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on Risk Management |
| By: | Shafique Ur Rehman; Touqeer Ahmad; Wu Dash Desheng; Amirhossein Karamoozian |
| Abstract: | This study examines the interdependence between cryptocurrencies and international financial indices, such as MSCI World and MSCI Emerging Markets. We compute the value at risk, expected shortfall (ES), and range value at risk (RVaR) and investigate the dynamics of risk spillover. We employ a hybrid approach to derive these risk measures that integrate GARCH models, extreme value models, and copula functions. This framework uses a bivariate portfolio approach involving cryptocurrency data and traditional financial indices. To estimate the above risks of these portfolio structures, we employ symmetric and asymmetric GARCH and both tail flexible EVT models as marginal to model the marginal distribution of each return series and apply different copula functions to connect the pairs of marginal distributions into a multivariate distribution. The empirical findings indicate that the eGARCH EVT-based copula model adeptly captures intricate dependencies, surpassing conventional methodologies like Historical simulations and t-distributed parametric in VaR estimation. At the same time, the HS method proves superior for ES, and the t-distributed parametric method outperforms RVaR. Eventually, the Diebold-Yilmaz approach will be applied to compute risk spillovers between four sets of asset sequences. This phenomenon implies that cryptocurrencies reveal substantial spillover effects among themselves but minimal impact on other assets. From this, it can be concluded that cryptocurrencies propose diversification benefits and do not provide hedging advantages within an investor's portfolio. Our results underline RVaR superiority over ES regarding regulatory arbitrage and model misspecification. The conclusions of this study will benefit investors and financial market professionals who aspire to comprehend digital currencies as a novel asset class and attain perspicuity in regulatory arbitrage. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.15766 |
| By: | Natalia Roszyk; Robert \'Slepaczuk |
| Abstract: | Predicting the S&P 500 index volatility is crucial for investors and financial analysts as it helps assess market risk and make informed investment decisions. Volatility represents the level of uncertainty or risk related to the size of changes in a security's value, making it an essential indicator for financial planning. This study explores four methods to improve the accuracy of volatility forecasts for the S&P 500: the established GARCH model, known for capturing historical volatility patterns; an LSTM network that utilizes past volatility and log returns; a hybrid LSTM-GARCH model that combines the strengths of both approaches; and an advanced version of the hybrid model that also factors in the VIX index to gauge market sentiment. This analysis is based on a daily dataset that includes S&P 500 and VIX index data, covering the period from January 3, 2000, to December 21, 2023. Through rigorous testing and comparison, we found that machine learning approaches, particularly the hybrid LSTM models, significantly outperform the traditional GARCH model. Including the VIX index in the hybrid model further enhances its forecasting ability by incorporating real-time market sentiment. The results of this study offer valuable insights for achieving more accurate volatility predictions, enabling better risk management and strategic investment decisions in the volatile environment of the S&P 500. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.16780 |
| By: | Couaillier, Cyril; Scalone, Valerio |
| Abstract: | In this paper, we propose a new framework to jointly calibrate cyclical and structural capital requirements. For this, we integrate a non-linear macroeconomic model and a stress test model. In the macroeconomic model, the severity of the scenarios depends on the level of cyclical risk. Risk-related scenarios are used as inputs for the stress test model. Banks’ capital losses derived from a scenario based on a reference level of risk are used to set the structural requirement. Additional losses associated with the current risk scenario are used to set the cyclical requirement. This approach provides a transparent method to strike the balance between cyclical and structural requirements. JEL Classification: C32, E51, E58, G01 |
| Keywords: | capital requirements, financial vulnerability, macroprudential policy, non-linear models |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20242966 |
| By: | Abdulnasser Hatemi-J |
| Abstract: | Reducing financial risk is of paramount importance to investors, financial institutions, and corporations. Since the pioneering contribution of Johnson (1960), the optimal hedge ratio based on futures is regularly utilized. The current paper suggests an explicit and efficient method for testing the null hypothesis of a symmetric optimal hedge ratio against an asymmetric alternative one within a multivariate setting. If the null is rejected, the position dependent optimal hedge ratios can be estimated via the suggested model. This approach is expected to enhance the accuracy of the implemented hedging strategies compared to the standard methods since it accounts for the fact that the source of risk depends on whether the investor is a buyer or a seller of the risky asset. An application is provided using spot and futures prices of Bitcoin. The results strongly support the view that the optimal hedge ratio for this cryptocurrency is position dependent. The investor that is long in Bitcoin has a much higher conditional optimal hedge ratio compared to the one that is short in the asset. The difference between the two conditional optimal hedge ratios is statistically significant, which has important repercussions for implementing risk management strategies. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.19932 |
| By: | Sourish Das |
| Abstract: | In this work, we present an alternative passive investment strategy. The passive investment philosophy comes from the Efficient Market Hypothesis (EMH), and its adoption is widespread. If EMH is true, one cannot outperform market by actively managing their portfolio for a long time. Also, it requires little to no intervention. People can buy an exchange-traded fund (ETF) with a long-term perspective. As the economy grows over time, one expects the ETF to grow. For example, in India, one can invest in NETF, which suppose to mimic the Nifty50 return. However, the weights of the Nifty 50 index are based on market capitalisation. These weights are not necessarily optimal for the investor. In this work, we present that volatility risk and extreme risk measures of the Nifty50 portfolio are uniformly larger than Markowitz's optimal portfolio. However, common people can't create an optimised portfolio. So we proposed an alternative passive investment strategy of an equal-weight portfolio. We show that if one pushes the maximum weight of the portfolio towards equal weight, the idiosyncratic risk of the portfolio would be minimal. The empirical evidence indicates that the risk profile of an equal-weight portfolio is similar to that of Markowitz's optimal portfolio. Hence instead of buying Nifty50 ETFs, one should equally invest in the stocks of Nifty50 to achieve a uniformly better risk profile than the Nifty 50 ETF portfolio. We also present an analysis of how portfolios perform to idiosyncratic events like the Russian invasion of Ukraine. We found that the equal weight portfolio has a uniformly lower risk than the Nifty 50 portfolio before and during the Russia-Ukraine war. All codes are available on GitHub (\url{https://github.com/sourish-cmi/qua nt/tree/main/Chap_Risk_Anal_of_Passive_P ortfolio}). |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.08332 |
| By: | Marcelo Righi; Eduardo Horta; Marlon Moresco |
| Abstract: | We introduce the concept of set risk measures (SRMs), which are real-valued maps defined on the space of all non-empty, closed, and bounded sets of almost surely bounded random variables. Traditional risk measures typically operate on spaces of random variables, but SRMs extend this framework to sets of random variables. We establish an axiom scheme for SRMs, similar to classical risk measures but adapted for set operations. The main technical contribution is an axiomatic dual representation of convex SRMs by using regular, finitely additive measures on the unit ball of the dual space of essentially bounded random variables. We explore worst-case SRMs, which evaluate risk as the supremum of individual risks within a set, and provide a collection of examples illustrating the applicability of our framework to systemic risk, portfolio optimization, and decision-making under uncertainty. This work extends the theory of risk measures to a more general and flexible setup, accommodating a broader range of financial and mathematical applications. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.18687 |
| By: | St\'ephane Cr\'epey (UFR Math\'ematiques UPCit\'e); Botao Li (LPSM); Hoang Nguyen (IES, LPSM); Bouazza Saadeddine |
| Abstract: | We present a unified framework for computing CVA sensitivities, hedging the CVA, and assessing CVA risk, using probabilistic machine learning meant as refined regression tools on simulated data, validatable by low-cost companion Monte Carlo procedures. Various notions of sensitivities are introduced and benchmarked numerically. We identify the sensitivities representing the best practical trade-offs in downstream tasks including CVA hedging and risk assessment. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.18583 |
| By: | Jacob Boudoukh; Yukun Liu; Tobias J. Moskowitz; Matthew P. Richardson |
| Abstract: | We characterize how risk evolves during a crisis. Using high-frequency data, we find that the first two principal components (PCs) of the covariance matrix of global asset returns experience large, sudden, and temporary spikes coinciding with well-known crises – Covid-19 pandemic, Global Financial Crisis, and Brexit. Despite the origin of these crises being very different, the risk dynamics share remarkably common features: PC1 shocks come solely from asset volatility, while PC2 shocks come from changing loadings/composition, effectively making it a “crisis” factor. Using the exogenous nature of Covid-19, we provide novel identification of risk dynamics by linking these changes to news about the virus and epidemiological model forecast errors over time and across countries. We conclude with investment implications, where shocks to systematic risk sharply reduce diversification benefits and ex ante attempts to hedge it are futile, which may be a defining characteristic of a crisis – that it is unavoidable. |
| JEL: | F3 F30 G01 G1 G10 G11 G12 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32693 |
| By: | OECD |
| Abstract: | The OECD AI Principles call for AI actors to be accountable for the proper functioning of their AI systems in accordance with their role, context, and ability to act. Likewise, the OECD Guidelines for Multinational Enterprises aim to minimise adverse impacts that may be associated with an enterprise’s operations, products and services. To develop ‘trustworthy’ and ‘responsible’ AI systems, there is a need to identify and manage AI risks. As calls for the development of accountability mechanisms and risk management frameworks continue to grow, interoperability would enhance efficiency and reduce enforcement and compliance costs. This report provides an analysis of the commonalities of AI risk management frameworks. It demonstrates that, while some elements may sometimes differ, all the risk management frameworks analysed follow a similar and sometimes functionally equivalent risk management process. |
| Keywords: | AI, AI interoperability, artificial intelligence, OECD AI Principles, OECD Guidelines for Multinational Enterprises, responsible AI, trustworthy AI |
| Date: | 2023–11–07 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:5-en |
| By: | Berg, Tobias; Heider, Florian |
| Abstract: | This paper examines the dynamic relationship between firm leverage and risktaking. We embed the traditional agency problem of asset substitution within a multi-period model, revealing a U-shaped relationship between leverage and risktaking, evident in data from both the U.S. and Europe. Firms with medium leverage avoid risk to preserve the option of issuing safe debt in the future. This option is valuable because safe debt does not incur the expected cost of bankruptcy, anticipated by debt-holders due to future risk-taking incentives. Our model offers new insights on the interaction between companies' debt financing and their risk profiles. |
| Keywords: | leverage, risk-taking incentives, dynamic model |
| JEL: | G3 G31 G32 G33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:safewp:300645 |
| By: | Benjamin Knox; Jakob Ahm Sørensen |
| Abstract: | We develop a theory that connects insurance prices, insurance companies’ investment behavior, and equilibrium asset prices. Consistent with the model’s predictions, we show empirically that (1) insurers with more stable insurance funding take more investment risk and, therefore, earn higher average investment returns; (2) insurers set lower prices on policies when expected investment returns are higher, both in the cross-section of insurance companies and in the time series. Our results hold for both life insurance and property and casualty insurance companies. The findings show that insurers’ asset allocation and product pricing decisions are more connected than previously thought. |
| Keywords: | Insurance pricing; Portfolio choice; Corporate bonds |
| JEL: | G11 G22 G12 |
| Date: | 2024–07–19 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2024-58 |
| By: | Mario Ghossoub; Qinghua Ren; Ruodu Wang |
| Abstract: | In risk-sharing markets with aggregate uncertainty, characterizing Pareto-optimal allocations when agents might not be risk averse is a challenging task, and the literature has only provided limited explicit results thus far. In particular, Pareto optima in such a setting may not necessarily be comonotonic, in contrast to the case of risk-averse agents. In fact, when market participants are risk-seeking, Pareto-optimal allocations are counter-monotonic. Counter-monotonicity of Pareto optima also arises in some situations for quantile-optimizing agents. In this paper, we provide a systematic study of efficient risk sharing in markets where allocations are constrained to be counter-monotonic. The preferences of the agents are modelled by a common distortion risk measure, or equivalently, by a common Yaari dual utility. We consider three different settings: risk-averse agents, risk-seeking agents, and those with an inverse S-shaped distortion function. In each case, we provide useful characterizations of optimal allocations, for both the counter-monotonic market and the unconstrained market. To illustrate our results, we consider an application to a portfolio choice problem for a portfolio manager tasked with managing the investments of a group of clients, with varying levels of risk aversion or risk seeking. We determine explicitly the optimal investment strategies in this case. Our results confirm the intuition that a manager investing on behalf of risk-seeking agents tends to invest more in risky assets than a manager acting on behalf of risk-averse agents. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.16099 |
| By: | Tiago Monteiro |
| Abstract: | In quantitative finance, machine learning methods are essential for alpha generation. This study introduces a new approach that combines Hidden Markov Models (HMM) and neural networks, integrated with Black-Litterman portfolio optimization. During the COVID period (2019-2022), this dual-model approach achieved a 83% return with a Sharpe ratio of 0.77. It incorporates two risk models to enhance risk management, showing efficiency during volatile periods. The methodology was implemented on the QuantConnect platform, which was chosen for its robust framework and experimental reproducibility. The system, which predicts future price movements, includes a three-year warm-up to ensure proper algorithm function. It targets highly liquid, large-cap energy stocks to ensure stable and predictable performance while also considering broker payments. The dual-model alpha system utilizes log returns to select the optimal state based on the historical performance. It combines state predictions with neural network outputs, which are based on historical data, to generate trading signals. This study examined the architecture of the trading system, data pre-processing, training, and performance. The full code and backtesting data are available under the QuantConnect terms. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.19858 |
| By: | Bertolozzi-Caredio, Daniele; Soriano, Barbara; Urquhart, Julie; Vigani, Mauro |
| Abstract: | Understanding how farmers learn and how this influences their decisions is still a key question in research, especially in the context of increasing challenges and uncertainties. We explore whether and how different learning preferences, notably learning by doing, from other farmers and through social media, influence farmers’ risk management (RM) choices. Based on a survey of farmers in Spain and the UK, we employed multivariate probit regressions and Poisson models with instrument variables. We found that all learning preferences are significantly correlated to RM choice, with learning through social media and from peers leading to more strategies adopted by the farmer, and learning by doing leading to fewer strategies. The results, however, show that each learning preference affects different specific RM strategies. Our findings suggest that policymakers should consider leveraging informal learning networks to improve farmers’ RM, whereas policy incentives might be designed to formalize and promote social media use (also by existing extension services) to boost the adoption of RM strategies. |
| Keywords: | Farm Management, Risk and Uncertainty |
| Date: | 2024–07–26 |
| URL: | https://d.repec.org/n?u=RePEc:ags:cfcp15:344372 |
| By: | Arnone, Massimo; Laureti, Lucio; Costantiello, Alberto; Anobile, Fabio; Leogrande, Angelo |
| Abstract: | This paper explores the critical role of the banking sector in facilitating the ecological transition through the integration of ESG (Environmental, Social, and Governance) factors. By analyzing the intersection of ESG considerations and access to credit globally, the study highlights how banks can catalyze sustainable investments while balancing financial risks. Using a systematic literature review and k-Means clustering analysis, we assess the global landscape of credit access, emphasizing the implications of ESG adoption on financial stability and economic growth. The findings suggest that while ESG integration presents challenges, it offers significant opportunities for banks to enhance their competitiveness and foster resilient financial systems. The paper concludes by proposing policy recommendations aimed at improving the incorporation of ESG factors within credit risk management and promoting sustainable finance. |
| Date: | 2024–07–31 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:aetb9 |
| By: | Ali, Waqar (HEC Paris) |
| Abstract: | Using ASU 2017-12, which substantially simplified reporting of hedging activities, I compare debt contracting outcomes from public bond issuers’ and private loan borrowers’ implementations of less complex hedge accounting. Bond issuers lower credit spreads by 13 – 22 basis points through effective hedging and reporting under the ASU. In contrast, private loan borrowers face 11 basis points higher loan pricing, and 50% greater balance-sheet covenants post-ASU adoption. I argue that when the ASU removes risk-relevant reporting requirements, information frictions and thus the agency cost of private debt increase for banks, and hedging outcomes from private borrowers’ ASU adoptions are insufficient towards offsetting the increased agency cost of debt. Bondholders and private lenders price the informational content of simplified hedge accounting rules differently even when borrowers’ real hedging outcomes remain unchanged. I extend the literature by showing that complex accounting rules have opposite effects on institutionally different public and private debt markets. |
| Keywords: | Reporting Complexity; Hedge accounting; Financial risk management; Debt contracting; Public debt; Private debt; ASU 2017-12 |
| JEL: | D82 G21 G32 M41 |
| Date: | 2023–11–01 |
| URL: | https://d.repec.org/n?u=RePEc:ebg:heccah:1494 |
| By: | OECD |
| Abstract: | Artificial intelligence (AI) offers tremendous benefits but also poses risks. Some of these risks have materialised into what are known as “AI incidents”. Due to the widespread use of AI in various sectors, a surge in such incidents can be expected. To effectively monitor and prevent these risks, stakeholders need a precise yet adaptable definition of AI incidents. This report presents research and findings on terminology and practices related to incident definitions, encompassing both AI-specific and cross-disciplinary contexts. It establishes a knowledge base for identifying commonalities and encouraging the development of AI-specific adaptations in the future. |
| Keywords: | Artificial intelligence, AI incident, AI risks |
| Date: | 2023–10–27 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:4-en |