which the CO2 and the ESG ones. The CO2 factor is strongly correlated consistently over time with the Utility sector, which is the most carbon intensive in the S&P 500 index. Conversely, identifying a group of sectors linked to the ESG factor proves challenging. As a consequence, while it is possible to obtain an efficient hedging portfolio strategy with our methodology for the carbon factor, the same cannot be achieved for the ESG one. The ESG scores appears to be an indicator too broadly defined for market applications. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.12450&r=rmg |
| By: | Viral V. Acharya; Toomas Laarits |
| Abstract: | We document that the convenience yield of U.S. Treasuries exhibits properties that are consistent with a hedging perspective of safe assets. The convenience yield tends to be low when the covariance of Treasury returns with the aggregate stock market returns is high. A decomposition of the aggregate stock-bond covariance into terms corresponding to the convenience yield, the frictionless risk-free rate, and default risk reveals that the covariance between stock returns and the convenience yield itself drives the effect in a substantive capacity. We show the convenience yield is reduced with heightened inflation expectations that erode the hedging properties of U.S. Treasuries and other fixed-income money-like assets, inducing a switch to alternatives such as gold; it is also reduced immediately prior to debt-ceiling standoffs and with increases in Treasury supply. |
| JEL: | E4 E5 F3 G11 G12 G15 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31863&r=rmg |
| By: | Christis Katsouris |
| Abstract: | This paper develops an asymptotic distribution theory for a two-stage instrumentation estimation approach in quantile predictive regressions when both generated covariates and persistent predictors are used. The generated covariates are obtained from an auxiliary quantile regression model and our main interest is the robust estimation and inference of the primary quantile predictive regression in which this generated covariate is added to the set of nonstationary regressors. We find that the proposed doubly IVX estimator is robust to the abstract degree of persistence regardless of the presence of generated regressor obtained from the first stage procedure. The asymptotic properties of the two-stage IVX estimator such as mixed Gaussianity are established while the asymptotic covariance matrix is adjusted to account for the first-step estimation error. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.08218&r=rmg |
| By: | Grochola, Nicolaus |
| Abstract: | Between 2016 and 2022, life insurers in several European countries experienced negative longterm interest rates, which put pressure on their business models. The aim of this paper is to empirically investigate the impact of negative interest rates on the stock performance of life insurers. To measure the sensitivities, I estimate the level, slope, and curvature of the yield curve using the Nelson-Siegel model and empirical proxies. Panel regressions show that the effect of changes in the level is up to three times greater in a negative interest rate environment than in a positive one. Thus, a 1ppt decline in long-term interest rates reduces the stock returns of European life insurers by up to 10ppt when interest rates are below 0%. I also show that the relationship between the level and the sensitivity to interest rates is convex, and that life insurers benefit from rising interest rates across all maturity types. |
| Keywords: | Life insurance, interest rate risk, negative interest rates |
| JEL: | G01 G18 G22 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:icirwp:279897&r=rmg |
| By: | Alexandros Skouralis; Nicole Lux; Mark Andrew |
| Keywords: | Climate Change; flood risk; Flood Risk Discount; UK Property Prices |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_79&r=rmg |
| By: | Maximilian Blesch (HU Berlin, DIW Berlin); Philipp Eisenhauer (Amazon) |
| Abstract: | Economists often estimate economic models on data and use the point estimates as a stand-in for the truth when studying the model’s implications for optimal decision-making. This practice ignores model ambiguity, exposes the decision problem to misspecification, and ultimately leads to post-decision disappointment. Using statistical decision theory, we develop a framework to explore, evaluate, and optimize robust decision rules that explicitly account for estimation uncertainty. We show how to operationalize our analysis by studying robust decisions in a stochastic dynamic investment model in which a decision-maker directly accounts for uncertainty in the model’s transition dynamics. |
| Keywords: | decision-making under uncertainty; robust Markov decision process; |
| JEL: | D81 C44 D25 |
| Date: | 2023–11–22 |
| URL: | http://d.repec.org/n?u=RePEc:rco:dpaper:463&r=rmg |
| By: | Michael S. Barr |
| Date: | 2023–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgsq:97405&r=rmg |
| By: | Emile A. Marin; Sanjay R. Singh (Department of Economics, University of California Davis) |
| Abstract: | Classical contributions in international macroeconomics rely on goods-market mechanisms to reconcile the cyclicality of real exchange rates when financial markets are incomplete. However, cross-border trade in one domestic and one foreign-currency-denominated risk-free asset prohibits these mechanisms from breaking the pattern consistent with complete markets. In this paper, we characterize how goods markets drive exchange rate cyclicality, taking into account trade in risk-free and/or risky assets. We show that goods market mechanisms come back into play, even when there is cross-border trade in two risk-free assets, as long as we allow for empirically plausible heterogeneity in the stochastic discount factors of domestic marginal investors. |
| Keywords: | risk sharing, incomplete markets, exchange rates |
| JEL: | E32 F31 F44 G15 |
| Date: | 2023–12–03 |
| URL: | http://d.repec.org/n?u=RePEc:cda:wpaper:361&r=rmg |
| By: | Sascha Desmettre; Sebastian Merkel; Annalena Mickel; Alexander Steinicke |
| Abstract: | We study and solve the worst-case optimal portfolio problem as pioneered by Korn and Wilmott (2002) of an investor with logarithmic preferences facing the possibility of a market crash with stochastic market coefficients by enhancing the martingale approach developed by Seifried in 2010. With the help of backward stochastic differential equations (BSDEs), we are able to characterize the resulting indifference optimal strategies in a fairly general setting. We also deal with the question of existence of those indifference strategies for market models with an unbounded market price of risk. We therefore solve the corresponding BSDEs via solving their associated PDEs using a utility crash-exposure transformation. Our approach is subsequently demonstrated for Heston's stochastic volatility model, Bates' stochastic volatility model including jumps, and Kim-Omberg's model for a stochastic excess return. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.10021&r=rmg |
| By: | Marcello D'Amato (University of Naples Suor Orsola Benincasa, CSEF and CELPE.); Christian Di Pietro (University of Napoli Parthenope and CELPE.); Marco M. Sorge (Università di Salerno, University of Göttingen, and CSEF-DISES) |
| Abstract: | We study a model of wealth accumulation in altruistic lineages, in which households face uninsurable risk, investment indivisibilities and credit market imperfections. A thick upper tail of the stationary distribution of wealth is shown to emerge as a robust prediction, irrespective of (i) the presence of multidimensional (wealth and ability) heterogeneity and non-convexities in human capital formation, and (ii) the nature of parental bequest motives (joy-of-giving vs. paternalism). Additionally, (iii) we identify conditions under which the unique, ergodic wealth distribution exhibits a mass point at the bottom of its support, where bequest incentives are inactive and social mobility can only occur via occupational upgrading within lineages. Our interest in the features of the left tail motivates the exploration of the effects of various frictions and fiscal measures on intergenerational wealth transmission and the persistence of inequality. We show that tax policies (e.g. capital income taxation) targeting top wealth inequality can dilate expected residence time of lineages in the lower states of the wealth space, providing a strong case for redistributive policies that favour access to education for the less wealthy. |
| Keywords: | Wealth distribution, Wealth inequality, Capital income risk, Credit market imperfections, Educational investment. |
| JEL: | D31 H20 I24 |
| Date: | 2023–11–03 |
| URL: | http://d.repec.org/n?u=RePEc:sef:csefwp:691&r=rmg |
| By: | Francesco Ruscitti; Ram Sewak Dubey; Giorgio Laguzzi |
| Abstract: | Motivated by the analysis of a general optimal portfolio selection problem, which encompasses as special cases an optimal consumption and an optimal debt-arrangement problem, we are concerned with the questions of how a personality trait like risk-perception can be formalized and whether the two objectives of utility-maximization and risk-minimization can be both achieved simultaneously. We address these questions by developing an axiomatic foundation of preferences for which utility-maximization is equivalent to minimizing a utility-based shortfall risk measure. Our axiomatization hinges on a novel axiom in decision theory, namely the risk-perception axiom. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.07269&r=rmg |
| By: | Alexej Brauer |
| Abstract: | Currently, there is a lot of research in the field of neural networks for non-life insurance pricing. The usual goal is to improve the predictive power via neural networks while building upon the generalized linear model, which is the current industry standard. Our paper contributes to this current journey via novel methods to enhance actuarial non-life models with transformer models for tabular data. We build here upon the foundation laid out by the combined actuarial neural network as well as the localGLMnet and enhance those models via the feature tokenizer transformer. The manuscript demonstrates the performance of the proposed methods on a real-world claim frequency dataset and compares them with several benchmark models such as generalized linear models, feed-forward neural networks, combined actuarial neural networks, LocalGLMnet, and pure feature tokenizer transformer. The paper shows that the new methods can achieve better results than the benchmark models while preserving certain generalized linear model advantages. The paper also discusses the practical implications and challenges of applying transformer models in actuarial settings. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.07597&r=rmg |
| By: | Mohammad Hosein HoushmandRad (Department of Civil Engineering, Estahban Branch, Islamic Azad University, Estahban, Iran); Mohammadreza Fetanatfardhaghighi (Department of Civil Engineering, Estahban Branch, Islamic Azad University, Estahban, Iran) |
| Abstract: | Since in order for developing countries to enter the chain of powerful countries in the world, we need to achieve development, and one of the main indicators of development and achieving sustainable development in today's world is that countries have developed organizations in both the private and public sectors. Managers of organizations today more than ever should be looking for sustainable development foundations, hence the role of active organizations in sustainable development has been more and more noticed by experts and practitioners of human societies. From the point of view of these people, the lack of attention and adherence of organizations and subordinate managers to their duty and social responsibility is one of the issues and problems that can hinder the achievement of sustainable development. One of the most important results of following the principles of social responsibility is sustainable development and welfare of society. Therefore, organizations, both public and private, should be pioneers in adhering to social responsibility beyond their defined legal responsibilities and take an important step towards achieving sustainable development. In this regard, the present book under the title of advanced strategic management can act as a beacon for managers of organizations and students in the fields of industrial engineering, civil engineering, business management and public administration. The preceding book consists of eleven chapters. From the first to the ninth chapter, it introduces and explains the different parts of strategic management in the three areas of planning, execution and control for implementation in the organization, and in the continuation of the descriptive basics of the tenth chapter of this book, it introduces risk, risk management, and relevant models in the field of risk management. And finally the theoretical foundations of value engineering and other management methods in the field of quality improvement are presented in the eleventh chapter. |
| Keywords: | Strategic Management, Strategic Planning, Risk Management, Earned value, Quality Management |
| Date: | 2023–09–23 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04254927&r=rmg |
| By: | Nick James; Max Menzies |
| Abstract: | We introduce new mathematical methods to study the optimal portfolio size of investment portfolios over time, considering investors with varying levels of ability. First, we explore the benefit of portfolio diversification on an annual basis for poor, average and strong investors defined by the 10th, 50th and 90th percentiles of risk-adjusted returns, respectively. Second, we conduct a thorough regression experiment examining quantiles of risk-adjusted return as a function of portfolio size across investor ability, testing for trends and curvature within these functions. Finally, we study the optimal portfolio size for poor, average and strong investors in a continuously temporal manner using more than 20 years of data. We show that strong investors should hold smaller (more concentrated) portfolios, poor investors should hold larger (more diversified) portfolios, while average investors have a less obvious distribution with the optimal number varying materially over time. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06519&r=rmg |
| By: | Leonard Mushunje; Maxwell Mashasha; Edina Chandiwana |
| Abstract: | The fundamental theorem behind financial markets is that stock prices are intrinsically complex and stochastic. One of the complexities is the volatility associated with stock prices. Volatility is a tendency for prices to change unexpectedly [1]. Price volatility is often detrimental to the return economics, and thus, investors should factor it in whenever making investment decisions, choices, and temporal or permanent moves. It is, therefore, crucial to make necessary and regular short and long-term stock price volatility forecasts for the safety and economics of investors returns. These forecasts should be accurate and not misleading. Different models and methods, such as ARCH GARCH models, have been intuitively implemented to make such forecasts. However, such traditional means fail to capture the short-term volatility forecasts effectively. This paper, therefore, investigates and implements a combination of numeric and probabilistic models for short-term volatility and return forecasting for high-frequency trades. The essence is that one-day-ahead volatility forecasts were made with Gaussian Processes (GPs) applied to the outputs of a Numerical market prediction (NMP) model. Firstly, the stock price data from NMP was corrected by a GP. Since it is not easy to set price limits in a market due to its free nature and randomness, a Censored GP was used to model the relationship between the corrected stock prices and returns. Forecasting errors were evaluated using the implied and estimated data. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.10935&r=rmg |
| By: | Rawin Assabumrungrat; Kentaro Minami; Masanori Hirano |
| Abstract: | Option pricing, a fundamental problem in finance, often requires solving non-linear partial differential equations (PDEs). When dealing with multi-asset options, such as rainbow options, these PDEs become high-dimensional, leading to challenges posed by the curse of dimensionality. While deep learning-based PDE solvers have recently emerged as scalable solutions to this high-dimensional problem, their empirical and quantitative accuracy remains not well-understood, hindering their real-world applicability. In this study, we aimed to offer actionable insights into the utility of Deep PDE solvers for practical option pricing implementation. Through comparative experiments, we assessed the empirical performance of these solvers in high-dimensional contexts. Our investigation identified three primary sources of errors in Deep PDE solvers: (i) errors inherent in the specifications of the target option and underlying assets, (ii) errors originating from the asset model simulation methods, and (iii) errors stemming from the neural network training. Through ablation studies, we evaluated the individual impact of each error source. Our results indicate that the Deep BSDE method (DBSDE) is superior in performance and exhibits robustness against variations in option specifications. In contrast, some other methods are overly sensitive to option specifications, such as time to expiration. We also find that the performance of these methods improves inversely proportional to the square root of batch size and the number of time steps. This observation can aid in estimating computational resources for achieving desired accuracies with Deep PDE solvers. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.07231&r=rmg |
| By: | Jirong Zhuang; Deng Ding; Weiguo Lu; Xuan Wu; Gangnan Yuan |
| Abstract: | Machine learning methods, such as Gaussian process regression (GPR), have been widely used in recent years for pricing American options. The GPR is considered as a potential method for estimating the continuation value of an option in the regression-based Monte Carlo method. However, it has some drawbacks, such as high computational cost and unreliability in high-dimensional cases. In this paper, we apply the Deep Kernel Learning with variational inference to the regression-based Monte Carlo method in order to overcome those drawbacks for high-dimensional American option pricing, and test its performance under geometric Brownian motion and Merton's jump diffusion models. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.07211&r=rmg |