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on Risk Management |
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Issue of 2024‒06‒17
fourteen papers chosen by |
| By: | Tong Pu; Yifei Zhang; Yiying Zhang |
| Abstract: | Systemic risk is the risk that a company- or industry-level risk could trigger a huge collapse of another or even the whole institution. Various systemic risk measures have been proposed in the literature to quantify the domino and (relative) spillover effects induced by systemic risks such as the well-known CoVaR, CoES, MES and CoD risk measures, and associated contribution measures. This paper proposes another new type of systemic risk measure, called the joint marginal expected shortfall (JMES), to measure whether the MES of one entity's risk-taking adds to another one or the overall risk conditioned on the event that the entity is already in some specified distress level. We further introduce two useful systemic risk contribution measures based on the difference function or relative ratio function of the JMES and the conventional ES, respectively. Some basic properties of these proposed measures are studied such as monotonicity, comonotonic additivity, non-identifiability and non-elicitability. For both risk measures and two different vectors of bivariate risks, we establish sufficient conditions imposed on copula structure, stress levels, and stochastic orders to compare these new measures. We further provide some numerical examples to illustrate our main findings. A real application in analyzing the risk contagion among several stock market indices is implemented to show the performances of our proposed measures compared with other commonly used measures including CoVaR, CoES, MES, and their associated contribution measures. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.07549&r= |
| By: | Sullivan Hu\'e; Christophe Hurlin; Yang Lu |
| Abstract: | We propose an original two-part, duration-severity approach for backtesting Expected Shortfall (ES). While Probability Integral Transform (PIT) based ES backtests have gained popularity, they have yet to allow for separate testing of the frequency and severity of Value-at-Risk (VaR) violations. This is a crucial aspect, as ES measures the average loss in the event of such violations. To overcome this limitation, we introduce a backtesting framework that relies on the sequence of inter-violation durations and the sequence of severities in case of violations. By leveraging the theory of (bivariate) orthogonal polynomials, we derive orthogonal moment conditions satisfied by these two sequences. Our approach includes a straightforward, model-free Wald test, which encompasses various unconditional and conditional coverage backtests for both VaR and ES. This test aids in identifying any mis-specified components of the internal model used by banks to forecast ES. Moreover, it can be extended to analyze other systemic risk measures such as Marginal Expected Shortfall. Simulation experiments indicate that our test exhibits good finite sample properties for realistic sample sizes. Through application to two stock indices, we demonstrate how our methodology provides insights into the reasons for rejections in testing ES validity. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.02012&r= |
| By: | Emmanuel Lepinette; Duc Thinh Vu |
| Abstract: | The NA condition is one of the pillars supporting the classical theory of financial mathematics. We revisit this condition for financial market models where a dynamic risk-measure defined on $L^0$ is fixed to characterize the family of acceptable wealths that play the role of non negative financial positions. We provide in this setting a new version of the fundamental theorem of asset pricing and we deduce a dual characterization of the super-hedging prices (called risk-hedging prices) of a European option. Moreover, we show that the set of all risk-hedging prices is closed under NA. At last, we provide a dual representation of the risk-measure on $L^0$ under some conditions. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.06764&r= |
| By: | Thanoj K. Muddana (Department of Mathematics, San Francisco State University, California, USA); Komal S.R. Bhimireddy (Department of Mathematics, San Francisco State University, California, USA); Anandamayee Majumdar (Department of Mathematics, San Francisco State University, California, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | We analyze the role of leverage, lower and upper tail risks, skewness and kurtosis of real gold returns in forecasting its volatility of over the annual data sample of 1258 to 2023. To conduct our forecasting experiment, we first fit Bayesian time-varying parameters quantile regressions to real gold returns, under six alternative prior settings, to obtain the estimates of volatility (as inter-quantile range), lower and upper tail risks, skewness and kurtosis. Second, we forecast the derived estimates of conditional volatility using the information contained in leverage of gold returns, tail risks, skewness and kurtosis using recursively estimated linear predictive regressions over the out-of-sample periods. We find strong statistical evidence of the role of the moments-based predictors in forecasting gold returns volatility over the short- to medium term, i.e., till one- to five-year ahead, when compared to the autoregressive benchmark. Our results have important implications for investors and policy makers. |
| Keywords: | Time-varying parameters quantile regressions, Bayesian inference; Real gold returns, Moments, Volatility forecasting, Linear predictive regressions |
| JEL: | C22 C53 Q02 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202421&r= |
| By: | Zhiyu Cao; Zachary Feinstein |
| Abstract: | This study explores the innovative use of Large Language Models (LLMs) as analytical tools for interpreting complex financial regulations. The primary objective is to design effective prompts that guide LLMs in distilling verbose and intricate regulatory texts, such as the Basel III capital requirement regulations, into a concise mathematical framework that can be subsequently translated into actionable code. This novel approach aims to streamline the implementation of regulatory mandates within the financial reporting and risk management systems of global banking institutions. A case study was conducted to assess the performance of various LLMs, demonstrating that GPT-4 outperforms other models in processing and collecting necessary information, as well as executing mathematical calculations. The case study utilized numerical simulations with asset holdings -- including fixed income, equities, currency pairs, and commodities -- to demonstrate how LLMs can effectively implement the Basel III capital adequacy requirements. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.06808&r= |
| By: | Tian Tian; Ricky Cooper; Jiahao Deng; Qingquan Zhang |
| Abstract: | This paper introduces a novel methodology for index return forecasting, blending highly correlated stock prices, advanced deep learning techniques, and intricate factor integration. Departing from conventional cap-weighted approaches, our innovative framework promises to reimagine traditional methodologies, offering heightened diversification, amplified performance capture, and nuanced market depiction. At its core lies the intricate identification of highly correlated company clusters, fueling predictive accuracy and robustness. By harnessing these interconnected constellations, we unlock a profound comprehension of market dynamics, bestowing both investment entities and individual enterprises with invaluable performance insights. Moreover, our methodology integrates pivotal factors such as indexes and ETFs, seamlessly woven with Hierarchical Risk Parity (HRP) portfolio optimization, to elevate performance and fortify risk management. This comprehensive amalgamation refines risk diversification, fortifying portfolio resilience against turbulent market forces. The implications reverberate resoundingly. Investment entities stand poised to calibrate against competitors with surgical precision, tactically sidestepping industry-specific pitfalls, and sculpting bespoke investment strategies to capitalize on market fluctuations. Concurrently, individual enterprises find empowerment in aligning strategic endeavors with market trajectories, discerning key competitors, and navigating volatility with steadfast resilience. In essence, this research marks a pivotal moment in economic discourse, unveiling novel methodologies poised to redefine decision-making paradigms and elevate performance benchmarks for both investment entities and individual enterprises navigating the intricate tapestry of financial realms. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.01892&r= |
| By: | Aur\'elien Alfonsi; Ahmed Kebaier; J\'er\^ome Lelong |
| Abstract: | This paper develops a new dual approach to compute the hedging portfolio of a Bermudan option and its initial value. It gives a "purely dual" algorithm following the spirit of Rogers (2010) in the sense that it only relies on the dual pricing formula. The key is to rewrite the dual formula as an excess reward representation and to combine it with a strict convexification technique. The hedging strategy is then obtained by using a Monte Carlo method, solving backward a sequence of least square problems. We show convergence results for our algorithm and test it on many different Bermudan options. Beyond giving directly the hedging portfolio, the strength of the algorithm is to assess both the relevance of including financial instruments in the hedging portfolio and the effect of the rebalancing frequency. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.18761&r= |
| By: | Xue Wen Tan; Stanley Kok |
| Abstract: | Every publicly traded company in the US is required to file an annual 10-K financial report, which contains a wealth of information about the company. In this paper, we propose an explainable deep-learning model, called FinBERT-XRC, that takes a 10-K report as input, and automatically assesses the post-event return volatility risk of its associated company. In contrast to previous systems, our proposed model simultaneously offers explanations of its classification decision at three different levels: the word, sentence, and corpus levels. By doing so, our model provides a comprehensive interpretation of its prediction to end users. This is particularly important in financial domains, where the transparency and accountability of algorithmic predictions play a vital role in their application to decision-making processes. Aside from its novel interpretability, our model surpasses the state of the art in predictive accuracy in experiments on a large real-world dataset of 10-K reports spanning six years. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.01881&r= |
| By: | Aikman, David; Angotti, Romain; Budnik, Katarzyna |
| Abstract: | This paper proposes an operational approach to stress testing, allowing one to assess the banking sector’s vulnerability in multiple plausible macro-financial scenarios. The approach helps identify macro-financial risk factors of particular relevance for the banking system and individual banks and searches for scenarios that could push them towards their worst outcomes. We demonstrate this concept using a macroprudential stress testing model for the euro area. By doing so, we show how multiple-scenario stress testing can complement single-scenario stress tests, aid in scenario design, and evaluate risks in the banking system. We also show how stress tests and scenarios can be optimized to accommodate different mandates and instruments of supervisory and macroprudential agencies. JEL Classification: E37, E58, G21, G28 |
| Keywords: | banking sector risks, financial stability, macroprudential stress test, multiple scenarios, reverse stress testing, systemic risks |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20242941&r= |
| By: | Ernest Aboagye; Vali Asimit; Tsz Chai Fung; Liang Peng; Qiuqi Wang |
| Abstract: | It is well-known that Excess-of-Loss reinsurance has more marketability than Stop-Loss reinsurance, though Stop-Loss reinsurance is the most prominent setting discussed in the optimal (re)insurance design literature. We point out that optimal reinsurance policy under Stop-Loss leads to a zero insolvency probability, which motivates our paper. We provide a remedy to this peculiar property of the optimal Stop-Loss reinsurance contract by investigating the optimal Excess-of-Loss reinsurance contract instead. We also provide estimators for the optimal Excess-of-Loss and Stop-Loss contracts and investigate their statistical properties under many premium principle assumptions and various risk preferences, which according to our knowledge, have never been investigated in the literature. Simulated data and real-life data are used to illustrate our main theoretical findings. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.00188&r= |
| By: | Thorsten Hens (University of Zurich - Department of Banking and Finance; Norwegian School of Economics and Business Administration (NHH); Swiss Finance Institute); Trine Nordlie (Norwegian School of Economics (NHH)) |
| Abstract: | This study compares OpenAI’s ChatGPT-4 and Google’s Bard with bank experts in determining investors’ risk profiles. We find that for half of the client cases used, there are no statistically significant differences in the risk profiles. Moreover, the economic relevance of the differences is small. However, the LLMs are not good in explaining the risk profiles. |
| Keywords: | Large Language Models, ChatGPT, Bard, Risk Profiling |
| JEL: | D8 D14 D81 G51 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2430&r= |
| By: | Taha Choukhmane; Tim de Silva |
| Abstract: | We study the role of risk preferences and frictions in portfolio choice using variation in 401(k) default options. Patterns of active choice in response to different default funds imply that, absent participation frictions, 94% of investors prefer holding stocks, with an equity share of retirement wealth declining with age—patterns markedly different from observed allocations. We use this quasi-experiment to estimate a life cycle model and find a relative risk aversion of 2, EIS of 0.4, and $200 portfolio adjustment cost. Our results suggest that low levels of stock market participation in retirement accounts are due to participation frictions rather than non-standard preferences such as loss aversion. |
| JEL: | D14 D15 G0 G11 G40 G5 G51 J32 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32476&r= |
| By: | Reilly Pickard; Finn Wredenhagen; Julio DeJesus; Mario Schlener; Yuri Lawryshyn |
| Abstract: | This article leverages deep reinforcement learning (DRL) to hedge American put options, utilizing the deep deterministic policy gradient (DDPG) method. The agents are first trained and tested with Geometric Brownian Motion (GBM) asset paths and demonstrate superior performance over traditional strategies like the Black-Scholes (BS) Delta, particularly in the presence of transaction costs. To assess the real-world applicability of DRL hedging, a second round of experiments uses a market calibrated stochastic volatility model to train DRL agents. Specifically, 80 put options across 8 symbols are collected, stochastic volatility model coefficients are calibrated for each symbol, and a DRL agent is trained for each of the 80 options by simulating paths of the respective calibrated model. Not only do DRL agents outperform the BS Delta method when testing is conducted using the same calibrated stochastic volatility model data from training, but DRL agents achieves better results when hedging the true asset path that occurred between the option sale date and the maturity. As such, not only does this study present the first DRL agents tailored for American put option hedging, but results on both simulated and empirical market testing data also suggest the optimality of DRL agents over the BS Delta method in real-world scenarios. Finally, note that this study employs a model-agnostic Chebyshev interpolation method to provide DRL agents with option prices at each time step when a stochastic volatility model is used, thereby providing a general framework for an easy extension to more complex underlying asset processes. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.06774&r= |
| By: | H. Peter Boswijk; Jun Yu; Yang Zu |
| Abstract: | Based on a continuous-time stochastic volatility model with a linear drift, we develop a test for explosive behavior in financial asset prices at a low frequency when prices are sampled at a higher frequency. The test exploits the volatility information in the high-frequency data. The method consists of devolatizing log-asset price increments with realized volatility measures and performing a supremum-type recursive Dickey-Fuller test on the devolatized sample. The proposed test has a nuisance-parameter-free asymptotic distribution and is easy to implement. We study the size and power properties of the test in Monte Carlo simulations. A real-time date-stamping strategy based on the devolatized sample is proposed for the origination and conclusion dates of the explosive regime. Conditions under which the real-time date-stamping strategy is consistent are established. The test and the date-stamping strategy are applied to study explosive behavior in cryptocurrency and stock markets. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.02087&r= |