nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒05‒15
23 papers chosen by
Stan Miles
Thompson Rivers University

  1. A macroprudential look into the risk-return framework of banks’ profitability By Joana Passinhas; Ana Pereira
  2. Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models By Fantazzini, Dean
  3. A Multilevel Stochastic Approximation Algorithm for Value-at-Risk and Expected Shortfall Estimation By St\'ephane Cr\'epey; Noufel Frikha; Azar Louzi
  4. Efficient Estimation in Extreme Value Regression Models of Hedge Fund Tail Risks By Julien Hambuckers; Marie Kratz; Antoine Usseglio-Carleve
  5. Managing Portfolio for Maximizing Alpha and Minimizing Beta By Soumyadip Sarkar
  6. Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training By Jiezhu Cheng; Kaizhu Huang; Zibin Zheng
  7. Technological Shocks and Stock Market Volatility Over a Century: A GARCH-MIDAS Approach By Afees A. Salisu; Riza Demirer; Rangan Gupta
  8. Measuring Discrete Risks on Infinite Domains: Theoretical Foundations, Conditional Five Number Summaries, and Data Analyses By Daoping Yu; Vytaras Brazauskas; Ricardas Zitikis
  9. A network-based strategy of price correlations for optimal cryptocurrency portfolios By Ruixue Jing; Luis Enrique Correa Rocha
  10. Crash risk in the Nordic Stock Market - a cross-sectional analysis By Fjærvik, Thomas
  11. Non-diversified portfolios with subjective expected utility By Christopher P. Chambers; Georgios Gerasimou
  12. Systemic risk measured by systems resiliency to initial shocks By Luka Klin\v{c}i\'c; Vinko Zlati\'c; Guido Caldarelli; Hrvoje \v{S}tefan\v{c}i\'c
  13. Optimizing Risk Strategies in Multiple Dimensions By Tom, Daniel M. Ph.D.
  14. Time-stability of risk preferences: A new approach with evidence from developed and developing countries By Nicolás Salamanca; Buly A. Cardak; Edwin Ip; Joe Vecci
  15. Credit Risk and Financial Performance of Commercial Banks: Evidence from Vietnam By Ha Nguyen
  16. Derivative Margin Calls: A New Driver of MMF Flows By Mr. German Villegas Bauer; Maddalena Ghio; Linda Rousova; Dilyara Salakhova
  17. Exploring the Determinants of Capital Adequacy in Commercial Banks: A Study of Bangladesh's Banking Sector By Md Shah Naoaj
  18. Short-Term Volatility Prediction Using Deep CNNs Trained on Order Flow By Mingyu Hao; Artem Lenskiy
  19. Rough volatility, path-dependent PDEs and weak rates of convergence By Ofelia Bonesini; Antoine Jacquier; Alexandre Pannier
  20. Risk Aversion and Changes in Regime By Tomas E. Caravello; John Driffill; Turalay Kenc; Martin Sola
  21. Ownership concentration and firm risk: the moderating role of mid-sized blockholders By Silvia Rossetto; Nassima Selmane; Raffaele Stagliano
  22. Mitigating Decentralized Finance Liquidations with Reversible Call Options By Kaihua Qin; Jens Ernstberger; Liyi Zhou; Philipp Jovanovic; Arthur Gervais
  23. Financial intermediation and new technology: theoretical and regulatory implications of digital financial markets By Maurizio Trapanese; Michele Lanotte

  1. By: Joana Passinhas; Ana Pereira
    Abstract: Ensuring the resilience of the financial system implies managing a trade-off between expected bank profitability and tail risk in bank returns. To describe this trade-off, we estimate a dynamic quantile regression model using bank-level data for Portugal that links future bank profitability to the current cyclical systemic risk environment net of the prevailing level of capital-based resilience (residual cyclical systemic risk). We find that an increase in residual cyclical systemic risk negatively affects the conditional distribution of bank profitability at the medium-term projection horizons, confirming the findings in the literature. We propose a novel calibration rule for the countercyclical capital buffer (CCyB), which is flexible enough to accommodate different preferences of the policymaker and factors in the prevailing levels of cyclical systemic risk and capital-based resilience. We illustrate the operationalisation of this rule under different assumptions for the policymaker preferences and show how tightening capital requirements alters the risk-return relationship of future profitability in the banking sector. We find evidence that increasing the CCyB rate improves the outlook for medium-term downside risk in bank profitability and worsens the outlook for short-term expected profitability, stressing the tradeoff faced by the policymaker when deploying policy instruments and the misalignment in the horizons at which costs and benefits take place.
    Keywords: Macroprudential policy, systemic risk, bank profitability, quantile regression
    JEL: C21 C54 G17 G21 G28
    Date: 2023–03
  2. By: Fantazzini, Dean
    Abstract: In this paper, we analyzed a dataset of over 2000 crypto-assets to assess their credit risk by computing their probability of death using the daily range. Unlike conventional low-frequency volatility models that only utilize close-to-close prices, the daily range incorporates all the information provided in traditional daily datasets, including the open-high-low-close (OHLC) prices for each asset. We evaluated the accuracy of the probability of death estimated with the daily range against various forecasting models, including credit scoring models, machine learning models, and time-series-based models. Our study considered different definitions of ``dead coins'' and various forecasting horizons. Our results indicate that credit scoring models and machine learning methods incorporating lagged trading volumes and online searches were the best models for short-term horizons up to 30 days. Conversely, time-series models using the daily range were more appropriate for longer term forecasts, up to one year. Additionally, our analysis revealed that the models using the daily range signaled, far in advance, the weakened credit position of the crypto derivatives trading platform FTX, which filed for Chapter 11 bankruptcy protection in the United States on 11 November 2022.
    Keywords: daily range; bitcoin; crypto-assets; cryptocurrencies; credit risk; default probability; probability of death; ZPP; cauchit; random forests
    JEL: C32 C35 C51 C53 C58 G12 G17 G32 G33
    Date: 2023
  3. By: St\'ephane Cr\'epey (LPSM); Noufel Frikha (CES); Azar Louzi (LPSM)
    Abstract: We propose a multilevel stochastic approximation (MLSA) scheme for the computation of the Value-at-Risk (VaR) and the Expected Shortfall (ES) of a financial loss, which can only be computed via simulations conditional on the realization of future risk factors. Thus, the problem of estimating its VaR and ES is nested in nature and can be viewed as an instance of a stochastic approximation problem with biased innovation. In this framework, for a prescribed accuracy $\epsilon$, the optimal complexity of a standard stochastic approximation algorithm is shown to be of order $\epsilon$ --3. To estimate the VaR, our MLSA algorithm attains an optimal complexity of order $\epsilon$ --2--$\delta$ , where $\delta$
    Date: 2023–03
  4. By: Julien Hambuckers; Marie Kratz; Antoine Usseglio-Carleve
    Abstract: We introduce a method to estimate simultaneously the tail and the threshold parameters of an extreme value regression model. This standard model finds its use in finance to assess the effect of market variables on extreme loss distributions of investment vehicles such as hedge funds. However, a major limitation is the need to select ex ante a threshold below which data are discarded, leading to estimation inefficiencies. To solve these issues, we extend the tail regression model to non-tail observations with an auxiliary splicing density, enabling the threshold to be selected automatically. We then apply an artificial censoring mechanism of the likelihood contributions in the bulk of the data to decrease specification issues at the estimation stage. We illustrate the superiority of our approach for inference over classical peaks-over-threshold methods in a simulation study. Empirically, we investigate the determinants of hedge fund tail risks over time, using pooled returns of 1, 484 hedge funds. We find a significant link between tail risks and factors such as equity momentum, financial stability index, and credit spreads. Moreover, sorting funds along exposure to our tail risk measure discriminates between high and low alpha funds, supporting the existence of a fear premium.
    Date: 2023–04
  5. By: Soumyadip Sarkar
    Abstract: Portfolio management is an essential component of investment strategy that aims to maximize returns while minimizing risk. This paper explores several portfolio management strategies, including asset allocation, diversification, active management, and risk management, and their importance in optimizing portfolio performance. These strategies are examined individually and in combination to demonstrate how they can help investors maximize alpha and minimize beta. Asset allocation is the process of dividing a portfolio among different asset classes to achieve the desired level of risk and return. Diversification involves spreading investments across different securities and sectors to minimize the impact of individual security or sector-specific risks. Active management involves security selection and risk management techniques to generate excess returns while minimizing losses. Risk management strategies, such as stop-loss orders and options strategies, aim to minimize losses in adverse market conditions. The importance of combining these strategies for optimizing portfolio performance is emphasized in this paper. The proper implementation of these strategies can help investors achieve their investment goals over the long-term, while minimizing exposure to risks. A call to action for investors to utilize portfolio management strategies to maximize alpha and minimize beta is also provided.
    Date: 2023–04
  6. By: Jiezhu Cheng; Kaizhu Huang; Zibin Zheng
    Abstract: In the stock market, a successful investment requires a good balance between profits and risks. Recently, stock recommendation has been widely studied in quantitative investment to select stocks with higher return ratios for investors. Despite the success in making profits, most existing recommendation approaches are still weak in risk control, which may lead to intolerable paper losses in practical stock investing. To effectively reduce risks, we draw inspiration from adversarial perturbations and propose a novel Split Variational Adversarial Training (SVAT) framework for risk-aware stock recommendation. Essentially, SVAT encourages the model to be sensitive to adversarial perturbations of risky stock examples and enhances the model's risk awareness by learning from perturbations. To generate representative adversarial examples as risk indicators, we devise a variational perturbation generator to model diverse risk factors. Particularly, the variational architecture enables our method to provide a rough risk quantification for investors, showing an additional advantage of interpretability. Experiments on three real-world stock market datasets show that SVAT effectively reduces the volatility of the stock recommendation model and outperforms state-of-the-art baseline methods by more than 30% in terms of risk-adjusted profits.
    Date: 2023–04
  7. By: Afees A. Salisu (Centre for Econometrics & Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: This paper provides a novel perspective to the innovation-stock market nexus by examining the predictive relationship between technological shocks and stock market volatility using data over a period of more than 140 years. Utilizing annual patent data for the U.S. and a large set of economies to create proxies for local and global technological shocks and a mixed-sampling data (MIDAS) framework, we present robust evidence that technological shocks capture significant predictive information regarding future realizations of stock market volatility, both in- and out-of-sample and at both the short and long forecast horizons. Further economic analysis shows that investment portfolios created by the volatility forecasts obtained from the forecasting models that incorporate technological shocks as predictors in volatility models experience significantly lower return volatility in the out-of-sample horizons, which in turn helps to improve the risk-return profile of those portfolios. Our findings present a novel take on the nexus between technological innovations and stock market dynamics and paves the way for several interesting avenues for future research regarding the role of technological innovations on asset pricing tests and portfolio models.
    Keywords: Patents, Technology shocks, Stock market volatility, Forecasting
    JEL: C32 C53 E37 G15 O33
    Date: 2023–04
  8. By: Daoping Yu; Vytaras Brazauskas; Ricardas Zitikis
    Abstract: To accommodate numerous practical scenarios, in this paper we extend statistical inference for smoothed quantile estimators from finite domains to infinite domains. We accomplish the task with the help of a newly designed truncation methodology for discrete loss distributions with infinite domains. A simulation study illustrates the methodology in the case of several distributions, such as Poisson, negative binomial, and their zero inflated versions, which are commonly used in insurance industry to model claim frequencies. Additionally, we propose a very flexible bootstrap-based approach for the use in practice. Using automobile accident data and their modifications, we compute what we have termed the conditional five number summary (C5NS) for the tail risk and construct confidence intervals for each of the five quantiles making up C5NS, and then calculate the tail probabilities. The results show that the smoothed quantile approach classifies the tail riskiness of portfolios not only more accurately but also produces lower coefficients of variation in the estimation of tail probabilities than those obtained using the linear interpolation approach.
    Date: 2023–04
  9. By: Ruixue Jing; Luis Enrique Correa Rocha
    Abstract: A cryptocurrency is a digital asset maintained by a decentralised system using cryptography. Investors in this emerging digital market are exploring the profitability potential of portfolios in place of single coins. Portfolios are particularly useful given that price forecasting in such a volatile market is challenging. The crypto market is a self-organised complex system where the complex inter-dependencies between the cryptocurrencies may be exploited to understand the market dynamics and build efficient portfolios. In this letter, we use network methods to identify highly decorrelated cryptocurrencies to create diversified portfolios using the Markowitz Portfolio Theory agnostic to future market behaviour. The performance of our network-based portfolios is optimal with 46 coins and superior to benchmarks up to an investment horizon of 14 days, reaching up to 1, 066% average expected return within 1 day, with reasonable associated risks. We also show that popular cryptocurrencies are typically not included in the optimal portfolios. Past price correlations reduce risk and may improve the performance of crypto portfolios in comparison to methodologies based exclusively on price auto-correlations. Short-term crypto investments may be competitive to traditional high-risk investments such as the stock market or commodity market but call for caution given the high variability of prices.
    Date: 2023–04
  10. By: Fjærvik, Thomas (Dept. of Business and Management Science, Norwegian School of Economics)
    Abstract: This paper takes the viewpoint of an investor that can invest in the Nordic countries Norway, Sweden, Denmark and Finland. The four markets are treated as one integrated market. In the analysis we investigate whether there exists a risk premium for investing in stocks exhibiting high crash risk, as measured by their lower tail dependence with the rest of the market portfolio. We indeed find evidence that this is the case, and this evidence is in line with previous research done on American and German stocks markets, as well as theoretical predictions in the literature. However, the results are less clear than was the case for the abovementioned markets. Lower tail dependence is estimated using convex combinations of copulas exhibiting different tail dependence characteristics. The results are robust to different portfolio formations and copula selection criteria.
    Keywords: Crash risk premium; copulas; Pearson correlation
    JEL: G00 G01 G11 G12
    Date: 2023–04–28
  11. By: Christopher P. Chambers; Georgios Gerasimou
    Abstract: Although portfolio diversification is the typical strategy followed by risk-averse investors, extreme portfolios that allocate all funds to a single asset/state of the world are common too. Such asset-demand behavior is compatible with risk-averse subjective expected utility maximization under beliefs that assign a strictly positive probability to every state. We show that whenever finitely many extreme asset demands are rationalizable in this way under such beliefs, they are simultaneously rationalizable under the same beliefs by: (i) constant absolute risk aversion; decreasing absolute risk aversion/increasing relative risk aversion (DARA/IRRA); risk-neutral; and ris-kseeking utility indices at all wealth levels; (ii) a distinct class of DARA/IRRA utility indices at some strictly positive fixed initial wealth; and (iii) decreasing relative risk aversion utility indices under bounded wealth. We also show that, in such situations, the observable data allow for sharp bounds to be given for the relevant parameters in each of the above classes of risk-averse preferences.
    Date: 2023–04
  12. By: Luka Klin\v{c}i\'c; Vinko Zlati\'c; Guido Caldarelli; Hrvoje \v{S}tefan\v{c}i\'c
    Abstract: The study of systemic risk is often presented through the analysis of several measures referring to quantities used by practitioners and policy makers. Almost invariably, those measures evaluate the size of the impact that exogenous events can exhibit on a financial system without analysing the nature of initial shock. Here we present a symmetric approach and propose a set of measures that are based on the amount of exogenous shock that can be absorbed by the system before it starts to deteriorate. For this purpose, we use a linearized version of DebtRank that allows to clearly show the onset of financial distress towards a correct systemic risk estimation. We show how we can explicitly compute localized and uniform exogenous shocks and explained their behavior though spectral graph theory. We also extend analysis to heterogeneous shocks that have to be computed by means of Monte Carlo simulations. We believe that our approach is more general and natural and allows to express in a standard way the failure risk in financial systems.
    Date: 2023–04
  13. By: Tom, Daniel M. Ph.D.
    Abstract: We optimize risk strategies going beyond a simple score cut to a dual score multi-cell strategy matrix. We further generalize to higher dimensions, and provide an example 3-D stairstep risk strategy optimization in code. Such algorithm is necessary to handle the huge number of stairstep boundaries for large matrices in high dimensions.
    Date: 2023–04–02
  14. By: Nicolás Salamanca (Melbourne Institute, University of Melbourne); Buly A. Cardak (La Trobe Business School, La Trobe University); Edwin Ip (Department of Economics, University of Exeter); Joe Vecci (Department of Economics, University of Gothenburg)
    Abstract: Time-stability of preferences is a crucial assumption in economics. We develop a novel test-retest method to examine the stability of risk preferences over time, while quantifying the importance of both idiosyncratic shocks and measurement error. Using eight large, representative datasets from developing and developed countries, we find risk preferences to be unstable in developing countries. In contrast, they are very stable in developed countries, except for low-income individuals in the U.S.. We discuss the important implications of these findings for policies and research.
    Keywords: risk preferences, stability, economic development
    JEL: D01 D81 O10 C18
    Date: 2023–04–17
  15. By: Ha Nguyen
    Abstract: Credit risk is a crucial topic in the field of financial stability, especially at this time given the profound impact of the ongoing pandemic on the world economy. This study provides insight into the impact of credit risk on the financial performance of 26 commercial banks in Vietnam for the period from 2006 to 2016. The financial performance of commercial banks is measured by return on assets (ROA), return on equity (ROE), and Net interest margin (NIM); credit risk is measured by the Non-performing loan ratio (NPLR); control variables are measured by bank-specific characteristics, including bank size (SIZE), loan loss provision ratio (LLPR), and capital adequacy ratio (CAR), and macroeconomic factors such as annual gross domestic product (GDP) growth and annual inflation rate (INF). The assumption tests show that models have autocorrelation, non-constant variance, and endogeneity. Hence, a dynamic Difference Generalized Method of Moments (dynamic Difference GMM) approach is employed to thoroughly address these problems. The empirical results show that the financial performance of commercial banks measured by ROE and NIM persists from one year to the next. Furthermore, SIZE and NPLR variables have a significant negative effect on ROA and ROE but not on NIM. There is no evidence found in support of the LLPR and CAR variables on models. The effect of GDP growth is statistically significant and positive on ROA, ROE, and NIM, whereas the INF is only found to have a significant positive impact on ROA and NIM.
    Date: 2023–04
  16. By: Mr. German Villegas Bauer; Maddalena Ghio; Linda Rousova; Dilyara Salakhova
    Abstract: During the March 2020 market turmoil, euro area money-market funds (MMFs) experienced significant outflows, reaching almost 8% of assets under management. This paper investigates whether the volatility in MMF flows was driven by investors’ liquidity needs related to derivative margin payments. We combine three highly granular unique data sources (EMIR data for derivatives, SHSS data for investor holdings of MMFs and Refinitiv Lipper data for daily MMF flows) to construct a daily fund-level panel dataset spanning from February to April 2020. We estimate the effects of variation margin paid and received by the largest holders of EURdenominated MMFs on flows of these MMFs. The main findings suggest that variation margin payments faced by some investors holding MMFs were an important driver of the flows of EUR-denominated MMFs domiciled in euro area.
    Keywords: liquidity risk; money market funds; big data; interconnectedness; non-bank financial intermediaries.; EUR-denominated MMFs; derivative margin calls; MMF flow; variation margin payment; VM flow; Stocks; Liquidity; Mutual funds; Nonbank financial institutions; Pension spending; Global
    Date: 2023–03–17
  17. By: Md Shah Naoaj
    Abstract: This study investigates the factors that influence the capital adequacy of commercial banks in Bangladesh using panel data from 28 banks over the period of 2013-2019. Three analytical methods, including the Fixed Effect model, Random Effect model, and Pooled Ordinary Least Square (POLS) method, are employed to analyze two versions of the capital adequacy ratio, namely the Capital Adequacy Ratio (CAR) and Tier 1 Capital Ratio. The study reveals that capital adequacy is significantly affected by several independent variables, with leverage and liquidity risk having a negative and positive relationship, respectively. Additionally, the study finds a positive correlation between real GDP and net profit and capital adequacy, while inflation has a negative correlation. For the Tier 1 Ratio, the study shows no significant relationship betweenleverage and liquidity risk, but a positive correlation with the number of employees, net profit, and real GDP, while a negative correlation with size and GDP deflator. Pooled OLS analysis reveals a negative correlation with leverage, size, and inflation for both CAR and Tier 1 Capital Ratio, and a positive correlation with liquidity risk, net profit, and real GDP. Based on the Hausman test, the Random Effect model is deemed moresuitable for this dataset. These findings have important implications for policymakers, investors, and bank managers in Bangladesh by providing insights into the factors that impact the capital ratios of commercial banks.
    Date: 2023–03
  18. By: Mingyu Hao; Artem Lenskiy
    Abstract: As a newly emerged asset class, cryptocurrency is evidently more volatile compared to the traditional equity markets. Due to its mostly unregulated nature, and often low liquidity, the price of crypto assets can sustain a significant change within minutes that in turn might result in considerable losses. In this paper, we employ an approach for encoding market information into images and making predictions of short-term realized volatility by employing Convolutional Neural Networks. We then compare the performance of the proposed encoding and corresponding model with other benchmark models. The experimental results demonstrate that this representation of market data with a Convolutional Neural Network as a predictive model has the potential to better capture the market dynamics and a better volatility prediction.
    Date: 2023–04
  19. By: Ofelia Bonesini; Antoine Jacquier; Alexandre Pannier
    Abstract: In the setting of stochastic Volterra equations, and in particular rough volatility models, we show that conditional expectations are the unique classical solutions to path-dependent PDEs. The latter arise from the functional It\^o formula developed by [Viens, F., & Zhang, J. (2019). A martingale approach for fractional Brownian motions and related path dependent PDEs. Ann. Appl. Probab.]. We then leverage these tools to study weak rates of convergence for discretised stochastic integrals of smooth functions of a Riemann-Liouville fractional Brownian motion with Hurst parameter $H \in (0, 1/2)$. These integrals approximate log-stock prices in rough volatility models. We obtain weak error rates of order 1 if the test function is quadratic and of order $H+1/2$ for smooth test functions.
    Date: 2023–04
  20. By: Tomas E. Caravello (Massachusetts Institute of Technology); John Driffill (Yale-NUS); Turalay Kenc (University of Cambridge); Martin Sola (Universidad Torcuato di Tella)
    Abstract: We develop and estimate a consumption-based asset pricing model that assumes recursive utility using historical US financial data, allowing for regime changes, priced regime risk, and intrinsic bubbles. We also estimate several restricted versions which include only a subset of these features. We find that switching risk is an essential component of the equity risk premium, explaining up to fifty percent of it. Furthermore, a model which does not take this into account would overestimate the degree of risk aversion of the public, mistakenly assigning the observed risk premium to high-risk aversion instead of priced regime-switching. Intrinsic bubbles are not crucial in explaining the risk premia, but they substantially improve the model’s fit at the end of the sample.
    Keywords: Equity Risk Premium; Macroeconomic Risk; Stochastic Differential Utility; Markov Chain; Intrinsic Bubbles
    JEL: G00 G12 E44 C32
    Date: 2023–04
  21. By: Silvia Rossetto (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Nassima Selmane (Unknown); Raffaele Stagliano (Unknown)
    Abstract: This study analyzes the relationship between mid-sized blockholders and firm risk. We show that ownership structure matters for firm risk beyond the first largest blockholder. Firms with multiple blockholders take more risk than firms with just one blockholder, even when controlling for the stake of the largest blockholder. Consistent with the diversification argument, we find that firm risk increases by 22% when the number of blockholders increases from one to two. Our results are robust to controlling for blockholder type and firm characteristics. We carry out various robustness checks to tackle endogeneity issues. More generally, we provide evidence that firms' decisions are affected by mid-sized blockholders and not merely the largest blockholder. This is in line with theoretical predictions.
    Date: 2022–05–31
  22. By: Kaihua Qin; Jens Ernstberger; Liyi Zhou; Philipp Jovanovic; Arthur Gervais
    Abstract: Liquidations in Decentralized Finance (DeFi) are both a blessing and a curse -- whereas liquidations prevent lenders from capital loss, they simultaneously lead to liquidation spirals and system-wide failures. Since most lending and borrowing protocols assume liquidations are indispensable, there is an increased interest in alternative constructions that prevent immediate systemic-failure under uncertain circumstances. In this work, we introduce reversible call options, a novel financial primitive that enables the seller of a call option to terminate it before maturity. We apply reversible call options to lending in DeFi and devise Miqado, a protocol for lending platforms to replace the liquidation mechanisms. To the best of our knowledge, Miqado is the first protocol that actively mitigates liquidations to reduce the risk of liquidation spirals. Instead of selling collateral, Miqado incentivizes external entities, so-called supporters, to top-up a borrowing position and grant the borrower additional time to rescue the debt. Our simulation shows that Miqado reduces the amount of liquidated collateral by 89.82% in a worst-case scenario.
    Date: 2023–02
  23. By: Maurizio Trapanese (Banca d'Italia); Michele Lanotte (Banca d'Italia)
    Abstract: Technological progress in finance has been accelerating over the last decade. In the future, it is likely that financial intermediaries may undergo significant challenges as regards their traditional business model and functions, since an increasing share of payments may be settled without banks’ deposits and capital markets may increasingly provide direct credit to the economy. This paper aims to outline the theoretical and regulatory implications stemming from digital financial markets, with a particular focus on the growing importance of BigTech and FinTech firms. We study the importance of information and communication in financial intermediation, and outline the impact of technological progress on the core functions traditionally performed by banks and other financial institutions, and on payment systems. In this context, we discuss the role of public policies, and the main issues for regulation, supervision, competition, and consumer protection.
    Keywords: firm behaviour, international financial markets, financial institutions, financial policy and regulation, risk management JEL Classification: D21, G15, G20, G28, G32
    Date: 2023–04

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