nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒10‒23
nineteen papers chosen by
Stan Miles, Thompson Rivers University

  1. Implementing portfolio risk management and hedging in practice By Paul Alexander Bilokon
  2. Systemic risk in financial networks: the effects of asymptotic independence By Bikramjit Das; Vicky Fasen-Hartmann
  3. Consumption Partial Insurance in the Presence of Tail Income Risk By Ghosh, Anisha; Theloudis, Alexandros
  4. Judging Banks’ Risk by the Profits They Report By Ben S. Meiselman; Stefan Nagel; Amiyatosh Purnanandam
  5. Banks' credit loss forecasts: lessons from supervisory data By Martin Birn; Renzo Corrias; Christian Schmieder; Nikola Tarashev
  6. Volatility or higher moments: Which is more important in return density forecasts of stochastic volatility model? By Li, Chenxing; Zhang, Zehua; Zhao, Ran
  7. Labor Market and Systemic Risk: a network-based approach By Michel Alexandre; Thiago Christiano Silva
  8. Foreign exchange hedging using regime-switching models: the case of pound sterling By Lee, Taehyun; Moutzouris, Ioannis C; Papapostolou, Nikos C; Fatouh, Mahmoud
  9. Banks’ Joint Exposure to Market and Run Risk By Alexander Copestake; Mr. Divya Kirti; Yang Liu
  10. New News is Bad News By Paul Glasserman; Harry Mamaysky; Jimmy Qin
  11. Elicitability and Encompassing for Volatility Forecasts by Bregman Functions By Tae-Hwy Lee; Ekaterina Seregina; Yaojue Xu
  12. VIX Fractal Compression Pattern and Markets Vulnerability: An Interdisciplinary Approach By Romain Bocher
  13. Irreversible Reinsurance: Minimization of Capital Injections in Presence of a Fixed Cost By Federico, Salvatore; Ferrari, Giorgio; Torrente, Maria Laura
  14. Delays in Climate Transition Can Increase Financial Tail Risks: A Global Lesson from a Study in Mexico By Mr. Dimitrios Laliotis; Sujan Lamichhane
  15. Gamma Hedging and Rough Paths By John Armstrong; Andrei Ionescu
  16. Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies By Jakub Micha\'nk\'ow; Pawe{\l} Sakowski; Robert \'Slepaczuk
  17. Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices By Jakub Micha\'nk\'ow; Pawe{\l} Sakowski; Robert \'Slepaczuk
  18. Stock Volatility Prediction Based on Transformer Model Using Mixed-Frequency Data By Wenting Liu; Zhaozhong Gui; Guilin Jiang; Lihua Tang; Lichun Zhou; Wan Leng; Xulong Zhang; Yujiang Liu
  19. Hedge Fund Treasury Exposures, Repo, and Margining By Ayelen Banegas; Phillip J. Monin

  1. By: Paul Alexander Bilokon
    Abstract: In academic literature portfolio risk management and hedging are often versed in the language of stochastic control and Hamilton--Jacobi--Bellman~(HJB) equations in continuous time. In practice the continuous-time framework of stochastic control may be undesirable for various business reasons. In this work we present a straightforward approach for thinking of cross-asset portfolio risk management and hedging, providing some implementation details, while rarely venturing outside the convex optimisation setting of (approximate) quadratic programming~(QP). We pay particular attention to the correspondence between the economic concepts and their mathematical representations; the abstractions enabling us to handle multiple asset classes and risk models at once; the dimensional analysis of the resulting equations; and the assumptions inherent in our derivations. We demonstrate how to solve the resulting QPs with CVXOPT.
    Date: 2023–09
  2. By: Bikramjit Das; Vicky Fasen-Hartmann
    Abstract: Systemic risk measurements are important for the assessment of stability of complex financial systems. Empirical evidence indicates that returns from various financial assets have a heavy-tailed behavior; moreover, such returns often exhibit asymptotic tail independence, i.e., extreme values are less likely to occur simultaneously. Surprisingly, asymptotic tail independence in dimensions larger than two has received limited attention both theoretically, and as well for financial risk modeling. In this paper, we establish the notion of mutual asymptotic tail independence for general $d$-dimensions and compare it with the traditional notion of pairwise asymptotic independence. Furthermore, we consider a financial network model using a bipartite graph of banks and assets with portfolios of possibly overlapping heavy-tailed risky assets exhibiting various asymptotic tail (in)dependence behavior. For such models we provide precise asymptotic expressions for a variety of conditional tail risk probabilities and associated CoVaR measures for assessing systemic risk. We also propose an Extremal CoVaR Index (ECI) for capturing the strength of dependence between risk entities in the network. We focus particularly on two well-known dependence structures to capture risk in any general dimension: Gaussian dependence and Marshall-Olkin dependence, both of which exhibit different levels of asymptotic independence.
    Date: 2023–09
  3. By: Ghosh, Anisha; Theloudis, Alexandros (Tilburg University, School of Economics and Management)
    Date: 2023
  4. By: Ben S. Meiselman; Stefan Nagel; Amiyatosh Purnanandam
    Abstract: In competitive capital markets, risky debt claims that offer high yields in good times have high systematic risk exposure in bad times. We apply this idea to bank risk measurement. We find that banks with high accounting return on equity (ROE) prior to a crisis have higher systematic tail risk exposure during the crisis. Proximate causes of crises differ, but the predictive power of ROE is pervasive, including during the financial crisis of 2007–2010 and the recent crisis triggered by the collapse of Silicon Valley Bank. ROE predicts systematic tail risk much better than conventional measures based on risk-weighted assets.
    JEL: G20 G30
    Date: 2023–08
  5. By: Martin Birn; Renzo Corrias; Christian Schmieder; Nikola Tarashev
    Abstract: Focusing on credit risk, we compare banks' expected loss (EL) rates, collected confidentially by the Basel Committee on Banking Supervision from 2009 to 2022, and the corresponding actual loss (AL) rates, as reported in vendor data. Consistent with the use of through-the-cycle risk estimates for regulatory purposes, EL rates rarely move in line with AL rates over time, which helps explain a large precautionary element in Basel III capital requirements. We also find that the rank-order of EL rates across banks matches closely that of the AL rates, in line with recent and forthcoming regulatory efforts to improve risk-measurement practices. EL rates are more likely to be excessively optimistic on the heels of higher bank profitability and financial overheating, as captured by the credit-to-GDP gap.
    Keywords: expected loss forecasts; regulatory capital; portfolio credit risk
    JEL: G21 G28 G32 G33 E44 P52
    Date: 2023–09
  6. By: Li, Chenxing; Zhang, Zehua; Zhao, Ran
    Abstract: The stochastic volatility (SV) model has been one of the most popular models for latent stock return volatility. Extensions of the SV model focus on either improving volatility inference or modeling higher moments of the return distribution. This study investigates which extension can better improve return density forecasts. By examining various specifications with S&P 500 daily returns for nearly 20 years, we find that a more accurate capture of volatility dynamics with realized volatility and implied volatility is more important than modeling higher moments for a conventional SV model in terms of both density and tail forecasts. The accuracy of volatility estimation and forecasts should be the precondition for higher moments extensions.
    Keywords: Stochastic volatility, realized volatility, implied volatility, MCMC, density forecast
    JEL: C11 C22 C58 G17
    Date: 2023–09–03
  7. By: Michel Alexandre; Thiago Christiano Silva
    Abstract: In this paper, we explore the labor market channel of systemic risk. We consider that distressed firms, besides defaulting on part of their debt commitments, also react to negative shocks by lying off part of their employees. This constitutes another source of systemic risk, as these dismissed employees will not be able to honor their debt commitments. Using Brazilian data, we compute the systemic risk considering three possible strategies adopted by distressed firms: layoff of employees, default on debt commitments, or both strategies. Our findings underscore the significance of the labor market channel, as it has exhibited a noticeable rise in contribution to overall systemic risk in recent months. Moreover, we demonstrate that the amplification of initial shocks is more pronounced through this channel compared to the traditional firms’ loans channel. This study emphasizes the critical role played by the labor market in shaping systemic risk dynamics and calls for enhanced risk management practices to address these challenges effectively.
    Date: 2023–09
  8. By: Lee, Taehyun (Bayes Business School, Faculty of Finance); Moutzouris, Ioannis C (Bayes Business School, Faculty of Finance); Papapostolou, Nikos C (Bayes Business School, Faculty of Finance); Fatouh, Mahmoud (Bank of England)
    Abstract: We develop a four-state regime-switching model for optimal foreign exchange (FX) hedging using forward contracts. The states reflect four possible market conditions, defined by the direction and magnitude of deviation of the prevailing FX spot rate from its long-term trends. The model’s performance is tested for five currencies against pound sterling for various horizons. Our analysis compares the hedging outcomes of the proposed model to those of other frequently used hedging approaches. The empirical results suggest that our model demonstrates the highest level of risk reduction for the US dollar, euro, Japanese yen and Turkish lira and the second-best performance for the Indian rupee. The risk reduction is significantly higher for lira, which suggests that the proposed model might be able to provide much more effective hedging for highly volatile currencies. The improved performance of the model can be attributed to the adjustability of the estimation horizon for the optimal hedge ratio based on the prevailing market conditions. This, in turn, allows it to better capture fat‑tail properties frequently observed in FX returns. Our findings suggest that FX investors tend to use short-term memory (focus more on recent price movements) during low market conditions (relative to trend) and long-term memory in high ones. It would be also useful to build a better understanding of how investor behaviour depends on market conditions and mitigate the adverse behavioural implications of short-term memory, such as panic.
    Keywords: Regime switching; foreign exchange hedging; hedging effectiveness; high‑volatility currencies; forward hedging
    JEL: G13 G15
    Date: 2023–09–22
  9. By: Alexander Copestake; Mr. Divya Kirti; Yang Liu
    Abstract: Recent failures of US banks highlight that large liability withdrawals can damage capital positions—i.e., that liquidity risk and solvency risk interact. A simple risk assessment for banks in a wide group of countries finds sizable exposure to this interaction. This varies significantly across banks—primarily reflecting differences in cash buffers, capitalization, securities holdings and exposure to market risk—and is highly concentrated. Vulnerability is generally greater for banks in AEs due to lower cash buffers, securities holdings and capitalization. Within AEs—unlike in EMs—larger banks are most exposed, due to greater wholesale funding and thinner capital buffers. Estimated aggregate losses are substantial in some countries, reflecting a range of recent shocks.
    Keywords: Banks; Liquidity Risk; Solvency Risk
    Date: 2023–09–22
  10. By: Paul Glasserman; Harry Mamaysky; Jimmy Qin
    Abstract: An increase in the novelty of news predicts negative stock market returns and negative macroeconomic outcomes over the next year. We quantify news novelty - changes in the distribution of news text - through an entropy measure, calculated using a recurrent neural network applied to a large news corpus. Entropy is a better out-of-sample predictor of market returns than a collection of standard measures. Cross-sectional entropy exposure carries a negative risk premium, suggesting that assets that positively covary with entropy hedge the aggregate risk associated with shifting news language. Entropy risk cannot be explained by existing long-short factors.
    Date: 2023–09
  11. By: Tae-Hwy Lee (Department of Economics, University of California Riverside); Ekaterina Seregina (Colby College); Yaojue Xu (Colby College)
    Abstract: In this paper, we construct a class of strictly consistent scoring functions based on the Bregman divergence measure, which jointly elicit the mean and variance. We use the scoring functions to develop a novel out-of-sample forecast encompassing test in volatility predictive models. We show the encompassing test is asymptotically normal. Simulation results demonstrate the merits of the proposed Bregman scoring functions and the forecast encompassing test. The forecast encompassing test exhibits a proper size and good power in finite samples. In an empirical application, we investigate the predictive ability of macroeconomic and financial variables in forecasting the equity premium volatility.
    Keywords: strictly consistent scoring function, elicitability, Bregman divergence, Granger-causality, encompassing, model averaging, equity premium.
    JEL: C53 E37 E27
    Date: 2023–09
  12. By: Romain Bocher (NN Investment Partners)
    Abstract: Between two significant implied volatility spikes, the CBOE VIX index tends to gradually converge towards a form of relative equilibrium, as if driven by stabilizing forces. Such fractal compression patterns can be analyzed with regards to investors behavioral biases, highlighting critical zones in which the stock market becomes vulnerable to even small shocks.
    Keywords: Implied Volatility, Options, Self-organized Criticality, Behavioral Finance
    Date: 2022
  13. By: Federico, Salvatore (Center for Mathematical Economics, Bielefeld University); Ferrari, Giorgio (Center for Mathematical Economics, Bielefeld University); Torrente, Maria Laura (Center for Mathematical Economics, Bielefeld University)
    Abstract: We propose a model in which, in exchange to the payment of a fixed transaction cost, an insurance company can choose the retention level as well as the time at which subscribing a perpetual reinsurance contract. The surplus process of the insurance company evolves according to the diffusive approximation of the Cramér-Lundberg model, claims arrive at a fixed constant rate, and the distribution of their sizes is general. Furthermore, we do not specify any specific functional form of the retention level. The aim of the company is to take actions in order to minimize the sum of the expected value of the total discounted flow of capital injections needed to avoid bankruptcy and of the fixed activation cost of the reinsurance contract. We provide an explicit solution to this problem, which involves the resolution of a static nonlinear optimization problem and of an optimal stopping problem for a reflected diffusion. We then illustrate the theoretical results in the case of proportional and excess-of-loss reinsurance, by providing a numerical study of the dependency of the optimal solution with respect to the model’s parameters.
    Keywords: reinsurance, fixed cost, capital injections, diffusive risk model, optimal stopping
    Date: 2023–10–06
  14. By: Mr. Dimitrios Laliotis; Sujan Lamichhane
    Abstract: This paper explores a novel forward-looking approach to study the financial stability implications of climate-related transition risks. We develop an integrated micro-macro framework with a new class of scenario called delayed-uncertain pathways. An additional stochastic financial modeling layer via a jump-diffusion process is considered to capture continuously changing risks, as well as the potential of large/sudden shocks in the financial markets. We applied this approach to study transition risks in the Mexican financial sector. But the implications are global in scope, and the framework is easily adaptable to other countries. We quantify the projections of future distributions of various risk metrics and, hence, the evolving tail risks due to compounding effects from delays in transitioning to a low-carbon economy and the consequent uncertainty of the future policy path. We find that the longer the delays in transition, the larger the future tail financial risks, which could be material to the overall system.
    Keywords: Climate change; transition risk; greenhouse gas emissions; financial stability; stress testing; default risk; jump-diffusion; mapping CGE model sector; tail risk; NAICS sector classification; probabilities of default; vulnerability indicator; Credit risk; Global
    Date: 2023–08–25
  15. By: John Armstrong; Andrei Ionescu
    Abstract: We apply rough path theory to study the discrete-time gamma-hedging strategy. We show that if a trader knows that the market price of a set of European options will be given by a diffusive pricing model, then the discrete-time gamma-hedging strategy will enable them to replicate other European options so long as the underlying price path is sufficiently regular. This is a sure result and does not require that the underlying price path has a quadratic variation corresponding to the pricing model. We show how to generalise this result to exotic derivatives when the gamma is defined to be the Gubinelli derivative of the delta by deriving rough-path versions of the Clark--Ocone formula which hold surely.
    Date: 2023–09
  16. By: Jakub Micha\'nk\'ow; Pawe{\l} Sakowski; Robert \'Slepaczuk
    Abstract: This paper investigates the issue of an adequate loss function in the optimization of machine learning models used in the forecasting of financial time series for the purpose of algorithmic investment strategies (AIS) construction. We propose the Mean Absolute Directional Loss (MADL) function, solving important problems of classical forecast error functions in extracting information from forecasts to create efficient buy/sell signals in algorithmic investment strategies. Finally, based on the data from two different asset classes (cryptocurrencies: Bitcoin and commodities: Crude Oil), we show that the new loss function enables us to select better hyperparameters for the LSTM model and obtain more efficient investment strategies, with regard to risk-adjusted return metrics on the out-of-sample data.
    Date: 2023–09
  17. By: Jakub Micha\'nk\'ow; Pawe{\l} Sakowski; Robert \'Slepaczuk
    Abstract: This paper proposes a novel approach to hedging portfolios of risky assets when financial markets are affected by financial turmoils. We introduce a completely novel approach to diversification activity not on the level of single assets but on the level of ensemble algorithmic investment strategies (AIS) built based on the prices of these assets. We employ four types of diverse theoretical models (LSTM - Long Short-Term Memory, ARIMA-GARCH - Autoregressive Integrated Moving Average - Generalized Autoregressive Conditional Heteroskedasticity, momentum, and contrarian) to generate price forecasts, which are then used to produce investment signals in single and complex AIS. In such a way, we are able to verify the diversification potential of different types of investment strategies consisting of various assets (energy commodities, precious metals, cryptocurrencies, or soft commodities) in hedging ensemble AIS built for equity indices (S&P 500 index). Empirical data used in this study cover the period between 2004 and 2022. Our main conclusion is that LSTM-based strategies outperform the other models and that the best diversifier for the AIS built for the S&P 500 index is the AIS built for Bitcoin. Finally, we test the LSTM model for a higher frequency of data (1 hour). We conclude that it outperforms the results obtained using daily data.
    Date: 2023–09
  18. By: Wenting Liu; Zhaozhong Gui; Guilin Jiang; Lihua Tang; Lichun Zhou; Wan Leng; Xulong Zhang; Yujiang Liu
    Abstract: With the increasing volume of high-frequency data in the information age, both challenges and opportunities arise in the prediction of stock volatility. On one hand, the outcome of prediction using tradition method combining stock technical and macroeconomic indicators still leaves room for improvement; on the other hand, macroeconomic indicators and peoples' search record on those search engines affecting their interested topics will intuitively have an impact on the stock volatility. For the convenience of assessment of the influence of these indicators, macroeconomic indicators and stock technical indicators are then grouped into objective factors, while Baidu search indices implying people's interested topics are defined as subjective factors. To align different frequency data, we introduce GARCH-MIDAS model. After mixing all the above data, we then feed them into Transformer model as part of the training data. Our experiments show that this model outperforms the baselines in terms of mean square error. The adaption of both types of data under Transformer model significantly reduces the mean square error from 1.00 to 0.86.
    Date: 2023–09
  19. By: Ayelen Banegas; Phillip J. Monin
    Abstract: Hedge funds have become among the most active participants in U.S. Treasury (UST) markets over the past decade. As a result, the financial stability vulnerabilities associated with their leveraged Treasury market exposures, which are facilitated by low or zero haircuts on their Treasury repo borrowing, have become more prominent.
    Date: 2023–09–08

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