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on Risk Management |
| By: | Qiuqi Wang; Ruodu Wang; Johanna Ziegel |
| Abstract: | In the recent Basel Accords, the Expected Shortfall (ES) replaces the Value-at-Risk (VaR) as the standard risk measure for market risk in the banking sector, making it the most important risk measure in financial regulation. One of the most challenging tasks in risk modeling practice is to backtest ES forecasts provided by financial institutions. Ideally, backtesting should be done based only on daily realized portfolio losses without imposing specific models. Recently, the notion of e-values has gained attention as potential alternatives to p-values as measures of uncertainty, significance and evidence. We use e-values and e-processes to construct a model-free backtesting procedure for ES using a concept of universal e-statistics, which can be naturally generalized to many other risk measures and statistical quantities. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.00991&r= |
| By: | W. Scott Frame; Nika Lazaryan; Ping McLemore; Atanas Mihov |
| Abstract: | Using supervisory data from large U.S. bank holding companies (BHCs), we document that operational loss recovery rates decrease in macroeconomic downturns. This procyclical relationship varies by business lines and loss event types and is robust to alternative data aggregations, macroeconomic measurement horizons and estimation methodologies. Further analysis shows that resource constraints faced by BHC risk management functions are a plausible explanation for these patterns. Our findings offer new evidence on how economic shocks transmit to banking industry losses with implications for risk management and supervision. |
| Keywords: | Operational risk; Operational losses; Loss recoveries; Macroeconomic environment; Banking sector |
| JEL: | G21 G28 G29 |
| Date: | 2022–09–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddwp:94718&r= |
| By: | Zhanyi Jiao; Yang Liu; Ruodu Wang |
| Abstract: | We study an axiomatic framework for anonymized risk sharing. In contrast to traditional risk sharing settings, our framework requires no information on preferences, identities, private operations and realized losses from the individual agents, and thereby it is useful for modeling risk sharing in decentralized systems. Four axioms natural in such a framework -- actuarial fairness, risk fairness, risk anonymity, and operational anonymity -- are put forward and discussed. We establish the remarkable fact that the four axioms characterizes the conditional mean risk sharing rule, revealing the unique and prominent role of this popular risk sharing rule among all others in relevant applications of anonymized risk sharing. Several other properties and their relations to the four axioms are studied. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.07533&r= |
| By: | Jaydip Sen; Arpit Awad; Aaditya Raj; Gourav Ray; Pusparna Chakraborty; Sanket Das; Subhasmita Mishra |
| Abstract: | The stock market offers a platform where people buy and sell shares of publicly listed companies. Generally, stock prices are quite volatile; hence predicting them is a daunting task. There is still much research going to develop more accuracy in stock price prediction. Portfolio construction refers to the allocation of different sector stocks optimally to achieve a maximum return by taking a minimum risk. A good portfolio can help investors earn maximum profit by taking a minimum risk. Beginning with Dow Jones Theory a lot of advancement has happened in the area of building efficient portfolios. In this project, we have tried to predict the future value of a few stocks from six important sectors of the Indian economy and also built a portfolio. As part of the project, our team has conducted a study of the performance of various Time series, machine learning, and deep learning models in stock price prediction on selected stocks from the chosen six important sectors of the economy. As part of building an efficient portfolio, we have studied multiple portfolio optimization theories beginning with the Modern Portfolio theory. We have built a minimum variance portfolio and optimal risk portfolio for all the six chosen sectors by using the daily stock prices over the past five years as training data and have also conducted back testing to check the performance of the portfolio. We look forward to continuing our study in the area of stock price prediction and asset allocation and consider this project as the first stepping stone. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.07166&r= |
| By: | Sérgio Leão; Rafael Schiozer; Raquel F. Oliveira; Gustavo Araujo |
| Abstract: | Firms’ currency exposure may result in financial distress and trigger macroeconomic instability. Such exposure can be hedged using currency over-the-counter derivatives. We investigate whether and how lending relationships affect the access to these derivatives using novel loan and derivatives microdata. We document that firms are more likely to buy derivatives from one of their lenders than from a non-lending bank. We also find that prices are lower for derivatives provided by the main lender. These results are stronger among small firms. Our findings are consistent with lending relationships mitigating information asymmetries and derivatives reducing a bank’s loan portfolio risk. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:565&r= |
| By: | Jos\'e A. Salmer\'on; Giulia Di Nunno; Bernardo D'Auria |
| Abstract: | Default risk calculus emerges naturally in a portfolio optimization problem when the risky asset is threatened with a bankruptcy. The usual stochastic control techniques do not hold in this case and some additional assumptions are generally added to achieve the optimization in a before-and-after default context. We show how it is possible to avoid one of theses restrictive assumptions, the so-called Jacod density hypothesis, by using the framework of the forward integration. In particular, in the logarithmic utility case, in order to get the optimal portfolio the right condition it is proved to be the intensity hypothesis. We use the anticipating calculus to analyze the existence of the optimal portfolio for the logarithmic utility, and than under the assumption of existence of the optimal portfolio we prove the semimartingale decomposition of the risky asset in the filtration enlarged with the default process. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.07163&r= |
| By: | Mikl\'os R\'asonyi; Hasanjan Sayit |
| Abstract: | Obtaining utility maximizing optimal portfolios in closed form is a challenging issue when the return vector follows a more general distribution than the normal one. In this note, we give closed form expressions, in markets based on finitely many assets, for optimal portfolios that maximize the expected exponential utility when the return vector follows normal mean-variance mixture models. We then consider large financial markets based on normal mean-variance mixture models also and show that, under exponential utility, the optimal utilities based on small markets converge to the optimal utility in the large financial market. This result shows, in particular, that to reach optimal utility level investors need to diversify their portfolios to include infinitely many assets into their portfolio and with portfolios based on any set of only finitely many assets, they never be able to reach optimum level of utility. In this paper, we also consider portfolio optimization problems with more general class of utility functions and provide an easy-to-implement numerical procedure for locating optimal portfolios. Especially, our approach in this part of the paper reduces a high dimensional problem in locating optimal portfolio into a three dimensional problem for a general class of utility functions. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.06549&r= |
| By: | Ricard Durall |
| Abstract: | Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets according to certain goals, risk tolerance, and investment horizon. Unfortunately, there is no simple formula that can find the right allocation for every individual. As a result, investors may use different asset allocations' strategy to try to fulfil their financial objectives. In this work, we conduct an extensive benchmark study to determine the efficacy and reliability of a number of optimization techniques. In particular, we focus on traditional approaches based on Modern Portfolio Theory, and on machine-learning approaches based on deep reinforcement learning. We assess the model's performance under different market tendency, i.e., both bullish and bearish markets. For reproducibility, we provide the code implementation code in this repository. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.07158&r= |
| By: | Leandro Gualario (Auteur indépendant) |
| Abstract: | "A reference to Aristotle and Leibniz has long ceased to be required in serious books", said Benoit Mandelbrot, father of fractal geometry. A reference to Nietzsche could increasingly become a requirement in economics books. The purpose of this work is to highlight the epistemological proximity between Nietzsche's and the underlying philosophical principles of fractal geometry. This work also aims to find the end of this philosophical continuity, finding an important divergence in their different approach to risk. |
| Keywords: | Nietzsche,Mandelbrot,Epistemology,Mathematics,Fractal Geometry,Finance,Risk,Risk Management,Models |
| Date: | 2022–07–19 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03727161&r= |
| By: | Danial Saef; Yuanrong Wang; Tomaso Aste |
| Abstract: | The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address the unorthodox market dynamics and frequent disruptions in the microstructure caused by the non-stationarity, and peculiar statistics, in DA markets. However, they are either prone to the curse of dimensionality, as additional complexity is required to employ traditional theories, or they overfit historical patterns that may never repeat. Instead, we leverage recent advances in market regime (MR) clustering with the Implied Stochastic Volatility Model (ISVM). Time-regime clustering is a temporal clustering method, that clusters the historic evolution of a market into different volatility periods accounting for non-stationarity. ISVM can incorporate investor expectations in each of the sentiment-driven periods by using implied volatility (IV) data. In this paper, we applied this integrated time-regime clustering and ISVM method (termed MR-ISVM) to high-frequency data on BTC options at the popular trading platform Deribit. We demonstrate that MR-ISVM contributes to overcome the burden of complex adaption to jumps in higher order characteristics of option pricing models. This allows us to price the market based on the expectations of its participants in an adaptive fashion. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.12614&r= |
| By: | Finer, David Andrew |
| Abstract: | Do investors correctly price extreme events that they have never seen occur? To shed light on this question, I examine market responses to the risk of nuclear war during the Cuban Missile Crisis. I find evidence that investors indeed priced firms' exposures to nuclear destruction: Firms headquartered in areas that American national-security experts and the general public perceived more at risk of nuclear destruction experienced lower returns. Such discrimination is plausible given contemporary survey evidence that investors generally believed that the US could recover from a nuclear war. Employing a calibrated model to reconcile survey expectations with aggregate market responses, I find that i.) Investors underreacted to the risk of nuclear war; ii.) Investors exhibited a lower level of risk aversion than is standard in the literature; or iii.) Investor heterogeneity or noise makes survey data inaccurate indicators of investors' perceived exposures to extreme risks. |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cbscwp:318&r= |
| By: | Yao Luo; Hidenori Takahashi |
| Abstract: | Procurement projects often involve substantial uncertainty in inputs at the time of contracting. Whether the procurer or contractor assumes such risk depends on the specific contractual agreement. We develop a model of auction contracts where bidders have multidimensional private information. Bidders balance skewed bidding and risk exposure; both efficient and inefficient bidders submit a low bid via skewed bidding. We document evidence of i) risk-balancing behavior through bid portfolio formation and ii) opportunistic behavior via skewed bidding using auction data. Counterfactual experiments suggest the onus of bearing project risk should fall on the procurer (contractor) when project risk is large (small). |
| Keywords: | Contract, Unit-Price, Fixed-Price, Portfolio, Cost Overrun, Procurement, Scoring Auction |
| JEL: | L5 |
| Date: | 2022–09–01 |
| URL: | http://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-732&r= |
| By: | Jun Lu; Danny Ding |
| Abstract: | Over the decades, the Markowitz framework has been used extensively in portfolio analysis though it puts too much emphasis on the analysis of the market uncertainty rather than on the trend prediction. While generative adversarial network (GAN), conditional GAN (CGAN), and autoencoding CGAN (ACGAN) have been explored to generate financial time series and extract features that can help portfolio analysis. The limitation of the CGAN or ACGAN framework stands in putting too much emphasis on generating series and finding the internal trends of the series rather than predicting the future trends. In this paper, we introduce a hybrid approach on conditional GAN based on deep generative models that learns the internal trend of historical data while modeling market uncertainty and future trends. We evaluate the model on several real-world datasets from both the US and Europe markets, and show that the proposed HybridCGAN and HybridACGAN models lead to better portfolio allocation compared to the existing Markowitz, CGAN, and ACGAN approaches. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.07159&r= |
| By: | Ms. Susan E Betts |
| Abstract: | This technical note describes CRM at a high level and how tax administrations can implement a CRM framework to significantly strengthen revenue outcomes. A tax administration’s primary role is to collect revenues on behalf of government to fund the country’s social and economic goals. Taxpayers are expected to comply with their tax obligations as stated in the law. Compliance is the degree to which taxpayers meet their obligations, whether voluntarily or through efforts by the tax administration to enforce compliance. Using CRM allows a country to optimize its revenue collection by identifying and focusing resources on the highest risks to the tax base. While the concepts of CRM are transferable to the customs context, this note focuses on tax administration compliance risks. |
| Keywords: | CRM framework; CRM process; CRM activity; framework to drive revenue performance; compliance risk; Tax administration core functions; Compliance risk management; Revenue mobilization; Compliance improvement plans |
| Date: | 2022–08–26 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imftnm:2022/005&r= |
| By: | Sojung Kim; Marcel Kleiber; Stefan Weber |
| Abstract: | The paper develops a methodology to enable microscopic models of transportation systems to be accessible for a statistical study of traffic accidents. Our approach is intended to permit an understanding not only of historical losses, but also of incidents that may occur in altered, potential future systems. Through this, it is possible, from both an engineering and insurance perspective, to assess changes in the design of vehicles and transport systems in terms of their impact on functionality and road safety. Structurally, we characterize the total loss distribution approximatively as a mean-variance mixture. This also yields valuation procedures that can be used instead of Monte Carlo simulation. Specifically, we construct an implementation based on the open-source traffic simulator SUMO and illustrate the potential of the approach in counterfactual case studies. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.12530&r= |
| By: | Nidhi Aggarwal (Indian Institute of Management, Udaipur); Manish K. Singh (Indian Institute of Technology, Roorkee); Susan Thomas (O. P. Jindal Business School and XKDR Forum) |
| Abstract: | The timeliness of the credit rating of a firm has been frequently called into question over the previous two decades. This paper examines whether changes in credit ratings can be updated more frequently than at the frequency of updates in the accounting data. The paper finds that, when market equity prices of firms are readily available, changes in high frequency measures such as the Distance to Default, along with low frequency firm characteristics such as ownership structure and accounting data, can provide a more timely update on the probability of credit ratings downgrades. |
| JEL: | G21 G24 G32 |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:anf:wpaper:14&r= |
| By: | Kang Gao; Perukrishnen Vytelingum; Stephen Weston; Wayne Luk; Ce Guo |
| Abstract: | This article presents XGB-Chiarella, a powerful new approach for deploying agent-based models to generate realistic intra-day artificial financial price data. This approach is based on agent-based models, calibrated by XGBoost machine learning surrogate. Following the Extended Chiarella model, three types of trading agents are introduced in this agent-based model: fundamental traders, momentum traders, and noise traders. In particular, XGB-Chiarella focuses on configuring the simulation to accurately reflect real market behaviours. Instead of using the original Expectation-Maximisation algorithm for parameter estimation, the agent-based Extended Chiarella model is calibrated using XGBoost machine learning surrogate. It is shown that the machine learning surrogate learned in the proposed method is an accurate proxy of the true agent-based market simulation. The proposed calibration method is superior to the original Expectation-Maximisation parameter estimation in terms of the distance between historical and simulated stylised facts. With the same underlying model, the proposed methodology is capable of generating realistic price time series in various stocks listed at three different exchanges, which indicates the universality of intra-day price formation process. For the time scale (minutes) chosen in this paper, one agent per category is shown to be sufficient to capture the intra-day price formation process. The proposed XGB-Chiarella approach provides insights that the price formation process is comprised of the interactions between momentum traders, fundamental traders, and noise traders. It can also be used to enhance risk management by practitioners. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.14207&r= |
| By: | Mingyu Xu; Zuo Quan Xu; Xun Yu Zhou |
| Abstract: | We define $g$-expectation of a distribution as the infimum of the $g$-expectations of all the terminal random variables sharing that distribution. We present two special cases for nonlinear $g$ where the $g$-expectation of distributions can be explicitly derived. As a related problem, we introduce the notion of law-invariant $g$-expectation and provide its sufficient conditions. Examples of application in financial dynamic portfolio choice are supplied. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.06535&r= |
| By: | Sung Hoon Choi; Donggyu Kim |
| Abstract: | Several large volatility matrix inference procedures have been developed, based on the latent factor model. They often assumed that there are a few of common factors, which can account for volatility dynamics. However, several studies have demonstrated the presence of local factors. In particular, when analyzing the global stock market, we often observe that nation-specific factors explain their own country's volatility dynamics. To account for this, we propose the Double Principal Orthogonal complEment Thresholding (Double-POET) method, based on multi-level factor models, and also establish its asymptotic properties. Furthermore, we demonstrate the drawback of using the regular principal orthogonal component thresholding (POET) when the local factor structure exists. We also describe the blessing of dimensionality using Double-POET for local covariance matrix estimation. Finally, we investigate the performance of the Double-POET estimator in an out-of-sample portfolio allocation study using international stocks from 20 financial markets. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.12323&r= |
| By: | Lech A. Grzelak |
| Abstract: | The class of Affine (Jump) Diffusion (AD) has, due to its closed form characteristic function (ChF), gained tremendous popularity among practitioners and researchers. However, there is clear evidence that a linearity constraint is insufficient for precise and consistent option pricing. Any non-affine model must pass the strict requirement of quick calibration -- which is often challenging. We focus here on Randomized AD (RAnD) models, i.e., we allow for exogenous stochasticity of the model parameters. Randomization of a pricing model occurs outside the affine model and, therefore, forms a generalization that relaxes the affinity constraints. The method is generic and can apply to any model parameter. It relies on the existence of moments of the so-called randomizer- a random variable for the stochastic parameter. The RAnD model allows flexibility while benefiting from fast calibration and well-established, large-step Monte Carlo simulation, often available for AD processes. The article will discuss theoretical and practical aspects of the RAnD method, like derivations of the corresponding ChF, simulation, and computations of sensitivities. We will also illustrate the advantages of the randomized stochastic volatility models in the consistent pricing of options on the S&P 500 and VIX. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.12518&r= |
| By: | Ferraz, Eduardo; Mantilla, César |
| Abstract: | We use a three-period model adopting a recursive definition of consumption to explore the optimal delegation that a present self, aware that her near-future self is present-biased but better informed, will make to protect her far-future self against income shocks. The model captures the present self's trade-off between using commitment mechanisms, restricting the near-future self's agency through illiquid savings, and profiting from the near-future self's better information about future shocks. Our main result states that agents with higher risk aversion can cover better against utility losses from time-inconsistent consumption through the commitment mechanism. Given the evidence of women being more risk-averse than men, this result provides the micro-foundation for the gender gap in adopting financial commitment devices, especially among single individuals. |
| Keywords: | commitment devices; dynamic inconsistency; Epstein-Zin preferences; present bias |
| JEL: | D11 D81 D90 G40 |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:rie:riecdt:97&r= |
| By: | Shunta Akiyama; Mitsuaki Obara; Yasushi Kawase |
| Abstract: | A lottery is a popular form of gambling between a seller and multiple buyers, and its profitable design is of primary interest to the seller. Designing a lottery requires modeling the buyer decision-making process for uncertain outcomes. One of the most promising descriptive models of such decision-making is the cumulative prospect theory (CPT), which represents people's different attitudes towards gain and loss, and their overestimation of extreme events. In this study, we design a lottery that maximizes the seller's profit when the buyers follow CPT. The derived problem is nonconvex and constrained, and hence, it is challenging to directly characterize its optimal solution. We overcome this difficulty by reformulating the problem as a three-level optimization problem. The reformulation enables us to characterize the optimal solution. Based on this characterization, we propose an algorithm that computes the optimal lottery in linear time with respect to the number of lottery tickets. In addition, we provide an efficient algorithm for a more general setting in which the ticket price is constrained. To the best of the authors' knowledge, this is the first study that employs the CPT framework for designing an optimal lottery. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.00822&r= |
| By: | Mathias Lindholm; Ronald Richman; Andreas Tsanakas; Mario V. W\"uthrich |
| Abstract: | Indirect discrimination is an issue of major concern in algorithmic models. This is particularly the case in insurance pricing where protected policyholder characteristics are not allowed to be used for insurance pricing. Simply disregarding protected policyholder information is not an appropriate solution because this still allows for the possibility of inferring the protected characteristics from the non-protected ones. This leads to so-called proxy or indirect discrimination. Though proxy discrimination is qualitatively different from the group fairness concepts in machine learning, these group fairness concepts are proposed to 'smooth out' the impact of protected characteristics in the calculation of insurance prices. The purpose of this note is to share some thoughts about group fairness concepts in the light of insurance pricing and to discuss their implications. We present a statistical model that is free of proxy discrimination, thus, unproblematic from an insurance pricing point of view. However, we find that the canonical price in this statistical model does not satisfy any of the three most popular group fairness axioms. This seems puzzling and we welcome feedback on our example and on the usefulness of these group fairness axioms for non-discriminatory insurance pricing. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.00858&r= |
| By: | Henri Arno; Klaas Mulier; Joke Baeck; Thomas Demeester |
| Abstract: | Models for bankruptcy prediction are useful in several real-world scenarios, and multiple research contributions have been devoted to the task, based on structured (numerical) as well as unstructured (textual) data. However, the lack of a common benchmark dataset and evaluation strategy impedes the objective comparison between models. This paper introduces such a benchmark for the unstructured data scenario, based on novel and established datasets, in order to stimulate further research into the task. We describe and evaluate several classical and neural baseline models, and discuss benefits and flaws of different strategies. In particular, we find that a lightweight bag-of-words model based on static in-domain word representations obtains surprisingly good results, especially when taking textual data from several years into account. These results are critically assessed, and discussed in light of particular aspects of the data and the task. All code to replicate the data and experimental results will be released. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.11334&r= |