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on Risk Management |
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Issue of 2021‒07‒26
35 papers chosen by |
| By: | Cosimo Munari; Lutz Wilhelmy; Stefan Weber |
| Abstract: | Protection of creditors is a key objective of financial regulation. Where the protection needs are high, i.e., in banking and insurance, regulatory solvency requirements are an instrument to prevent that creditors incur losses on their claims. The current regulatory requirements based on Value at Risk and Average Value at Risk limit the probability of default of financial institutions, but they fail to control the size of recovery on creditors' claims in the case of default. We resolve this failure by developing a novel risk measure, Recovery Value at Risk. Our conceptual approach can flexibly be extended and allows the construction of general recovery risk measures for various risk management purposes. By design, these risk measures control recovery on creditors' claims and integrate the protection needs of creditors into the incentive structure of the management. We provide detailed case studies and applications: We analyze how recovery risk measures react to the joint distributions of assets and liabilities on firms' balance sheets and compare the corresponding capital requirements with the current regulatory benchmarks based on Value at Risk and Average Value at Risk. We discuss how to calibrate recovery risk measures to historic regulatory standards. Finally, we show that recovery risk measures can be applied to performance-based management of business divisions of firms and that they allow for a tractable characterization of optimal tradeoffs between risk and return in the context of investment management. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.10635&r= |
| By: | Chabi-Yo, Fousseni; Huggenberger, Markus; Weigert, Florian |
| Abstract: | This paper investigates whether multivariate crash risk (MCRASH), defined as exposure to extreme realizations of multiple systematic factors, is priced in the cross-section of expected stock returns. We derive an extended linear model with a positive premium for MCRASH and we empirically confirm that stocks with high MCRASH earn significantly higher future returns than stocks with low MCRASH. The premium is not explained by linear factor exposures, alternative downside risk measures or stock characteristics. Extending market-based definitions of crash risk to other well-established factors helps to determine the cross-section of expected stock returns without further expanding the factor zoo. |
| Keywords: | Asset pricing,Non-linear dependence,Crash aversion,Downside risk,Tail risk,Lower tail dependence,Copulas |
| JEL: | C58 G01 G11 G12 G17 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:2107&r= |
| By: | International Monetary Fund |
| Abstract: | The HKFE Clearing Corporation Limited (HKCC) observes the CPSS/IOSCO Principles for Financial Market Infrastructures (PFMI). It has a sound, coherent and transparent legal basis. As an integral part of the Hong Kong Exchanges and Clearing Limited (HKEX Group), the HKCC has a comprehensive and adequate risk management framework to address financial, business, and operational risks. Participant assets as well as HKCC’s collaterals are safely kept in several banks and regulated central securities depositories. The credit and liquidity risks are minimized by having a robust risk management framework, including rigorous stress testing methodology and access to qualifying liquid resources. Furthermore, the HKCC has clear rules and procedures to handle and manage a participant’s default procedures. Moreover, the HKCC has established risk management framework to handle operational risk, including cyber risk, and business continuity management that addresses events posing significant risk of operational disruption. |
| Keywords: | Hong Kong exchange fund bill; People's Republic of China-Hong Kong Special Administrative Region FSAP; Hong Kong securities market; HKCC rule; derivatives Clearing; HKCC Participants; Collateral; Credit risk; Liquidity risk; Operational risk; Currencies; Global |
| Date: | 2021–06–15 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfscr:2021/122&r= |
| By: | Akif Ince; Ilaria Peri; Silvana Pesenti |
| Abstract: | Risk contributions of portfolios form an indispensable part of risk adjusted performance measurement. The risk contribution of a portfolio, e.g., in the Euler or Aumann-Shapley framework, is given by the partial derivatives of a risk measure applied to the portfolio return in direction of the asset weights. For risk measures that are not positively homogeneous of degree 1, however, known capital allocation principles do not apply. We study the class of lambda quantile risk measures, that includes the well-known Value-at-Risk as a special case, but for which no known allocation rule is applicable. We prove differentiability and derive explicit formulae of the derivatives of lambda quantiles with respect to their portfolio composition, that is their risk contribution. For this purpose, we define lambda quantiles on the space of portfolio compositions and consider generic (also non-linear) portfolio operators. We further derive the Euler decomposition of lambda quantiles for generic portfolios and show that lambda quantiles are homogeneous in the space of portfolio compositions, with a homogeneity degree that depends on the portfolio composition and the lambda function. This result is in stark contrast to the positive homogeneity properties of risk measures defined on the space of random variables which admit a constant homogeneity degree. We introduce a generalised version of Euler contributions and Euler allocation rule, which are compatible with risk measures of any homogeneity degree and non-linear portfolios. We further provide financial interpretations of the homogeneity degree of lambda quantiles and introduce the notion of event-specific homogeneity of portfolio operators. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.14824&r= |
| By: | Hengxu Lin; Dong Zhou; Weiqing Liu; Jiang Bian |
| Abstract: | Modeling and managing portfolio risk is perhaps the most important step to achieve growing and preserving investment performance. Within the modern portfolio construction framework that built on Markowitz's theory, the covariance matrix of stock returns is required to model the portfolio risk. Traditional approaches to estimate the covariance matrix are based on human designed risk factors, which often requires tremendous time and effort to design better risk factors to improve the covariance estimation. In this work, we formulate the quest of mining risk factors as a learning problem and propose a deep learning solution to effectively "design" risk factors with neural networks. The learning objective is carefully set to ensure the learned risk factors are effective in explaining stock returns as well as have desired orthogonality and stability. Our experiments on the stock market data demonstrate the effectiveness of the proposed method: our method can obtain $1.9\%$ higher explained variance measured by $R^2$ and also reduce the risk of a global minimum variance portfolio. Incremental analysis further supports our design of both the architecture and the learning objective. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.05201&r= |
| By: | Abu Karsh, Sharif M. |
| Abstract: | Financial institution within the USA is faced with great challenge of risk management, hence the pursuit of every financial institution to come up with better innovative ways of managing risks. However, the emerging innovation in risk management in financial institution has an underlying negative implication which is yet to be studied. The aim of this research was to explore emerging innovation in risk management in financial institutions. The research utilized qualitative research design, through an intensive literature review that involved deep research and reviewing of academic scholarly academic articles. This type of approach ensures that the research includes wide variety of sources that support this research and making it viable for future reference. Results showed that the emerging innovation in risk management in financial institutions is digital financing. Owing to the associated implication of excessive technology use, the research suggests that financial institutions should be very cautious, particularly with the associated risk of cybercrime. |
| Date: | 2021–06–26 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:3j62p&r= |
| By: | Heidorn, Thomas; Pottmeyer, Andreas |
| Abstract: | The aim of this working paper is to introduce the reader to the relatively new instrument of AT 1 bonds. For this purpose, the strict regulatory requirements for the instrument class are explained and the capital requirements of banks are outlined. Afterwards, the market for AT 1 bonds is analyzed and the interests of the respective market participants are discussed. Finally, the Credit Derivatives Model, the Equity Derivatives Model and the Value at Risk model are applied to 20 AT 1 bonds issued by various European banks in order to find the extra credit spread and to determine the risk associated with this bond class. |
| Keywords: | AT1,Additional Tier 1,Regulatory Requirements,Bank Capital Management,Capital Buffer,Capital Requirements Regulation (CRR),Capital Requirements Directive IV,Hybrid Tier 1,Pillar 1 Requirements |
| JEL: | G12 G18 G28 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:fsfmwp:229&r= |
| By: | Zongxia Liang; Yang Liu; Ming Ma |
| Abstract: | We propose a general family of piecewise hyperbolic absolute risk aversion (PHARA) utility, including many non-standard utilities as examples. A typical application is the composition of an HARA preference and a piecewise linear payoff in hedge fund management. We derive a unified closed-form formula of the optimal portfolio, which is a four-term division. The formula has clear economic meanings, reflecting the behavior of risk aversion, risk seeking, loss aversion and first-order risk aversion. One main finding is that risk-taking behaviors are greatly increased by non-concavity and reduced by non-differentiability. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.06460&r= |
| By: | Silvana M. Pesenti |
| Abstract: | We consider the problem where a modeller conducts sensitivity analysis of a model consisting of random input factors, a corresponding random output of interest, and a baseline probability measure. The modeller seeks to understand how the model (the distribution of the input factors as well as the output) changes under a stress on the output's distribution. Specifically, for a stress on the output random variable, we derive the unique stressed distribution of the output that is closest in the Wasserstein distance to the baseline output's distribution and satisfies the stress. We further derive the stressed model, including the stressed distribution of the inputs, which can be calculated in a numerically efficient way from a set of baseline Monte Carlo samples. The proposed reverse sensitivity analysis framework is model-free and allows for stresses on the output such as (a) the mean and variance, (b) any distortion risk measure including the Value-at-Risk and Expected-Shortfall, and (c) expected utility type constraints, thus making the reverse sensitivity analysis framework suitable for risk models. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.01065&r= |
| By: | Peter Carr; Roger Lee; Matthew Lorig |
| Abstract: | We price and replicate a variety of claims written on the log price $X$ and quadratic variation $[X]$ of a risky asset, modeled as a positive semimartingale, subject to stochastic volatility and jumps. The pricing and hedging formulas do not depend on the dynamics of volatility process, aside from integrability and independence assumptions; in particular, the volatility process may be non-Markovian and exhibit jumps of unknown distribution. The jump risk may be driven by any finite activity Poisson random measure with bounded jump sizes. As hedging instruments, we use the underlying risky asset, a zero-coupon bond, and European calls and puts with the same maturity as the claim to be hedged. Examples of contracts that we price include variance swaps, volatility swaps, a claim that pays the realized Sharpe ratio, and a call on a leveraged exchange traded fund. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.00554&r= |
| By: | Thomas Knispel; Roger J. A. Laeven; Gregor Svindland |
| Abstract: | We analyze the limiting behavior of the risk premium associated with the Pareto optimal risk sharing contract in an infinitely expanding pool of risks under a general class of law-invariant risk measures encompassing rank-dependent utility preferences. We show that the corresponding convergence rate is typically only $n^{1/2}$ instead of the conventional $n$, with $n$ the multiplicity of risks in the pool, depending upon the precise risk preferences. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.01730&r= |
| By: | Farhad Billimoria; Filiberto Fele; Iacopo Savelli; Thomas Morstyn; Malcolm McCulloch |
| Abstract: | Securing an adequate supply of dispatchable resources is critical for keeping a power system reliable under high penetrations of variable generation. Traditional resource adequacy mechanisms are poorly suited to exploiting the growing flexibility and heterogeneity of load enabled by advancements in distributed resource and control technology. To address these challenges this paper develops a resource adequacy mechanism for the electricity sector utilising insurance risk management frameworks that is adapted to a future with variable generation and flexible demand. The proposed design introduces a central insurance scheme with prudential requirements that align diverse consumer reliability preferences with the financial objectives of an insurer-of-last-resort. We illustrate the benefits of the scheme in (i) differentiating load by usage to enable better management of the system during times of extreme scarcity, (ii) incentivising incremental investment in generation infrastructure that is aligned with consumer reliability preferences and (iii) improving overall reliability outcomes for consumers. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.14351&r= |
| By: | Chongrui Zhu |
| Abstract: | In this paper, a dividend optimization problem with a terminal value at creeping ruin for Levy risk models has been investigated. We consider an insurance company whose surplus process evolves as a spectrally negative Levy process with a Gaussian part and its objective function is given by cumulative discounted dividend payments and a terminal value at creeping ruin. In views of identities from fluctuation theory, under the restriction on the negative terminal value, we show that the threshold strategy turns out to be the optimal one with threshold level at zero over an admissible class with restricted dividend rates. Furthermore, some sufficient conditions for the positive one also have been given. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.06841&r= |
| By: | Lorenzo Silotto; Marco Scaringi; Marco Bianchetti |
| Abstract: | Valuation adjustments, collectively named XVA, play an important role in modern derivatives pricing. XVA are an exotic pricing component since they require the forward simulation of multiple risk factors in order to compute the portfolio exposure including collateral, leading to a significant model risk and computational effort, even in case of plain vanilla trades. This work analyses the most critical model risk factors, meant as those to which XVA are most sensitive, finding an acceptable compromise between accuracy and performance. This task has been conducted in a complete context including a market standard multi-curve G2++ model calibrated on real market data, both Variation Margin and ISDA-SIMM dynamic Initial Margin, different collateralization schemes, and the most common linear and non-linear interest rates derivatives. Moreover, we considered an alternative analytical approach for XVA in case of uncollateralized Swaps. We show that a crucial element is the construction of a parsimonious time grid capable of capturing all periodical spikes arising in collateralized exposure during the Margin Period of Risk. To this end, we propose a workaround to efficiently capture all spikes. Moreover, we show that there exists a parameterization which allows to obtain accurate results in a reasonable time, which is a very important feature for practical applications. In order to address the valuation uncertainty linked to the existence of a range of different parameterizations, we calculate the Model Risk AVA (Additional Valuation Adjustment) for XVA according to the provisions of the EU Prudent Valuation regulation. Finally, this work can serve as an handbook containing step-by-step instructions for the implementation of a complete, realistic and robust modelling framework of collateralized exposure and XVA. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.10377&r= |
| By: | Li, Wei; Paraschiv, Florentina; Sermpinis, Georgios |
| Abstract: | The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field. |
| Keywords: | Case-based reasoning,Financial risk detection,Multiple-criteria decision-making,Feature scoring,Particle swarm optimization,Parallel computing |
| JEL: | C51 C52 C53 C61 C63 D81 G21 G32 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2021010&r= |
| By: | N. Packham; F. Woebbeking |
| Abstract: | We develop a general approach for stress testing correlations of financial asset portfolios. The correlation matrix of asset returns is specified in a parametric form, where correlations are represented as a function of risk factors, such as country and industry factors. A sparse factor structure linking assets and risk factors is built using Bayesian variable selection methods. Regular calibration yields a joint distribution of economically meaningful stress scenarios of the factors. As such, the method also lends itself as a reverse stress testing framework: using the Mahalanobis distance or highest density regions (HDR) on the joint risk factor distribution allows to infer worst-case correlation scenarios. We give examples of stress tests on a large portfolio of European and North American stocks. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.06839&r= |
| By: | Jose M. Berrospide; Arun Gupta; Matthew P. Seay |
| Abstract: | Did banks curb lending to creditworthy small and mid-sized enterprises (SME) during the COVID-19 pandemic? Sitting on top of minimum capital requirements, regulatory capital buffers introduced after the 2008 global financial crisis (GFC) are costly regions of “rainy day†equity capital designed to absorb losses and provide lending capacity in a downturn. Using a novel set of confidential loan level data that includes private SME firms, we show that “buffer-constrained†banks (those entering the pandemic with capital ratios close to this regulatory buffer region) reduced loan commitments to SME firms by an average of 1.4 percent more (quarterly) and were 4 percent more likely to end pre-existing lending relationships during the pandemic as compared to “buffer-unconstrained†banks (those entering the pandemic with capital ratios far from the regulatory capital buffer region). We further find heterogenous effects across firms, as buffer-constrained banks disproportionately curtailed credit to three types of borrowers: (1) private, bank-dependent SME firms, (2) firms whose lending relationships were relatively young, and (3) firms whose pre-pandemic credit lines contractually matured at the start of the pandemic (and thus were up for renegotiation). While the post-2008 period saw the rise of banking system capital to historically high levels, these capital buffers went effectively unused during the pandemic. To the best of our knowledge, our study is the first to: (1) empirically test the usability of these Basel III regulatory buffers in a downturn, and (2) contribute a bank capital-based transmission channel to the literature studying the effects of the pandemic on SME firms. |
| Keywords: | Financial institutions; Capital regulation; Procyclicality; COVID-19 |
| JEL: | G20 G21 G28 |
| Date: | 2021–07–15 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2021-43&r= |
| By: | Jie Cao (The Chinese University of Hong Kong (CUHK) - CUHK Business School); Amit Goyal (University of Lausanne; Swiss Finance Institute); Xintong Zhan (The Chinese University of Hong Kong (CUHK) - CUHK Business School); Weiming Elaine Zhang (The Chinese University of Hong Kong (CUHK)) |
| Abstract: | We find that option expensiveness, as measured by implied volatility, is higher for low-ESG stocks, showing that investors pay a premium in the option market to hedge ESG-related uncertainty. Using delta-hedged option returns, we estimate this ESG premium to be about 0.3% per month. All three components of ESG contribute to option pricing. The effect of ESG performance heightens after the announcement of Paris Agreement, after speeches of Greta Thunberg, and in the aftermath of Me-Too movement. We find that investors pay ESG premium to hedge volatility, jump, and other higher moment risks. The influence of ESG on option premia is stronger for firms that are closer to end-consumers, facing severer product competition, with higher investors’ ESG awareness, and without corporate hedging activity. |
| Keywords: | ESG, implied volatility, delta-hedged option return |
| JEL: | G12 G14 G41 M14 |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2139&r= |
| By: | Masud Alam |
| Abstract: | This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of the conditional Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), which predicts an average maximum of 5.6 percent of risk premium in REIT equity return. In subsample analyses, the positive relationship is not affected by sample periods' choice but shows higher housing risk beta values for the 2009-18 sample period. The relationship remains significant after controlling for VIX, Fama-French three factors, and a broad set of macroeconomic and financial variables. Moreover, the proposed housing beta also accurately forecasts U.S. macroeconomic and financial conditions. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.10455&r= |
| By: | Raphaël Cardot-Martin (CRESE EA3190, Univ. Bourgogne Franche-Comté, F-25000 Besançon, France); Fabien Labondance (CRESE EA3190, Univ. Bourgogne Franche-Comté, F-25000 Besançon, France); Catherine Refait-Alexandre (CRESE EA3190, Univ. Bourgogne Franche-Comté, F-25000 Besançon, France) |
| Abstract: | We assess if capital ratios reduced the occurrence of banking crises in the European Union from 1998 to 2017. We use a Probit model and estimate the effect of two measures: the bank capital to total assets ratio and the bank regulatory capital to Risk Weighted Assets (RWA). We found that both measures affect negatively the probability of crisis. This result is robust to the exclusion of outliers, to the inclusion of various control variables for banking, financial and macroeconomic risks. Finally, we show that while the bank regulatory capital to RWA has always a negative effect on the probability of crisis, the bank capital to total assets ratio is only significant above a threshold, estimated between 10% and 12%. |
| Keywords: | Unconventional measures, retail interest rate, Heterogeneous panel |
| JEL: | G21 E44 |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:crb:wpaper:2021-05&r= |
| By: | Wolfgang Schadner |
| Abstract: | Forward-looking correlations are of interest in different financial applications, including factor-based asset pricing, forecasting stock-price movements or pricing index options. With a focus on non-FX markets, this paper defines necessary conditions for option implied correlation matrices to be mathematically and economically feasible and argues, that existing models are typically not capable of guaranteeing so. To overcome this difficulty, the problem is addressed from the underlying factor structure and introduces two approaches to solve it. Under the quantitative approach, the puzzle is reformulated into a nearest correlation matrix problem which can be used either as a stand-alone estimate or to re-establish positive-semi-definiteness of any other model's estimate. From an economic approach, it is discussed how expected correlations between stocks and risk factors (like CAPM, Fama-French) can be translated into a feasible implied correlation matrix. Empirical experiments are carried out on monthly option data of the S\&P 100 and S\&P 500 index (1996-2020). |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.00427&r= |
| By: | Carol Alexander; Daniel Heck; Andreas Kaeck |
| Abstract: | We analyse high-frequency realised volatility dynamics and spillovers in the bitcoin market, focusing on two pairs: bitcoin against the US dollar (the main fiat-crypto pair) and trading bitcoin against tether (the main crypto-crypto pair). We find that the tether-margined perpetual contract on Binance is clearly the main source of volatility, continuously transmitting strong flows to all other instruments and receiving only a little volatility. Moreover, we find that (i) during US trading hours, traders pay more attention and are more reactive to prevailing market conditions when updating their expectations and (ii) the crypto market exhibits a higher interconnectedness when traditional Western stock markets are open. Our results highlight that regulators should not only consider spot exchanges offering bitcoin-fiat trading but also the tether-margined derivatives products available on most unregulated exchanges, most importantly Binance. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.00298&r= |
| By: | Gautier Marti; Victor Goubet; Frank Nielsen |
| Abstract: | We propose a methodology to approximate conditional distributions in the elliptope of correlation matrices based on conditional generative adversarial networks. We illustrate the methodology with an application from quantitative finance: Monte Carlo simulations of correlated returns to compare risk-based portfolio construction methods. Finally, we discuss about current limitations and advocate for further exploration of the elliptope geometry to improve results. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.10606&r= |
| By: | Patrick Martin (Environmental law specialist and Consultan, Macroeconomic and Financing for Development Division, UNESCAP); Zeinab Elbeltagy (Consultant, Macroeconomic and Financing for Development Division, UNESCAP); Zenathan Hasannudin; Masato Abe (Macroeconomic Policy and Financing for Development Division, UNESCAP) |
| Abstract: | Considering the significant effect financial institutions (FIs) have on society and the environment, they have a crucial role in achieving the Sustainable Development Goals (SDG) and addressing climate change concerns. Not surprisingly, there is a growing interest in how FIs manage the environmental and social (E&S) risks emanating from their activities. While studying the ‘Innovative Climate Finance Mechanisms for Financial Institutions’, we conducted a survey to investigate the factors affecting FIs’ E&S performance in 11 countries in the Asia-Pacific region. This paper outlines the survey findings and provides insights into the factors affecting E&S performance of FIs. The paper identifies that awareness of E&S risks in the region is growing but from a low base and that E&S risks are increasingly integrated into risk management analysis and reporting frameworks. The paper demonstrates that although some FIs have made significant progress, considerable variation still exists among countries and institutions, and considerable work is still needed to improve E&S performance of FIs in the region. The paper highlights that although policy reforms and engagement can, over time, influence E&S performance of FIs, a lack of management support and institutional capacity remain significant constraints. The paper can assist policymakers in understanding the factors affecting E&S performance of FIs and in distilling the policy options needed to help them better integrate E&S risks into their operations |
| Keywords: | Financial institutions, Climate Finance, Environmental and Social Risk paper |
| JEL: | F65 G23 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:unt:wpmpdd:wp/21/01&r= |
| By: | Nicklas Werge |
| Abstract: | Financial markets tend to switch between various market regimes over time, making stationarity-based models unsustainable. We construct a regime-switching model independent of asset classes for risk-adjusted return predictions based on hidden Markov models. This framework can distinguish between market regimes in a wide range of financial markets such as the commodity, currency, stock, and fixed income market. The proposed method employs sticky features that directly affect the regime stickiness and thereby changing turnover levels. An investigation of our metric for risk-adjusted return predictions is conducted by analyzing daily financial market changes for almost twenty years. Empirical demonstrations of out-of-sample observations obtain an accurate detection of bull, bear, and high volatility periods, improving risk-adjusted returns while keeping a preferable turnover level. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.05535&r= |
| By: | International Monetary Fund |
| Abstract: | Banking supervision and regulation by the Hong Kong Monetary Authority (HKMA) remain strong. This assessment confirms the 2014 Basel Core Principles assessment that the HKMA achieves a high level of compliance with the BCPs. The Basel III framework (and related guidance) and domestic and cross-border cooperation arrangements are firmly in place. The HKMA actively contributes to the development and implementation of relevant international standards. Updating their risk based supervisory approach helped the HKMA optimize supervisory resources. The HKMA’s highly experienced supervisory staff is a key driver to achieving one of the most sophisticated levels of supervision and regulation observed in Asia and beyond. |
| Keywords: | People's republic of china-Hong kong special administrative region; B. banking sector structure; holding company; China banking; People's Republic of China-Hong Kong Special Administrative Region FSAP; Bank supervision; Artificial intelligence; Commercial banks; Basel Core Principles; Bank soundness; Global; Central Asia; Asia and Pacific;External audit |
| Date: | 2021–06–15 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfscr:2021/118&r= |
| By: | Haselmann, Rainer; Tröger, Tobias |
| Abstract: | This in-depth analysis provides evidence on differences in the practice of supervising large banks in the UK and in the euro area. It identifies the diverging institutional architecture (partially supranationalised vs. national oversight) as a pivotal determinant for a higher effectiveness of supervisory decision making in the UK. The ECB is likely to take a more stringent stance in prudential supervision than UK authorities. The setting of risk weights and the design of macroprudential stress test scenarios document this hypothesis. This document was provided by the Economic Governance Support Unit at the request of the ECON Committee. |
| Keywords: | Bank Supervision,Economic Governance,Banking Union,Brexit |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safewh:86&r= |
| By: | Xiaoqing Liang; Ruodu Wang; Virginia Young |
| Abstract: | In this paper, we study an optimal insurance problem for a risk-averse individual who seeks to maximize the rank-dependent expected utility (RDEU) of her terminal wealth, and insurance is priced via a general distortion-deviation premium principle. We prove necessary and sufficient conditions satisfied by the optimal solution and consider three ambiguity orders to further determine the optimal indemnity. Finally, we analyze examples under three distortion-deviation premium principles to explore the specific conditions under which no insurance or deductible insurance is optimal. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.02656&r= |
| By: | Erik Andres-Escayola (Banco de España); Juan Carlos Berganza (Banco de España); Rodolfo Campos (Banco de España); Luis Molina (Banco de España) |
| Abstract: | This paper describes the set of Bayesian vector autoregression (BVAR) models that are being used at Banco de España to project GDP growth rates and to simulate macrofinancial risk scenarios for Brazil and Mexico. The toolkit consists of large benchmark models to produce baseline projections and various smaller satellite models to conduct risk scenarios. We showcase the use of this modelling framework with tailored empirical applications. Given the material importance of Brazil and Mexico to the Spanish economy and banking system, this toolkit contributes to the monitoring of Spain’s international risk exposure. |
| Keywords: | macroeconomic projections, risk scenarios, Bayesian vector autoregressions |
| JEL: | C32 C53 F44 F47 |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:bde:opaper:2114&r= |
| By: | International Monetary Fund |
| Abstract: | Hong Kong SAR (HKSAR) is a small and open economy, and a major international financial center with extensive linkages to Mainland China. Over the past two years, Hong Kong SAR’s economy and financial sector were adversely impacted by domestic social unrest, US-China tensions, and the global COVID-19 pandemic, resulting in an unprecedented two consecutive years of negative economic growth. |
| Keywords: | bank solvency stress; HKSAR banking system; bank solvency St result; solvency ANALYSIS; HKSAR GDP growth scenario; incorporated bank; Commercial banks; Liquidity risk; Liquidity; Mortgages; Loans; Global |
| Date: | 2021–06–15 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfscr:2021/114&r= |
| By: | Makarov, Dmitry |
| Abstract: | A prominent approach to modelling ambiguity about stock return distribution is to assume that investors have multiple priors about the distribution and these priors are distributed according to a certain second-order distribution. Realistically, investors may also have multiple priors about the second-order distribution, thus allowing for ambiguous ambiguity. Despite a long history of debates about this idea (Reichenbach [1949], Savage [1954]), there seems to be no formal analysis of investment behavior in the presence of this feature. We develop a tractable portfolio choice framework incorporating ambiguous ambiguity, characterize analytically the optimal portfolio, and examine its properties. |
| Keywords: | ambiguous ambiguity, portfolio choice, smooth ambiguity, third-order probabilities |
| JEL: | D81 G11 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:108837&r= |
| By: | Nassim Nicholas Taleb |
| Abstract: | We apply quantitative finance methods and economic arguments to cryptocurrencies in general and bitcoin in particular -- as there are about $10,000$ cryptocurrencies, we focus (unless otherwise specified) on the most discussed crypto of those that claim to hew to the original protocol (Nakamoto, 2009) and the one with, by far, the largest market capitalization. In its current version, in spite of the hype, bitcoin failed to satisfy the notion of "currency without government" (it proved to not even be a currency at all), can be neither a short nor long term store of value (its expected value is no higher than $0$), cannot operate as a reliable inflation hedge, and, worst of all, does not constitute, not even remotely, a safe haven for one's investments, a shield against government tyranny, nor a tail protection vehicle for catastrophic episodes. Furthermore, there appears to be an underlying conflation between the success of a payment mechanism (as a decentralized mode of exchange), which so far has failed, and the speculative variations in the price of a zero-sum asset with massive negative externalities. Going through monetary history, we also show how a true numeraire must be one of minimum variance with respect to an arbitrary basket of goods and services, how gold and silver lost their inflation hedge status during the Hunt brothers squeeze in the late 1970s and what would be required from a true inflation hedged store of value. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.14204&r= |
| By: | Dan Wang; Zhi Chen; Ionut Florescu |
| Abstract: | In Artificial Intelligence, interpreting the results of a Machine Learning technique often termed as a black box is a difficult task. A counterfactual explanation of a particular "black box" attempts to find the smallest change to the input values that modifies the prediction to a particular output, other than the original one. In this work we formulate the problem of finding a counterfactual explanation as an optimization problem. We propose a new "sparsity algorithm" which solves the optimization problem, while also maximizing the sparsity of the counterfactual explanation. We apply the sparsity algorithm to provide a simple suggestion to publicly traded companies in order to improve their credit ratings. We validate the sparsity algorithm with a synthetically generated dataset and we further apply it to quarterly financial statements from companies in financial, healthcare and IT sectors of the US market. We provide evidence that the counterfactual explanation can capture the nature of the real statement features that changed between the current quarter and the following quarter when ratings improved. The empirical results show that the higher the rating of a company the greater the "effort" required to further improve credit rating. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.10306&r= |
| By: | Lara Dalmeyer; Tim Gebbie |
| Abstract: | We investigate and extend the results of Golts and Jones (2009) that an alpha-weight angle resulting from unconstrained quadratic portfolio optimisations has an upper bound dependent on the condition number of the covariance matrix. This implies that better conditioned covariance matrices produce weights from unconstrained mean-variance optimisations that are better aligned with each assets expected return. We provide further clarity on the mathematical insights that relate the inequality between the $\alpha$-weight angle and the condition number and extend the result to include portfolio optimisations with gearing constraints. We provide an extended family of robust optimisations that include the gearing constraints, and discuss their interpretation. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.06194&r= |
| By: | R. G. Alcoforado; W. Bernardino; A. D. Eg\'idio dos Reis; J. A. C. Santos |
| Abstract: | This study analysed a series of live cattle spot and futures prices from the Boi Gordo Index (BGI) in Brazil. The objective was to develop a model that best portrays this commodity's behaviour to estimate futures prices more accurately. The database created contained 2,010 daily entries in which trade in futures contracts occurred, as well as BGI spot sales in the market, from 1 December 2006 to 30 April 2015. One of the most important reasons why this type of risk needs to be measured is to set loss limits. To identify patterns in price behaviour in order to improve future transactions' results, investors must analyse fluctuations in assets' value for longer periods. Bibliographic research revealed that no other study has conducted a comprehensive analysis of this commodity using this approach. Cattle ranching is big business in Brazil given that in 2017, this sector moved 523.25 billion Brazilian reals (about 130.5 billion United States dollars). In that year, agribusiness contributed 22% of Brazil's total gross domestic product. Using the proposed risk modelling technique, economic agents can make the best decision about which options within these investors' reach produce more effective risk management. The methodology was based on Holt-Winters exponential smoothing algorithm, autoregressive integrated moving average (ARIMA), ARIMA with exogenous inputs, generalised autoregressive conditionally heteroskedastic and generalised autoregressive moving average (GARMA) models. More specifically, 5 different methods were applied that allowed a comparison of 12 different models as ways to portray and predict the BGI commodity's behaviour. The results show that GARMA with order c(2,1) and without intercept is the best model. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.07556&r= |