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on Risk Management |
| By: | Makoto Takahashi; Yuta Yamauchi; Toshiaki Watanabe; Yasuhiro Omori |
| Abstract: | Forecasting volatility and quantiles of financial returns is essential for accurately measuring financial tail risks, such as value-at-risk and expected shortfall. The critical elements in these forecasts involve understanding the distribution of financial returns and accurately estimating volatility. This paper introduces an advancement to the traditional stochastic volatility model, termed the realized stochastic volatility model, which integrates realized volatility as a precise estimator of volatility. To capture the well-known characteristics of return distribution, namely skewness and heavy tails, we incorporate three types of skew-t distributions. Among these, two distributions include the skew-normal feature, offering enhanced flexibility in modeling the return distribution. We employ a Bayesian estimation approach using the Markov chain Monte Carlo method and apply it to major stock indices. Our empirical analysis, utilizing data from US and Japanese stock indices, indicates that the inclusion of both skewness and heavy tails in daily returns significantly improves the accuracy of volatility and quantile forecasts. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.13179&r=rmg |
| By: | Virginie Terraza (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier); Aslı Boru İpek; Mohammad Mahdi Rounaghi |
| Abstract: | The spread of the coronavirus has reduced the value of stock indexes, depressed energy and metals commodities prices including oil, and caused instability in financial markets around the world. Due to this situation, investors should consider investing in more secure assets, such as real estate property, cash, gold, and crypto assets. In recent years, among secure assets, cryptoassets are gaining more attention than traditional investments. This study compares the Bitcoin market, the gold market, and American stock indexes (S&P500, Nasdaq, and Dow Jones) before and during the COVID-19 pandemic. For this purpose, the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets. Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin, gold, and stock markets. In particular, we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period. This paper provides practical impacts on risk management and portfolio diversification. |
| Keywords: | JEL Classification: C22 C58 G17 Bitcoin market Gold market American stock markets COVID-19 pandemic VAR-DCC-EGARCH model ANN model, JEL Classification: C22, C58, G17 Bitcoin market, Gold market, American stock markets, COVID-19 pandemic, VAR-DCC-EGARCH model, ANN model |
| Date: | 2024–01–15 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04395168&r=rmg |
| By: | Fatouh, Mahmoud (Bank of England); Giansante, Simone (Department of Economics, Business and Statistics, University of Palermo); Ongena, Steven (University of Zurich, Swiss Finance Institute, KU Leuven, NTNU Business School and CEPR) |
| Abstract: | We assess the impact of the leverage ratio capital requirements on the risk‑taking behaviour of banks both theoretically and empirically. We use a difference‑in‑differences (DiD) setup to compare the behaviour of UK banks subject to the leverage ratio requirements (LR banks) to otherwise similar banks (non‑LR banks). Conceptually, introducing binding leverage ratio requirements into a regulatory framework with risk-based capital requirements induces banks to reoptimise, shifting from safer to riskier assets (higher asset risk). Yet, this shift would not be one‑for‑one due to risk‑weight differences, meaning the shift would be associated with a lower level of leverage (lower insolvency risk). The interaction of these two changes determines the impact on the aggregate level of risk. Empirically, we show that LR banks did not increase asset risk, and slightly reduced leverage levels, compared to the control group after the introduction of leverage ratio in the UK. As expected, these two changes lead to a lower aggregate level of risk. Our results show that credit default swap spreads on the five‑year subordinated debt of LR banks dropped relative to non‑LR banks post leverage ratio introduction. |
| Keywords: | Finance; capital regulation; risk-taking; leverage ratio; risk‑based requirements |
| JEL: | G01 G21 G28 |
| Date: | 2023–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:1048&r=rmg |
| By: | Marcel Bluhm (The Block); Adrian Cachinero Vasiljevi\'c (Steakhouse Financial Limited); S\'ebastien Derivaux (Steakhouse Financial Limited); S{\o}ren Terp H{\o}rl\"uck Jessen (Balloonist ApS) |
| Abstract: | Stablecoins have turned out to be the "killer" use case of the growing digital asset space. However, risk management frameworks, including regulatory ones, have been largely absent. In this paper, we address the critical question of measuring and managing risk in stablecoin protocols, which operate on public blockchain infrastructure. The on-chain environment makes it possible to monitor risk and automate its management via transparent smart-contracts in real-time. We propose two risk metrics covering capitalization and liquidity of stablecoin protocols. We then explore in a case-study type analysis how our risk management framework can be applied to DAI, the biggest decentralized stablecoin by market capitalisation to-date, governed by MakerDAO. Based on our findings, we recommend that the protocol explores implementing automatic capital buffer adjustments and dynamic maturity gap matching. Our analysis demonstrates the practical benefits for scalable (prudential) risk management stemming from real-time availability of high-quality, granular, tamper-resistant on-chain data in the digital asset space. We name this approach Crypto Asset-Liability Management (CALM). |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.13399&r=rmg |
| By: | Ilke Aydogan (IÉSEG School Of Management [Puteaux]); Loïc Berger (CNRS - Centre National de la Recherche Scientifique, IÉSEG School Of Management [Puteaux], EIEE - European Institute on Economics and the Environment, CMCC - Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna]); Valentina Bosetti (Bocconi University [Milan, Italy], EIEE - European Institute on Economics and the Environment, CMCC - Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna]); Ning Liu (BUAA - Beihang University) |
| Abstract: | We explore decision-making under uncertainty using a framework that decomposes uncertainty into three distinct layers: (1) risk, which entails inherent randomness within a given probability model; (2) model ambiguity, which entails uncertainty about the probability model to be used; and (3) model misspecification, which entails uncertainty about the presence of the correct probability model among the set of models considered. Using a new experimental design, we isolate and measure attitudes toward each layer separately. We conduct our experiment on three different subject pools and document, the existence of a behavioral distinction between the three layers. In addition to |
| Date: | 2023–03–27 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04370968&r=rmg |
| By: | Karol Szafranek; Michał Rubaszek; Gazi Salah Uddin |
| Abstract: | We quantify intraday volatility connectedness between oil and key financial assets and assess how it is related to uncertainty and sentiment measures. For that purpose, we integrate the well-known spillover methodology with a TVP VAR model estimated on a unique, vast dataset of roughly 300 thousand 5 minute quotations for crude oil, the US dollar, S&P 500 index, gold and US treasury prices. This distinguishes our investigation from previous studies, which usually employ relatively short samples of daily or weekly data and focus on connectedness between two asset classes. We contribute to the literature across three margins. First, we document that market connectedness at intraday frequency presents new picture on markets co-movement compared to the estimates obtained using daily data. Second, we show that at 5 minute frequency volatility is mostly transmitted from the stock market and absorbed by the bond and dollar markets, with oil and gold markets being occasionally important for volatility transmission. Third, we present evidence that daily averages of intraday connectedness measures respond to changes in sentiment and market-specific uncertainty. Interestingly, our results contrast with earlier findings, as they show that connectedness among markets decreases in periods of high volatility owing to market-specific factors. Our study points to the importance of using high-frequency data in order to better understand market dynamics. |
| Keywords: | volatility connectedness, uncertainty and sentiment, oil market, intraday data, TVP-VAR model |
| JEL: | C32 C58 D80 Q31 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:sgh:kaewps:2023095&r=rmg |
| By: | Ilke Aydogan (IÉSEG School Of Management [Puteaux]); Loïc Berger (CNRS - Centre National de la Recherche Scientifique, IÉSEG School Of Management [Puteaux], EIEE - European Institute on Economics and the Environment, CMCC - Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna]); Valentina Bosetti (Bocconi University [Milan, Italy], EIEE - European Institute on Economics and the Environment, CMCC - Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna]) |
| Abstract: | We report the results of two experiments designed to better understand the mechanisms driving decision-making under ambiguity. We elicit individual preferences over different sources of uncertainty (risk, compound risk, model ambiguity, and Ellsberg ambiguity), which entail different degrees of complexity, from subjects with different sophistication levels. We show that (1) ambiguity aversion is robust to sophistication, but the strong relationship that has been previously reported between attitudes toward ambiguity and compound risk is not. (2) Ellsberg ambiguity attitude can be partly explained by attitudes toward complexity for less sophisticated subjects, but not for more sophisticated ones. Overall, and regardless of the subject's sophistication level, the main driver of Ellsberg ambiguity attitude is a specific treatment of unknown probabilities. These results leave room for using ambiguity models in applications with prescriptive purposes. |
| Keywords: | Ambiguity aversion, complexity, reduction of compound risk, model uncertainty |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04071242&r=rmg |
| By: | Liexin Cheng; Xue Cheng |
| Abstract: | We develop a novel time-change approach to study the shape of implied volatility smiles. The method is applicable to common semimartingale models, including jump-diffusion, rough volatility and infinite activity models. We approximate the at-the-money skew and curvature with an improved moment-based formula. The moments are further explicitly computed under a time change framework. The limiting skew and curvature for several models are considered. We also test the accuracy of the short-term approximation results on models via numerical methods and on empirical data. Finally, we apply the method to the calibration problem. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.03776&r=rmg |
| By: | Zongwu Cai (Department of Economics, University of Kansa, Lawrence, KS 66045, USA); Ying Fang (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China); Dingshi Tian (Department of Statistics, School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, Hubei 430073, China) |
| Abstract: | The estimation and model selection of conditional autoregressive value at risk (CAViaR) model may be computationally intensive and even impractical when the true order of the quantile autoregressive components or the dimension of the other regressors are high. On the other hand, automatic variable selection methods cannot be directly applied to this problem because the quantile lag components are latent. In this paper, we propose to identify the optimal CAViaR model using a two-step approach. The estimation procedure consists of an approximation of the conditional quantile in the first step, followed by an adaptive Lasso penalized quantile regression of the regressors as well as the estimated quantile lag components in the second step. We show that under some mild regularity conditions, the proposed adaptive Lasso penalized quantile estimators enjoy the oracle properties. Finally, the proposed method is illustrated by Monte Carlo simulation study and applied to analyzing the daily data of the S&P500 return series. |
| Keywords: | CAViaR model; Adaptive Lasso; Model selection; Tail risk. |
| JEL: | C32 C51 C58 |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:kan:wpaper:202403&r=rmg |
| By: | Roberto Rivera; Guido Rocco; Massimiliano Marzo; Enrico Talin |
| Abstract: | This whitepaper introduces RIVCoin, a cryptocurrency built on Cosmos, fully stabilized by a diversified portfolio of both CeFi and DeFi assets, available in a digital, non-custodial wallet called RIV Wallet, that aims to provide Users an easy way to access the cryptocurrency markets, compliant to the strictest AML laws and regulations up to date. The token is a cryptocurrency at any time stabilized by a basket of assets: reserves are invested in a portfolio composed long term by 50% of CeFi assets, comprised of Fixed Income, Equity, Mutual and Hedge Funds and 50% of diversified strategies focused on digital assets, mainly staking and LP farming on the major, battle tested DeFi protocols. The cryptocurrency, as well as the dollar before Bretton Woods, is always fully stabilized by vaulted proof of assets: it is born and managed as a decentralized token, minted by a Decentralized Autonomous Organization, and entirely stabilized by assets evaluated by professional independent third parties. Users will trade, pool, and exchange the token without any intermediary, being able to merge them into a Liquidity Pool whose rewards will be composed by both the trading fees and the liquidity rewards derived from the reserve's seigniorage. Users who wish and decide to pool RIVCoin in the Liquidity Pool will receive additional RIVCoin for themselves, and new RIVCoin are minted when the reserves increase in value or in case of purchase of new RIVCoin. The proposed model allows for alignment of incentives: decreasing the risk exposure by wealthier Users, but implicitly increasing that of smaller ones to a level perceived by them as still sustainable. Users indirectly benefit from the access to the rewards of sophisticated cryptocurrency portfolios hitherto precluded to them, without this turning into a disadvantage for the wealthy User. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.05393&r=rmg |
| By: | Bellaumay, Rémy |
| Abstract: | How much do the world's inhabitants worry about the major risks they face, and how does this affect their subjective well-being? We address these questions through two global surveys: the Gallup World Poll and the World Risk Poll. We show that the experience of risk, worry and subjective well-being are inextricably linked. Climate risk is the most worrisome, followed by road risk, natural disasters, and violent crime. Unlike other risks, concern about climate change does not depend on a country's income level: people in wealthy countries say they are almost as concerned about this risk as people in poor countries, which are more affected. In addition, for the same level of risk exposure, people living in low-income countries are more resilient, that is, the experience of risk affects their subjective well-being less. Finally, the experience of one risk has a contagion effect on anxiety relating to all other risks. |
| Keywords: | Wellbeing, Migration |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:cpm:notobe:2313b&r=rmg |