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on Risk Management |
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Issue of 2023‒10‒09
twelve papers chosen by |
| By: | Hui Mi; Zuo Quan Xu; Dongfang Yang |
| Abstract: | This paper investigates an optimal investment problem under the tail Value at Risk (tail VaR, also known as expected shortfall, conditional VaR, average VaR) and portfolio insurance constraints confronted by a defined-contribution pension member. The member's aim is to maximize the expected utility from the terminal wealth exceeding the minimum guarantee by investing his wealth in a cash bond, an inflation-linked bond and a stock. Due to the presence of the tail VaR constraint, the problem cannot be tackled by standard control tools. We apply the Lagrange method along with quantile optimization techniques to solve the problem. Through delicate analysis, the optimal investment output in closed-form and optimal investment strategy are derived. A numerical analysis is also provided to show how the constraints impact the optimal investment output and strategy. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.01936&r=rmg |
| By: | Zsámboki, Balázs; Leitner, Georg; Dvořák, Michal; Magi, Alessandro |
| Abstract: | This paper analyses banks’ ability to use capital buffers in the euro area, taking into account overlapping capital requirements between the risk-based capital framework and the leverage ratio capital framework from 2016 to 2022. This analysis is the first to quantify buffer usability in multiple jurisdictions and across various bank types, identify key drivers of buffer usability and assess the impact of various policy measures using longer time series. The paper shows that while both risk-based and leverage frameworks play a key role in enhancing the resilience of the banking system and ensuring financial stability, their simultaneous application creates interactions that may affect the functioning of capital buffers. In this regard, we investigate to what extent banks could have drawn down regulatory capital buffers in the risk-based framework without breaching current leverage ratio requirements, which is in line with the approach to buffer usability taken in ESRB (2021b). We show that buffer usability was partially constrained in the period examined and is expected to remain so under the current regulatory framework and if risk weight densities (RWDs) remain low. This finding indicates that the leverage ratio constitutes an effective backstop to the risk-based framework, both as regards minimum requirements and capital buffers. Limited buffer usability was identified especially for global systemically important institutions (G-SIIs) that rely largely on internal modelling approaches to calculate risk-based capital requirements, leading to comparably low risk weights and making the leverage ratio relatively more binding. Adding to previous contributions, we find that banks’ ability to use capital buffers fluctuated over time, generally increasing before 2019 and decreasing after the start of the coronavirus (COVID-19) pandemic, with substantial heterogeneity across countries. Furthermore, we provide new insights into the relationship between the RWD of a bank and its buffer usability and find that there is a critical RWD range between 25% JEL Classification: G21, G28 |
| Keywords: | banking regulation, buffer usability, capital buffers, leverage ratio, macroprudential policy |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbops:2023329&r=rmg |
| By: | Chen Liu; Minh-Ngoc Tran; Chao Wang; Richard Gerlach; Robert Kohn |
| Abstract: | This paper introduces DeepVol, a promising new deep learning volatility model that outperforms traditional econometric models in terms of model generality. DeepVol leverages the power of transfer learning to effectively capture and model the volatility dynamics of all financial assets, including previously unseen ones, using a single universal model. This contrasts to the prevailing practice in econometrics literature, which necessitates training separate models for individual datasets. The introduction of DeepVol opens up new avenues for volatility modeling and forecasting in the finance industry, potentially transforming the way volatility is understood and predicted. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.02072&r=rmg |
| By: | Hitesh Doshi; Hyung Joo Kim; Sang Byung Seo |
| Abstract: | The identification of disaster risk has remained a significant challenge due to the rarity of macroeconomic disasters. We show that the interbank market can help characterize the time variation in disaster risk. We propose a risk-based model in which macroeconomic disasters are likely to coincide with interbank market failure. Using interbank rates and their options, we estimate our model via MLE and filter out the short-run and long-run components of disaster risk. Our estimation results are independent of the stock market and serve as an external validity test of rare disaster models, which are typically calibrated to match stock moments. |
| Keywords: | Economic disasters; Extended Kalman filter; Interbank rate options; Interbank rates; Maximum likelihood estimation; Time-varying disaster risk |
| JEL: | G12 C58 C13 G13 |
| Date: | 2023–08–14 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2023-54&r=rmg |
| By: | Lydia Cheung; Jaqueson K. Galimberti; Philip Vermeulen (University of Canterbury) |
| Abstract: | Using around one million repeat sales, we show that idiosyncratic risk in real house price appreciation varies considerably across houses. We find that idiosyncratic risk is timevarying, depends negatively on the initial house price, varies across locations, and reduces as the holding period of the house increases. We find that risk is priced in expected returns across all these dimensions, except location. The variation in idiosyncratic risk by location can be explained by variations in market thinness and information quality across markets. Risk is related to macroeconomic credit conditions and financial regulation. The higher risk for cheaper houses is associated with valuation uncertainty and financial vulnerability of homeowners. The risk-return relationship across initial house prices depends on the current state of the market. During busts of the housing cycle, the distribution of house prices widens and cheaper houses depreciate faster than more expensive houses, leading to an inverted riskreturn relationship. |
| Keywords: | Idiosyncratic risk, house prices, housing markets |
| JEL: | G1 R1 |
| Date: | 2023–09–01 |
| URL: | http://d.repec.org/n?u=RePEc:cbt:econwp:23/13&r=rmg |
| By: | Jozef Barunik; Mattia Bevilacqua; Michael Ellington |
| Abstract: | We identify a new type of risk, common firm-level investor fears, from commonalities within the cross-sectional distribution of individual stock options. We define firm-level fears that link with upward price movements as good fears, and those relating to downward price movements as bad fears. Such information is different to market fears that we extract from index options. Stocks with high sensitivities to common firm-level investor fears earn lower returns, with investors demanding a higher compensation for exposure to common bad fears relative to common good fears. Risk premium estimates for common bad fears range from -5.63% to -4.92% per annum. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.03968&r=rmg |
| By: | Abir Sridi; Paul Bilokon |
| Abstract: | Stochastic volatility models, where the volatility is a stochastic process, can capture most of the essential stylized facts of implied volatility surfaces and give more realistic dynamics of the volatility smile or skew. However, they come with the significant issue that they take too long to calibrate. Alternative calibration methods based on Deep Learning (DL) techniques have been recently used to build fast and accurate solutions to the calibration problem. Huge and Savine developed a Differential Deep Learning (DDL) approach, where Machine Learning models are trained on samples of not only features and labels but also differentials of labels to features. The present work aims to apply the DDL technique to price vanilla European options (i.e. the calibration instruments), more specifically, puts when the underlying asset follows a Heston model and then calibrate the model on the trained network. DDL allows for fast training and accurate pricing. The trained neural network dramatically reduces Heston calibration's computation time. In this work, we also introduce different regularisation techniques, and we apply them notably in the case of the DDL. We compare their performance in reducing overfitting and improving the generalisation error. The DDL performance is also compared to the classical DL (without differentiation) one in the case of Feed-Forward Neural Networks. We show that the DDL outperforms the DL. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.07843&r=rmg |
| By: | Saqib Aziz (ESC [Rennes] - ESC Rennes School of Business); Mahabubur Rahman (ESC [Rennes] - ESC Rennes School of Business); Dildar Hussain (ESC [Rennes] - ESC Rennes School of Business); Duc Nguyen (IPAG Business School, VNU - Vietnam National University [Hanoï]) |
| Abstract: | Little is known about the effects of green performance on corporate insolvency risk. This study examines the relationship between green performance and firm insolvency risk from both theoretical and empirical perspectives. Using a panel of 179 US firms included in the Newsweek Green Rankings and a system generalised method of moments estimation which generates endogeneity-robust regression coefficients, we found that firms with higher green performance are at lower risk of insolvency. We further postulate and provide theory-based empirical evidence that the nexus between green performance and insolvency risk is contingent upon other internal and external boundary conditions. Specifically, this research documents that the nexus between green performance and firm insolvency risk is moderated by market power as well as industry competitive intensity. The results of this study are robust across several sensitivity analyses. |
| Keywords: | Green performance, Insolvency risk, Market share, Industry competitiveness, Z-score |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03344206&r=rmg |
| By: | Emiel Lemahieu; Kris Boudt; Maarten Wyns |
| Abstract: | A novel generative machine learning approach for the simulation of sequences of financial price data with drawdowns quantifiably close to empirical data is introduced. Applications such as pricing drawdown insurance options or developing portfolio drawdown control strategies call for a host of drawdown-realistic paths. Historical scenarios may be insufficient to effectively train and backtest the strategy, while standard parametric Monte Carlo does not adequately preserve drawdowns. We advocate a non-parametric Monte Carlo approach combining a variational autoencoder generative model with a drawdown reconstruction loss function. To overcome issues of numerical complexity and non-differentiability, we approximate drawdown as a linear function of the moments of the path, known in the literature as path signatures. We prove the required regularity of drawdown function and consistency of the approximation. Furthermore, we obtain close numerical approximations using linear regression for fractional Brownian and empirical data. We argue that linear combinations of the moments of a path yield a mathematically non-trivial smoothing of the drawdown function, which gives one leeway to simulate drawdown-realistic price paths by including drawdown evaluation metrics in the learning objective. We conclude with numerical experiments on mixed equity, bond, real estate and commodity portfolios and obtain a host of drawdown-realistic paths. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.04507&r=rmg |
| By: | Sabiou Inoua |
| Abstract: | Financial volatility obeys two fascinating empirical regularities that apply to various assets, on various markets, and on various time scales: it is fat-tailed (more precisely power-law distributed) and it tends to be clustered in time. Many interesting models have been proposed to account for these regularities, notably agent-based models, which mimic the two empirical laws through a complex mix of nonlinear mechanisms such as traders' switching between trading strategies in highly nonlinear way. This paper explains the two regularities simply in terms of traders' attitudes towards news, an explanation that follows almost by definition of the traditional dichotomy of financial market participants, investors versus speculators, whose behaviors are reduced to their simplest forms. Long-run investors' valuations of an asset are assumed to follow a news-driven random walk, thus capturing the investors' persistent, long memory of fundamental news. Short-term speculators' anticipated returns, on the other hand, are assumed to follow a news-driven autoregressive process, capturing their shorter memory of fundamental news, and, by the same token, the feedback intrinsic to the short-sighted, trend-following (or herding) mindset of speculators. These simple, linear, models of traders' expectations, it is shown, explain the two financial regularities in a generic and robust way. Rational expectations, the dominant model of traders' expectations, is not assumed here, owing to the famous no-speculation, no-trade results |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.04876&r=rmg |
| By: | Giulia Di Nunno; K\k{e}stutis Kubilius; Yuliya Mishura; Anton Yurchenko-Tytarenko |
| Abstract: | In this paper, we present a comprehensive survey of continuous stochastic volatility models, discussing their historical development and the key stylized facts that have driven the field. Special attention is dedicated to fractional and rough methods: we outline the motivation behind them and characterize some landmark models. In addition, we briefly touch the problem of VIX modeling and recent advances in the SPX-VIX joint calibration puzzle. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.01033&r=rmg |
| By: | Jonas M. Bruhin; Rolf Scheufele; Yannic Stucki |
| Abstract: | We quantify the economic impact of Russia’s invasion of Ukraine on Germany, the United Kingdom, France, Italy and Switzerland using data on historical geopolitical events. Applying a structural VAR approach based on sign and narrative sign restrictions, we find that the war has exerted a notable drag on real activity and has pushed inflation up considerably. For example, a counterfactual exercise suggests that in Germany, GDP would have been 0.7 percent higher and the CPI 0.4 percent lower in 2022Q4 if Russia had neither attacked nor threatened Ukraine. The negative consequences of the war are likely to be far greater in the medium-to-long term, especially with regard to the real economy. |
| Keywords: | Geopolitical risk, structural VAR, narrative sign restriction, war in Ukraine, Russia, Europe |
| JEL: | C1 E32 H56 |
| Date: | 2023–08–02 |
| URL: | http://d.repec.org/n?u=RePEc:snb:snbwpa:2023-04&r=rmg |