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on Risk Management |
| By: | Naimoli, Antonio |
| Abstract: | The aim of this paper is to investigate the impact of public sentiment on tail risk forecasting. In this framework, we extend the Realized Exponential GARCH model to directly incorporate information from realized volatility measures and exogenous variables. Several indices related to social media and journal articles regarding the economy and stock market volatility are considered as potential drivers of volatility dynamics. An application to the prediction of daily Value at Risk and Expected Shortfall for the Standard & Poor's 500 index provides evidence that combining the information content of realized volatility and sentiment measures can lead to significant accuracy gains in forecasting tail risk. |
| Keywords: | Realized Exponential GARCH; sentiment indices; economic policy uncertainty; tail risk forecasting; risk management. |
| JEL: | C22 C53 C58 D80 E66 G32 |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:112588&r= |
| By: | Rama Cont; Mihai Cucuringu; Renyuan Xu; Chao Zhang |
| Abstract: | The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components, with particular importance devoted to the simulation of tail risk scenarios. Commonly used parametric models have been successful in applications involving a small number of assets, but may not be scalable to large or heterogeneous portfolios involving multiple asset classes. We propose a novel data-driven approach for the simulation of realistic multi-asset scenarios with a particular focus on the accurate estimation of tail risk for a given class of static and dynamic portfolios selected by the user. By exploiting the joint elicitability property of Value-at-Risk (VaR) and Expected Shortfall (ES), we design a Generative Adversarial Network (GAN) architecture capable of learning to simulate price scenarios that preserve tail risk features for these benchmark trading strategies, leading to consistent estimators for their Value-at-Risk and Expected Shortfall. We demonstrate the accuracy and scalability of our method via extensive simulation experiments using synthetic and market data. Our results show that, in contrast to other data-driven scenario generators, our proposed scenario simulation method correctly captures tail risk for both static and dynamic portfolios. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.01664&r= |
| By: | Godwin, Alexander |
| Abstract: | This paper introduces a novel method for estimating the alpha and beta of hedge fund indices that corrects for stale pricing in reported returns. This approach can be further used to estimate volatility and other risk measures. We apply this technique to a composite hedge fund index and six strategy indices provided by HFR. Once corrected for stale pricing, we find these indices exhibit higher betas and volatility with negative or statistically insignificant positive alpha. |
| Keywords: | hedge funds; alternative investments; stale pricing; risk; beta; alpha; asset allocation; volatility |
| JEL: | G10 G11 G12 |
| Date: | 2022–03–18 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:112509&r= |
| By: | Puneet Pasricha; Dharmaraja Selvamuthu; Selvaraju Natarajan |
| Abstract: | The modeling of the probability of joint default or total number of defaults among the firms is one of the crucial problems to mitigate the credit risk since the default correlations significantly affect the portfolio loss distribution and hence play a significant role in allocating capital for solvency purposes. In this article, we derive a closed-form expression for the probability of default of a single firm and the probability of the total number of defaults by any time $t$ in a homogeneous portfolio of firms. We use a contagion process to model the arrival of credit events that causes the default and develop a framework that allows firms to have resistance against default unlike the standard intensity-based models. We assume the point process driving the credit events to be composed of a systematic and an idiosyncratic component, whose intensities are independently specified by a mean-reverting affine jump-diffusion process with self-exciting jumps. The proposed framework is competent of capturing the feedback effect, an empirically observed phenomenon in the default events. We further demonstrate how the proposed framework can be used to price synthetic collateralized debt obligation (CDO) and obtain a closed-form solution for tranche spread. Finally, we present the sensitivity analysis to demonstrate the effect of different parameters governing the contagion effect on the spread of tranches and the expected loss of the CDO. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.12946&r= |
| By: | Baishuai Zuo; Chuancun Yin |
| Abstract: | In this paper, we focus on multivariate doubly truncated first two moments of generalized skew-elliptical (GSE) distributions and derive explicit expressions for them. It includes many useful distributions, for examples, generalized skew-normal (GSN), generalized skew-Laplace (GSLa), generalized skew-logistic (GSLo) and generalized skew student-$t$ (GSSt) distributions, all as special cases. We also give formulas of multivariate doubly truncated expectation and covariance for GSE distributions. As applications, we show the results of multivariate tail conditional expectation (MTCE) and multivariate tail covariance (MTCov) for GSE distributions. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.00839&r= |
| By: | Lotfi Boudabsa (Ecole Polytechnique Fédérale de Lausanne - School of Basic Sciences); Damir Filipović (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute) |
| Abstract: | We introduce an ensemble learning method for dynamic portfolio valuation and risk management building on regression trees. We learn the dynamic value process of a derivative portfolio from a finite sample of its cumulative cash flow. The estimator is given in closed form. The method is fast and accurate, and scales well with sample size and path space dimension. The method can also be applied to Bermudan style options. Numerical experiments show good results in moderate dimension problems. |
| Keywords: | dynamic portfolio valuation, ensemble learning, gradient boosting, random forest, regression trees, risk management, Bermudan options |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2230&r= |
| By: | Tobias Fissler; Silvana M. Pesenti |
| Abstract: | We propose a holistic framework for constructing sensitivity measures for any elicitable functional $T$ of a response variable. The sensitivity measures, termed score-based sensitivities, are constructed via scoring functions that are (strictly) consistent for $T$. These score-based sensitivities quantify the relative improvement in predictive accuracy when available information, e.g., from explanatory variables, is used ideally. We establish intuitive and desirable properties of these sensitivities and discuss advantageous choices of scoring functions leading to scale-invariant sensitivities. Since elicitable functionals typically possess rich classes of (strictly) consistent scoring functions, we demonstrate how Murphy diagrams can provide a picture of all score-based sensitivity measures. We discuss the family of score-based sensitivities for the mean functional (of which the Sobol indices are a special case) and risk functionals such as Value-at-Risk, and the pair Value-at-Risk and Expected Shortfall. The sensitivity measures are illustrated using numerous examples, including the Ishigami--Homma test function and applications to a non-linear insurance portfolio. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.00460&r= |
| By: | {\O}yvind Grotmol; Martin Jullum; Kjersti Aas; Michael Scheuerer |
| Abstract: | Quantifying both historic and future volatility is key in portfolio risk management. This note presents and compares estimation strategies for volatility estimation in an estimation universe consisting on 28 629 unique companies from February 2010 to April 2021, with 858 different portfolios. The estimation methods are compared in terms of how they rank the volatility of the different subsets of portfolios. The overall best performing approach estimates volatility from direct entity returns using a GARCH model for variance estimation. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.12402&r= |
| By: | Silvia Sarpietro; Yuya Sasaki; Yulong Wang |
| Abstract: | Common moment-based measures of earnings risk, including the variance, skewness, and kurtosis, may be undefined in population under heavy-tailed distributions. In this light, we propose conditional Pareto exponents as novel measures of earnings risk that are robust against non-existence of moments, and develop estimation and inference methods for them. Using these measures with an administrative data set for the UK, the New Earnings Survey Panel Dataset (NESPD), and the US Panel Study of Income Dynamics (PSID), we quantify the tail heaviness of the conditional distributions of earnings changes given age, gender, and past earnings. Our main findings are that: 1) the population kurtosis, skewness, and even variance may fail to exist for the conditional distribution of earnings growth; 2) earnings risk is increasing over the life cycle; 3) job stayers are more vulnerable to earnings risk, and 4) these patterns appear in both the period 2007-2008 of great recession and the period 2015-2016 of a positive growth among others. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.08014&r= |
| By: | Baishuai Zuo; Chuancun Yin |
| Abstract: | In this paper, we define doubly truncated moment (DTM), doubly truncated skewness (DTS) and kurtosis (DTK). We derive DTM formulae for elliptical family, with emphasis on normal, student-$t$, logistic, Laplace and Pearson type VII distributions. We also present explicit formulas of the DTE (doubly truncated expectation), DTV (doubly truncated variance), DTS and DTK for those distributions. As illustrative example, DTEs, DTVs, DTSs and DTKs of three industry segments' (Banks, Insurance, Financial and Credit Service) stock return in London stock exchange are discussed. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.01091&r= |
| By: | Maria Teresa Medeiros Garcia; Ana Jin Ye |
| Abstract: | The aim of this paper is to study the relation between banks’ ownership structure and their risk-taking behavior. Additionally, we examine the impact of banking regulation on banks’ approach to taking risk. The empirical analysis considers a sample of listed banks from EU countries over the period of 2011 to 2016. We found that the structure of the board of directors can influence bank risk behavior but not the ownership concentration. No significant relation was found between the influence of the regulatory environment and bank risk, i.e., stricter regulation has no effect on risk taking by banks. |
| Keywords: | Banks; Risk; Corporate governance; Regulation; EU countries. |
| JEL: | G21 G32 G34 G38 |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:ise:remwps:wp02252022&r= |
| By: | Yuanying Guan; Zhanyi Jiao; Ruodu Wang |
| Abstract: | The celebrated Expected Shortfall (ES) optimization formula implies that ES at a fixed probability level is the minimum of a linear real function plus a scaled mean excess function. We establish a reverse ES optimization formula, which says that a mean excess function at any fixed threshold is the maximum of an ES curve minus a linear function. Despite being a simple result, this formula reveals elegant symmetries between the mean excess function and the ES curve, as well as their optimizers. The reverse ES optimization formula is closely related to the Fenchel-Legendre transforms, and our formulas are generalized from ES to optimized certainty equivalents, a popular class of convex risk measures. We analyze worst-case values of the mean excess function under two popular settings of model uncertainty to illustrate the usefulness of the reverse ES optimization formula, and this is further demonstrated with an application using insurance datasets. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.02599&r= |
| By: | Alim, Wajid; Ali, Amjad; Metla, Mahwish Rauf |
| Abstract: | The study tests the effect of liquidity risk management on the financial performance of commercial banks in Pakistan. Pakistani financial market is heavily dependent on its banking sector to achieve its financial goals and stability. Therefore, the banking sector’s performance has a significant effect on the overall economy of the country. To achieve its need for stability, the central bank of Pakistan ensures that banks maintain an optimum liquidity position to reap the most benefits and increase returns. In this study, the effect of liquidity risk management on financial performance is studied using panel data for Ordinary Least Square analysis. Financial data of all commercial banks operating in Pakistan during the period of study was taken from the year 2006 to 2019 using data archives of the State Bank of Pakistan website. It is concluded that higher liquidity increases banks’ performance in commercial banks of Pakistan. The results are in line with several studies and available literature. This study can become a good reference for future policy decisions regarding the minimum liquidity requirements of banks in this region. This study can be further enhanced using a longer period of study and include more variables specific to the banking sector in Pakistan, like bank size, age of bank, etc. Further studies may include other non-commercial banks to further strengthen the study and increase its reliability. |
| Keywords: | Liquidity Risk; Performance; Banking Sector; ROA; ROE; Pakistan |
| JEL: | G21 G33 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:112482&r= |
| By: | Jonas Crevecoeur; Katrien Antonio; Stijn Desmedt; Alexandre Masquelein |
| Abstract: | Due to the presence of reporting and settlement delay, claim data sets collected by non-life insurance companies are typically incomplete, facing right censored claim count and claim severity observations. Current practice in non-life insurance pricing tackles these right censored data via a two-step procedure. First, best estimates are computed for the number of claims that occurred in past exposure periods and the ultimate claim severities, using the incomplete, historical claim data. Second, pricing actuaries build predictive models to estimate technical, pure premiums for new contracts by treating these best estimates as actual observed outcomes, hereby neglecting their inherent uncertainty. We propose an alternative one step approach suitable for both non-life pricing and reserving. As such we effectively bridge these two key actuarial tasks that have traditionally been discussed in silos. Hereto we develop a granular occurrence and development model for non-life claims that allows to resolve the inconsistency in traditional pricing techniques between actual, complete observations on the one hand and best estimates on the other hand. We illustrate our proposed model on a reinsurance portfolio, where large uncertainties in the best estimates originate from long reporting and settlement delays, low claim frequencies and heavy (even extreme) claim sizes. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.07145&r= |
| By: | Benjamin Avanzi; Ping Chen; Lars Frederik Brandt Henriksen; Bernard Wong |
| Abstract: | In this paper we consider a company whose assets and liabilities evolve according to a correlated bivariate geometric Brownian motion, such as in Gerber and Shiu (2003). We determine what dividend strategy maximises the expected present value of dividends until ruin in two cases: (i) when shareholders won't cover surplus shortfalls and a solvency constraint (as in Paulsen, 2003) is consequently imposed, and (ii) when shareholders are always to fund any capital deficiency with capital (asset) injections. In the latter case, ruin will never occur and the objective is to maximise the difference between dividends and capital injections. Developing and using appropriate verification lemmas, we show that the optimal dividend strategy is, in both cases, of barrier type. Both value functions are derived in closed form. Furthermore, the barrier is defined on the ratio of assets to liabilities, which mimics some of the dividend strategies that can be observed in practice by insurance companies. Existence and uniqueness of the optimal strategies are shown. Results are illustrated. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.05139&r= |
| By: | Yevhen Havrylenko; Maria Hinken; Rudi Zagst |
| Abstract: | Equity-linked insurance products often have capital guarantees. Common investment strategies ensuring these guarantees are challenged nowadays by low interest rates. Thus, we study an alternative strategy when an insurance company shares financial risk with a reinsurance company. We model this situation as a Stackelberg game. The reinsurer is the leader in the game and maximizes its expected utility by selecting its optimal investment strategy and a safety loading in the reinsurance contract it offers to the insurer. The reinsurer can assess how the insurer will rationally react on each action of the reinsurer. The insurance company is the follower and maximizes its expected utility by choosing its investment strategy and the amount of reinsurance the company purchases at the price offered by the reinsurer. In this game, we derive the Stackelberg equilibrium for general utility functions. For power utility functions, we calculate the equilibrium explicitly and find that the reinsurer selects the largest reinsurance premium such that the insurer may still buy the maximal amount of reinsurance. Since in the equilibrium the insurer is indifferent in the amount of reinsurance, in practice, the reinsurer should consider charging a smaller reinsurance premium than the equilibrium one. Therefore, we propose several criteria for choosing such a discount rate and investigate its wealth-equivalent impact on the utilities of both parties. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.04053&r= |
| By: | Benjamin Avanzi; Mark Lavender; Greg Taylor; Bernard Wong |
| Abstract: | Traditional techniques for calculating outstanding claim liabilities such as the chain ladder are notoriously at risk of being distorted by outliers in past claims data. Unfortunately, the literature in robust methods of reserving is scant, with notable exceptions such as Verdonck and Debruyne (2011) and Verdonck and Van Wouwe (2011). In this paper, we put forward two alternative robust bivariate chain-ladder techniques to extend the approach of Verdonck and Van Wouwe (2011). The first technique is based on Adjusted Outlyingness (Hubert and Van der Veeken, 2008) and explicitly incorporates skewness into the analysis whilst providing a unique measure of outlyingness for each observation. The second technique is based on bagdistance (Hubert et al., 2016) which is derived from the bagplot however is able to provide a unique measure of outlyingness and a means to adjust outlying observations based on this measure. Subsequently, we also extend our robust bivariate chain-ladder approach to an N-dimensional framework. This is illustrated on a trivariate data set from Australian general insurers, and results under the different outlier detection and treatment mechanisms are compared. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.03874&r= |
| By: | Heard, Claire Louise; Rakow, Tim |
| Abstract: | Affect can influence judgments of event riskiness and use of risk-related information. Two studies (Ns: 85 and 100) examined the insensitivity-to-probability effect—where people discount probability information when scenarios are affect-rich—applying it to evidence-informed risk communication. We additionally investigated whether this effect is moderated by format, based on predictions from the evaluability and pattern-recognition literatures, suggesting that graphical formats may attenuate insensitivity to probability. Participants completed a prior beliefs questionnaire (Study 1), and risk perception booklet (both studies) that presented identical statistical information about the relative risks associated with two scenarios—one with an affect-rich outcome, the other an affect-poorer outcome. In Study 1, this was presented graphically. In Study 2, information was presented in one of three formats: written, tabular, or graphical. Participants provided their perceptions of the risk for each scenario at a range of risk-levels. The affect-rich scenario was perceived as higher in risk, and, importantly, despite presenting identical relative risk information in both scenarios, was associated with a reduced sensitivity to probability information (both studies). These differences were predicted by participants’ prior beliefs concerning the scenario events (Study 1) and were larger for the single-item written format than graphical format (Study 2). The findings illustrate that insensitivity to probability information can occur in evidence-informed risk communications and highlight how communication format can moderate this effect. This interplay between affect and format therefore reflects an important consideration for information designers and researchers. |
| Keywords: | affect; information format; insensitivity-to-probability effect; risk communication; risk perception; sensitivity to probabilities; PhD studentship/Winton Fund |
| JEL: | G32 |
| Date: | 2021–11–27 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:113810&r= |
| By: | Cecilia Parlatore; Thomas Philippon |
| Abstract: | We develop a tractable framework to study the optimal design of stress scenarios. A principal wants to manage the unknown risk exposures of a set of agents. She asks the agents to report their losses under hypothetical scenarios before mandating actions to mitigate the exposures. We show how to apply a Kalman filter to solve the learning problem and we characterize the scenario design as a function of the risk environment, the principal’s preferences, and the available remedial actions. We apply our results to banking stress tests. We show how the principal learns from estimated losses under different scenarios and across different banks. Optimal capital requirements are set to cover losses under an adverse scenario while targeted interventions depend on the covariance between residual exposure uncertainty and physical risks. |
| JEL: | D8 G2 H12 |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29901&r= |
| By: | Jun Lu; Shao Yi |
| Abstract: | SVR-GARCH model tends to "backward eavesdrop" when forecasting the financial time series volatility in which case it tends to simply produce the prediction by deviating the previous volatility. Though the SVR-GARCH model has achieved good performance in terms of various performance measurements, trading opportunities, peak or trough behaviors in the time series are all hampered by underestimating or overestimating the volatility. We propose a blending ARCH (BARCH) and an augmented BARCH (aBARCH) model to overcome this kind of problem and make the prediction towards better peak or trough behaviors. The method is illustrated using real data sets including SH300 and S&P500. The empirical results obtained suggest that the augmented and blending models improve the volatility forecasting ability. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.12456&r= |
| By: | Florian Eckert (ETH Zurich, Switzerland); Heiner Mikosch (ETH Zurich, Switzerland) |
| Abstract: | This paper examines the incidence of rm bankruptcies and start-ups in Switzerland based on unique register data. We propose to assess the frequency of bankruptcies over time using the concept of excess mortality. During the Corona crisis in 2020 and the rst half of 2021, bankruptcy rates were substantially lower and the number of new rm formations was substantially higher as compared to the pre-crisis period. This holds across most industries and regions. The Great Recession and the Swiss Franc Shock showed reverse patterns. Bankruptcies dropped more in industries and cantons, in which the share of rms who received a Covid-19 loan is comparatively high. The strong start-up activity is driven by industries where the pandemic induced structural adjustments. |
| Keywords: | Firm Bankruptcies, Insolvencies, Excess Mortality, Firm Formations, Start-Ups, Switzerland, Corona Crisis, Industry-Level, Canton-Level |
| JEL: | E32 G33 M13 |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:kof:wpskof:21-499&r= |
| By: | Christian Bayer; Masaaki Fukasawa; Shonosuke Nakahara |
| Abstract: | We study the weak convergence rate in the discretization of rough volatility models. After showing a lower bound $2H$ under a general model, where $H$ is the Hurst index of the volatility process, we give a sharper bound $H + 1/2$ under a linear model. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.02943&r= |
| By: | Miguel Angel Ruiz-Ortiz; Jos\'e Carlos G\'omez-Larra\~naga; Jes\'us Rodr\'iguez-Viorato |
| Abstract: | The Topological Data Analysis (TDA) has had many applications. However, financial markets has been studied slightly through TDA. Here we present a quick review of some recent applications of TDA on financial markets and propose a new turbulence index based on persistent homology -- the fundamental tool for TDA -- that seems to capture critical transitions on financial data, based on our experiment with SP500 data before 2020 stock market crash in February 20, 2020, due to the COVID-19 pandemic. We review applications in the early detection of turbulence periods in financial markets and how TDA can help to get new insights while investing and obtain superior risk-adjusted returns compared with investing strategies using classical turbulence indices as VIX and the Chow's index based on the \textit{Mahalanobis} distance. Furthermore, we include an introduction to persistent homology so the reader could be able to understand this paper without knowing TDA. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.05603&r= |
| By: | Raad Khraishi; Ramin Okhrati |
| Abstract: | We introduce a method for pricing consumer credit using recent advances in offline deep reinforcement learning. This approach relies on a static dataset and requires no assumptions on the functional form of demand. Using both real and synthetic data on consumer credit applications, we demonstrate that our approach using the conservative Q-Learning algorithm is capable of learning an effective personalized pricing policy without any online interaction or price experimentation. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.03003&r= |