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on Risk Management |
| By: | Parley R Yang; Alexander Y Shestopaloff |
| Abstract: | We employ a Bayesian modelling technique for high dimensional cointegration estimation to construct low volatility portfolios from a large number of stocks. The proposed Bayesian framework effectively identifies sparse and important cointegration relationships amongst large baskets of stocks across various asset spaces, resulting in portfolios with reduced volatility. Such cointegration relationships persist well over the out-of-sample testing time, providing practical benefits in portfolio construction and optimization. Further studies on drawdown and volatility minimization also highlight the benefits of including cointegrated portfolios as risk management instruments. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.10175 |
| By: | Ravi Kashyap |
| Abstract: | We engineer blockchain based risk managed portfolios by creating three funds with distinct risk and return profiles: 1) Alpha - high risk portfolio; 2) Beta - mimics the wider market; and 3) Gamma - represents the risk free rate adjusted to beat inflation. Each of the sub-funds (Alpha, Beta and Gamma) provides risk parity because the weight of each asset in the corresponding portfolio is set to be inversely proportional to the risk derived from investing in that asset. This can be equivalently stated as equal risk contributions from each asset towards the overall portfolio risk. We provide detailed mechanics of combining assets - including mathematical formulations - to obtain better risk managed portfolios. The descriptions are intended to show how a risk parity based efficient frontier portfolio management engine - that caters to different risk appetites of investors by letting each individual investor select their preferred risk-return combination - can be created seamlessly on blockchain. Any Investor - using decentralized ledger technology - can select their desired level of risk, or return, and allocate their wealth accordingly among the sub funds, which balance one another under different market conditions. This evolution of the risk parity principle - resulting in a mechanism that is geared to do well under all market cycles - brings more robust performance and can be termed as conceptual parity. We have given several numerical examples that illustrate the various scenarios that arise when combining Alpha, Beta and Gamma to obtain Parity. The final investment frontier is now possible - a modification to the efficient frontier, thus becoming more than a mere theoretical construct - on blockchain since anyone from anywhere can participate at anytime to obtain wealth appreciation based on their financial goals. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.09536 |
| By: | Paolo Bartesaghi; Fernando Diaz-Diaz; Rosanna Grassi; Pierpaolo Uberti |
| Abstract: | We show that the global balance index of financial correlation networks can be used as a systemic risk measure. We define the global balance of a network starting from a diffusive process that describes how the information spreads across nodes in a network, providing an alternative derivation to the usual combinatorial one. The steady state of this process is the solution of a linear system governed by the exponential of the replication matrix of the process. We provide a bridge between the numerical stability of this linear system, measured by the condition number in an opportune norm, and the structural predictability of the underlying signed network. The link between the condition number and related systemic risk measures, such as the market rank indicators, allows the global balance index to be interpreted as a new systemic risk measure. A comprehensive empirical application to real financial data finally confirms that the global balance index of the financial correlation network represents a valuable and effective systemic risk indicator. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.14272 |
| By: | Pascal Fran\c{c}ois; Genevi\`eve Gauthier; Fr\'ed\'eric Godin; Carlos Octavio P\'erez Mendoza |
| Abstract: | The recent work of Horikawa and Nakagawa (2024) claims that under a complete market admitting statistical arbitrage, the difference between the hedging position provided by deep hedging and that of the replicating portfolio is a statistical arbitrage. This raises concerns as it entails that deep hedging can include a speculative component aimed simply at exploiting the structure of the risk measure guiding the hedging optimisation problem. We test whether such finding remains true in a GARCH-based market model. We observe that the difference between deep hedging and delta hedging can be a statistical arbitrage if the risk measure considered does not put sufficient relative weight on adverse outcomes. Nevertheless, a suitable choice of risk measure can prevent the deep hedging agent from including a speculative overlay within its hedging strategy. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.14736 |
| By: | \c{C}a\u{g}{\i}n Ararat; Zachary Feinstein |
| Abstract: | Risk measures for random vectors have been considered in multi-asset markets with transaction costs and financial networks in the literature. While the theory of set-valued risk measures provide an axiomatic framework for assigning to a random vector its set of all capital requirements or allocation vectors, the actual decision-making process requires an additional rule to select from this set. In this paper, we define vector-valued risk measures by an analogous list of axioms and show that, in the convex and lower semicontinuous case, such functionals always ignore the dependence structures of the input random vectors. We also show that set-valued risk measures do not have this issue as long as they do not reduce to a vector-valued functional. Finally, we demonstrate that our results also generalize to the conditional setting. These results imply that convex vector-valued risk measures are not suitable for defining capital allocation rules for a wide range of financial applications including systemic risk measures. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.16878 |
| By: | Liyang Wang; Yu Cheng; Xingxin Gu; Zhizhong Wu |
| Abstract: | With the increasing complexity of financial markets and rapid growth in data volume, traditional risk monitoring methods no longer suffice for modern financial institutions. This paper designs and optimizes a risk monitoring system based on big data and machine learning. By constructing a four-layer architecture, it effectively integrates large-scale financial data and advanced machine learning algorithms. Key technologies employed in the system include Long Short-Term Memory (LSTM) networks, Random Forest, Gradient Boosting Trees, and real-time data processing platform Apache Flink, ensuring the real-time and accurate nature of risk monitoring. Research findings demonstrate that the system significantly enhances efficiency and accuracy in risk management, particularly excelling in identifying and warning against market crash risks. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.19352 |
| By: | Yannick Becker; Pascal Halffmann; Anita Sch\"obel |
| Abstract: | In portfolio optimization, decision makers face difficulties from uncertainties inherent in real-world scenarios. These uncertainties significantly influence portfolio outcomes in both classical and multi-objective Markowitz models. To address these challenges, our research explores the power of robust multi-objective optimization. Since portfolio managers frequently measure their solutions against benchmarks, we enhance the multi-objective min-regret robustness concept by incorporating these benchmark comparisons. This approach bridges the gap between theoretical models and real-world investment scenarios, offering portfolio managers more reliable and adaptable strategies for navigating market uncertainties. Our framework provides a more nuanced and practical approach to portfolio optimization under real-world conditions. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.19936 |
| By: | Maciej Wysocki; Robert \'Slepaczuk |
| Abstract: | This research presents a comprehensive evaluation of systematic index option-writing strategies, focusing on S&P500 index options. We compare the performance of hedging strategies using the Black-Scholes-Merton (BSM) model and the Variance-Gamma (VG) model, emphasizing varying moneyness levels and different sizing methods based on delta and the VIX Index. The study employs 1-minute data of S&P500 index options and index quotes spanning from 2018 to 2023. The analysis benchmarks hedged strategies against buy-and-hold and naked option-writing strategies, with a focus on risk-adjusted performance metrics including transaction costs. Portfolio delta approximations are derived using implied volatility for the BSM model and market-calibrated parameters for the VG model. Key findings reveal that systematic option-writing strategies can potentially yield superior returns compared to buy-and-hold benchmarks. The BSM model generally provided better hedging outcomes than the VG model, although the VG model showed profitability in certain naked strategies as a tool for position sizing. In terms of rehedging frequency, we found that intraday hedging in 130-minute intervals provided both reliable protection against adverse market movements and a satisfactory returns profile. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.13908 |
| By: | Aditya Saxena; Dr Parizad Dungore |
| Abstract: | Credit ratings are becoming one of the primary references for financial institutions of the country to assess credit risk in order to accurately predict the likelihood of business failure of an individual or an enterprise. Financial institutions, therefore, depend on credit rating tools and services to help them predict the ability of creditors to meet financial persuasions. Conventional credit rating is broadly categorized into two classes namely: good credit and bad credit. This approach lacks adequate precision to perform credit risk analysis in practice. Related studies have shown that data-driven machine learning algorithms outperform many conventional statistical approaches in solving this type of problem, both in terms of accuracy and efficiency. The purpose of this paper is to construct and validate a credit risk assessment model using Linear Discriminant Analysis as a dimensionality reduction technique to discriminate good creditors from bad ones and identify the best classifier for credit assessment of commercial banks based on real-world data. This will help commercial banks to avoid monetary losses and prevent financial crisis |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.12044 |
| By: | Hurlin, Christophe (University of Orleans); Pérignon, Christophe (HEC Paris) |
| Abstract: | This survey proposes a theoretical and practical reflection on the use of machine learning methods in the context of the Internal Ratings Based (IRB) approach to banks' capital requirements. While machine learning is still rarely used in the regulatory domain (IRB, IFRS 9, stress tests), recent discussions initiated by the European Banking Authority suggest that this may change in the near future. While technically complex, this subject is crucial given growing concerns about the potential financial instability caused by the banks' use of opaque internal models. Conversely, for their proponents, machine learning models offer the prospect of better measurement of credit risk and enhancing financial inclusion. This survey yields several conclusions and recommendations regarding (i) the accuracy of risk parameter estimations, (ii) the level of regulatory capital, (iii) the trade-off between performance and interpretability, (iv) international banking competition, and (v) the governance and operational risks of machine learning models. |
| Keywords: | Banking; Machine Learning; Artificial Intelligence; Internal models; Prudential regulation; Regulatory capital |
| JEL: | C10 C38 C55 G21 G29 |
| Date: | 2023–06–25 |
| URL: | https://d.repec.org/n?u=RePEc:ebg:heccah:1480 |
| By: | Chunhui Qiao; Xiangwei Wan |
| Abstract: | This paper proposes a deep delta hedging framework for options, utilizing neural networks to learn the residuals between the hedging function and the implied Black-Scholes delta. This approach leverages the smoother properties of these residuals, enhancing deep learning performance. Utilizing ten years of daily S&P 500 index option data, our empirical analysis demonstrates that learning the residuals, using the mean squared one-step hedging error as the loss function, significantly improves hedging performance over directly learning the hedging function, often by more than 100%. Adding input features when learning the residuals enhances hedging performance more for puts than calls, with market sentiment being less crucial. Furthermore, learning the residuals with three years of data matches the hedging performance of directly learning with ten years of data, proving that our method demands less data. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.19367 |
| By: | Elie Bouri (Adnan Kassar School of Business, Lebanese American University, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany.); Onur Polat (Department of Public Finance, Bilecik Seyh Edebali University, Bilecik, Turkiye) |
| Abstract: | Using monthly data from 1871 to 2024 and logistic models with shrinkage estimators, we compare the contribution of stock and oil-market moments (returns, volatility, skewness, and kurtosis) to the accuracy of out-of-sample forecasts of U.S. recessions at various forecast horizons, while controling for various standard macroeconomic predictors and the total connectedness indexes of the moments. Adding stock-market moments to the potential predictors improves significantly the accuracy of out-of-sample forecasts at the long forecast horizon, whereas oil-market moments and connectedness indexes do not contribute much. The lagged recession dummy, the term spread, and stock returns are found to be the top predictors of recessions. |
| Keywords: | Recessions, Stock-market and oil-market moments, Forecasting, Shrinkage estimators, AUC statistics |
| JEL: | C53 E32 E37 G17 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202435 |
| By: | Hamdi Ben-Nasr (Qatar University); Sabri Boubaker (VNU - Vietnam National University [Hanoï], Métis Lab EM Normandie - EM Normandie - École de Management de Normandie, Swansea University) |
| Abstract: | We add to the literature on the economic outcomes of government debt and argue that government debt increases crash risk via two channels: (i) hoarding bad news and (ii) tax avoidance. Based on a large international sample, our results indicate that stock crash risk is positively associated with government debt. Our conclusions are robust when we treat endogeneity issues, and our tests confirm the validity of bad news hoarding and tax avoidance as channels through which government debt influences stock price crash risk. |
| Keywords: | Government debt, Fiscal policy uncertainty, Bad news hoarding, Tax avoidance, Crash risk |
| Date: | 2024–02–16 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04648524 |
| By: | Dangxing Chen; Yuan Gao |
| Abstract: | Over the past few decades, machine learning models have been extremely successful. As a result of axiomatic attribution methods, feature contributions have been explained more clearly and rigorously. There are, however, few studies that have examined domain knowledge in conjunction with the axioms. In this study, we examine asset pricing in finance, a field closely related to risk management. Consequently, when applying machine learning models, we must ensure that the attribution methods reflect the underlying risks accurately. In this work, we present and study several axioms derived from asset pricing domain knowledge. It is shown that while Shapley value and Integrated Gradients preserve most axioms, neither can satisfy all axioms. Using extensive analytical and empirical examples, we demonstrate how attribution methods can reflect risks and when they should not be used. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.08953 |
| By: | Feinstein, Zachary; Hałaj, Grzegorz; Søjmark, Andreas |
| Abstract: | We build a balance sheet-based model to capture run risk, i.e., a reduced potential to raise capital from liquidity buffers under stress, driven by depositor scrutiny and further fuelled by fire sales in response to withdrawals. The setup is inspired by the Silicon Valley Bank (SVB) meltdown in March 2023 and our model may serve as a supervisory analysis tool to monitor build-up of balance sheet vulnerabilities. Specifically, we analyze which characteristics of the balance sheet are critical in order for banking system regulators to adequately assess run risk and resilience. By bringing a time series of SVB’s balance sheet data to our model, we are able to demonstrate how changes in the funding and respective asset composition made SVB prone to run risk, as they were increasingly relying on heldto-maturity, aka hidden-to-maturity, accounting standards, masking revaluation losses in securities portfolios. Finally, we formulate a tractable optimisation problem to address the designation of heldto-maturity assets and quantify banks’ ability to hold these assets without resorting to remarking. By calibrating this to SVB’s balance sheet data, we shed light on the bank’s funding risk and impliedrisk tolerance in the years 2020–22 leading up to its collapse. JEL Classification: C62, G21, G11 |
| Keywords: | accounting standards, bank runs, fire sales, funding risk |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20242970 |
| By: | Xiaobai Zhu; Kenneth Q. Zhou; Zijia Wang |
| Abstract: | The significance of mortality modeling extends across multiple research areas, including life insurance valuation, longevity risk management, life-cycle hypothesis, and retirement income planning. Despite the variety of existing approaches, such as mortality laws and factor-based models, they often lack compatibility or fail to meet specific research needs. To address these shortcomings, this study introduces a novel approach centered on modeling the dynamics of individual vitality and defining mortality as the depletion of vitality level to zero. More specifically, we develop a four-component framework to analyze the initial value, trend, diffusion, and sudden changes in vitality level over an individual's lifetime. We demonstrate the framework's estimation and analytical capabilities in various settings and discuss its practical implications in actuarial problems and other research areas. The broad applicability and interpretability of our vitality-based modeling approach offer an enhanced paradigm for mortality modeling. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.15388 |
| By: | Felix Fie{\ss}inger; Mitja Stadje |
| Abstract: | This paper studies the equity holders' mean-variance optimal portfolio choice problem for (non-)protected participating life insurance contracts. We derive explicit formulas for the optimal terminal wealth and the optimal strategy in the multi-dimensional Black-Scholes model, showing the existence of all necessary parameters. In incomplete markets, we state Hamilton-Jacobi-Bellman equations for the value function. Moreover, we provide a numerical analysis of the Black-Scholes market. The equity holders on average increase their investment into the risky asset in bad economic states and decrease their investment over time. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.11761 |
| By: | Nacira Agram; Bernt {\O}ksendal; Jan Rems |
| Abstract: | We propose a deep learning approach to study the minimal variance pricing and hedging problem in an incomplete jump diffusion market. It is based upon a rigorous stochastic calculus derivation of the optimal hedging portfolio, optimal option price, and the corresponding equivalent martingale measure through the means of the Stackelberg game approach. A deep learning algorithm based on the combination of the feedforward and LSTM neural networks is tested on three different market models, two of which are incomplete. In contrast, the complete market Black-Scholes model serves as a benchmark for the algorithm's performance. The results that indicate the algorithm's good performance are presented and discussed. In particular, we apply our results to the special incomplete market model studied by Merton and give a detailed comparison between our results based on the minimal variance principle and the results obtained by Merton based on a different pricing principle. Using deep learning, we find that the minimal variance principle leads to typically higher option prices than those deduced from the Merton principle. On the other hand, the minimal variance principle leads to lower losses than the Merton principle. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.13688 |
| By: | Brignone, Davide; Gambetti, Luca; Ricci, Martino |
| Abstract: | In this paper, we investigate the presence of non-linearities in the transmission of geopolitical risk (GPR) shocks. Our methodology involves incorporating a non-linear function of the identified shock into a VARX model and examining its impulse response functions and historical decomposition. We find that the primary transmission channel of such shocks is associated with heightened uncertainty, which significantly escalates only with substantially large GPR shocks (i.e., above 4 standard deviations). This increase in uncertainty prompts precautionary saving behaviors, exerting a strong impact on consumption and reducing activity. The response of inflation is more subdued, reflecting both diminished demand and heightened uncertainty, which influence prices in opposing directions. JEL Classification: C30, D80, E32, F44, H56 |
| Keywords: | economic activity, geopolitical risk, inflation, uncertainty, vector autoregressions |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20242972 |
| By: | Vania Stavrakeva; Jenny Tang |
| Abstract: | In this paper, we study how the volatility of both realized and expected macroeconomic variables relates to the variation in exchange rate volatility through the prism of the Great Moderation hypothesis. We find significant heterogeneity in exchange rate trend volatility across currency pairs despite decreases in the volatility of expected future interest rate differentials and of realized yields themselves. We argue that time variation in the relationship between macroeconomic variables and exchange rates has prevented the Great Moderation in realized yield volatility from translating to a decrease in exchange rate volatility. Considering a Campbell‐Shiller‐type decomposition of exchange rate changes into forward‐looking components linked to inflation, policy rate, and currency risk premia expectations, we find that the Great Moderation in volatility of expected yield differentials cannot explain the patterns in exchange rate volatility we observe. The main drivers of these patterns were trends in the volatility of the currency risk premium component and in the covariance between the components capturing the strength of the Fama puzzle and the expected responsiveness of monetary policy to inflation. |
| Keywords: | exchange rates; international finance; volatility trends; risk premia; Fama puzzle |
| JEL: | E44 F31 G15 |
| Date: | 2024–02–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbwp:98625 |
| By: | António Afonso; José Alves; Sofia Monteiro |
| Abstract: | Recognizing the profound influence of geopolitical risks and world uncertainty on financial investment behaviour, this study uses a comprehensive approach to assess the impact of rising geopolitical risk on sovereign debt holdings for a panel of 24 OECD economies from Q1 2004 to Q4 2023. To do so, we employ Ordinary Least Squares (OLS) fixed effects and Quantile Regression techniques within a panel data framework to capture the nuanced effects on both domestic and foreign entities. We find that escalating geopolitical tension decreases government debt holdings among domestic entities, notably domestic Banks, while foreign investors increase their ownership. This phenomenon is more pronounced for high proportion levels of debt in investor’s portfolios. Our results allow us to conclude that while domestic economic agents display clearer risk aversion, foreign economic agents have a more risk-taking behaviour in what concerns the financial investment on government debt. |
| Keywords: | sovereign debt, geopolitical risk, world uncertainty, OLS, quantile regression |
| JEL: | C23 E44 G32 H63 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11235 |
| By: | Herv\'e Andr\`es (CERMICS); Benjamin Jourdain (CERMICS, MATHRISK) |
| Abstract: | We show the existence and uniqueness of a continuous solution to a path-dependent volatility model introduced by Guyon and Lekeufack (2023) to model the price of an equity index and its spot volatility. The considered model for the trend and activity features can be written as a Stochastic Volterra Equation (SVE) with non-convolutional and non-bounded kernels as well as non-Lipschitz coefficients. We first prove the existence and uniqueness of a solution to the SVE under integrability and regularity assumptions on the two kernels and under a condition on the second kernel weighting the past squared returns which ensures that the activity feature is bounded from below by a positive constant. Then, assuming in addition that the kernel weighting the past returns is of exponential type and that an inequality relating the logarithmic derivatives of the two kernels with respect to their second variables is satisfied, we show the positivity of the volatility process which is obtained as a non-linear function of the SVE's solution. We show numerically that the choice of an exponential kernel for the kernel weighting the past returns has little impact on the quality of model calibration compared to other choices and the inequality involving the logarithmic derivatives is satisfied by the calibrated kernels. These results extend those of Nutz and Valdevenito (2023). |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.02477 |
| By: | Digvijay S. Negi (Ashoka University); Christopher B. Barrett (Charles H. Dyson School of Applied Economics and Management & Cornell University) |
| Abstract: | How do low-income, rural households smooth consumption in the face of seasonal and stochastic variation in income when household access to formal financial services is limited? And how do consumption smoothing modes evolve in response to changes in transport infrastructure? We explore these questions by studying milk consumption smoothing for a panel of households from rural India. Household milk consumption is highly but incompletely smoothed relative to intertemporal variation in household milk production. Informal inter-household transfers provide only modest quasi-insurance. Mainly, households smooth consumption through milk market transactions. And as new roads reach villages, markets become even more important mechanisms for consumption smoothing, especially in high productivity seasons. These patterns underscore the central importance of product market participation for risk management in low-income rural communities. |
| Keywords: | insurance; market participation; ndia; risk sharing; transactions costs |
| Date: | 2024–07–15 |
| URL: | https://d.repec.org/n?u=RePEc:ash:wpaper:116 |
| By: | Yuxin Liu; Jimin Lin; Achintya Gopal |
| Abstract: | Traditional approaches to estimating beta in finance often involve rigid assumptions and fail to adequately capture beta dynamics, limiting their effectiveness in use cases like hedging. To address these limitations, we have developed a novel method using neural networks called NeuralBeta, which is capable of handling both univariate and multivariate scenarios and tracking the dynamic behavior of beta. To address the issue of interpretability, we introduce a new output layer inspired by regularized weighted linear regression, which provides transparency into the model's decision-making process. We conducted extensive experiments on both synthetic and market data, demonstrating NeuralBeta's superior performance compared to benchmark methods across various scenarios, especially instances where beta is highly time-varying, e.g., during regime shifts in the market. This model not only represents an advancement in the field of beta estimation, but also shows potential for applications in other financial contexts that assume linear relationships. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.01387 |