nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒06‒26
29 papers chosen by
Stan Miles
Thompson Rivers University

  1. Study on Intelligent Forecasting of Credit Bond Default Risk By Kai Ren
  2. Value-at-Risk-Based Portfolio Insurance: Performance Evaluation and Benchmarking Against CPPI in a Markov-Modulated Regime-Switching Market By Peyman Alipour; Ali Foroush Bastani
  3. Risk management with Local Least Squares Monte-Carlo By Hainaut, Donatien; Akbaraly, Adnane
  4. Efficient Learning of Nested Deep Hedging using Multiple Options By Masanori Hirano; Kentaro Imajo; Kentaro Minami; Takuya Shimada
  5. Backward Hedging for American Options with Transaction Costs By Ludovic Gouden\`ege; Andrea Molent; Antonino Zanette
  6. The Unified Framework for Modelling Credit Cycles with Marshall-Walras Price Formation Process And Systemic Risk Assessment By Kamil Fortuna; Janusz Szwabi\'nski
  7. Endowment contingency funds for mutual aid and public financing By Denuit, Michel; Robert, Christian Y.
  8. Skewness-seeking behavior and financial investments By Matteo Benuzzi; Matteo Ploner
  9. A Simulation Package in VBA to Support Finance Students for Constructing Optimal Portfolios By Abdulnasser Hatemi-J; Alan Mustafa
  10. A District-Level Analysis of the Effect of Risk Exposure on the Demand for Index Insurance in Mongolia By Mogge, Lukas
  11. Loss Aversion, Risk Aversion, and the Shape of the Probability Weighting Function By Matthew D. Rablen
  12. The Dynamic Persistence of Economic Shocks By Jozef Barunik; Lukas Vacha
  13. Measuring Transition Risk in Investment Funds By Ricardo Crisóstomo
  14. Reinforcement Learning and Portfolio Allocation: Challenging Traditional Allocation Methods By Lavko, Matus; Klein, Tony; Walther, Thomas
  15. Extreme ATM skew in a local volatility model with discontinuity: joint density approach By Alexander Gairat; Vadim Shcherbakov
  16. The Credit Suisse CoCo Wipeout: Facts, Misperceptions, and Lessons for Financial Regulation By Patrick Bolton; Wei Jiang; Anastasia V. Kartasheva
  17. Comonotonicity and Pareto Optimality, with Application to Collaborative Insurance By Denuit, Michel; Dhaene, Jan; Ghossoub, Mario; Robert, Christian Y.
  18. How Political Tensions and Geopolitical Risks Impact Oil Prices? By Valérie Mignon; Jamel Saadaoui
  19. Mind Your Language: Market Responses to Central Bank Speeches By Maximilian Ahrens; Deniz Erdemlioglu; Michael McMahon; Christopher J. Neely; Xiye Yang
  20. Crowdfunding and Risk By David Cimon
  21. Equity, Commodity, and Distillate Risk for Oil Upstream Producers and Downstream Consumers By Scott Alan Carson; Scott A. Carson
  22. What Is “Outlook-at-Risk?” By Nina Boyarchenko; Richard K. Crump; Leonardo Elias; Ignacio Lopez Gaffney
  23. Prosocial Risk-Taking: Growing the Pie or Increasing your Slice? By Nina Weber
  24. Competition and risk taking in local bank markets: evidence from the business loans segment By Canta, Chiara; Nilsen, Øivind A.; Ulsaker, Simen A.
  25. Equity Protection Swaps: An New Type of Insurance for Superannuation By Huansang Xu; Ruyi Liu
  26. Evolutionary multi-objective optimisation for large-scale portfolio selection with both random and uncertain returns By Liu, Weilong; Zhang, Yong; Liu, Kailong; Quinn, Barry; Yang, Xingyu; Peng, Qiao
  27. Betting on the Lord: lotteries and religiosity in Haiti By Emmanuelle Auriol; Diego Delissaint; Maleke Fourati; Josepa Miquel-Florensa; Paul Seabright
  28. Is the role of precious metals as precious as they are? A vine copula and BiVaR approaches By Marwa Talbi; Rihab Bedoui; Christian de Peretti; Lotfi Belkacem
  29. Evaluation of the prospects of hedging Botswana's maize prices against the Johannesburg Stock Exchange Commodity Market Derivative By Ofentse, Goetswamang Phankie

  1. By: Kai Ren
    Abstract: Credit risk in the China's bond market has become increasingly evident, creating a progressively escalating risk of default for credit bond investors. Given the current incomplete and inaccurate bond information disclosure, timely tracking and forecasting the individual credit bond default risks have become essential to maintain market stability and ensure healthy development. This paper proposes an Intelligent Forecasting Framework for Default Risk that provides precise day-by-day default risk prediction. In this framework, we first summarize the factors that impact credit bond defaults and construct a risk index system. Then, we employ a combined default probability annotation method based on the evolutionary characteristics of bond default risk. The method considers the weighted average of Variational Bayesian Gaussian Mixture estimation, Market Index estimation, and Default Trend Backward estimation for daily default risk annotation of matured or defaulted bonds according to the risk index system. Moreover, to mine time-series correlation and cross-sectional index correlation features efficiently, an intelligent prediction model for Chinese credit bond default risk is designed using the ConvLSTM neural network and trained with structured feature data. The experiments demonstrate that the predicted individual bond risk is slightly higher and substantially more responsive to fluctuations than the risk indicated by authoritative ratings, thereby improving on the inadequacies of inflated and untimely bond ratings. Consequently, this study's findings offer multiple insights for regulators, issuers, and investors.
    Date: 2023–05
  2. By: Peyman Alipour; Ali Foroush Bastani
    Abstract: Designing dynamic portfolio insurance strategies under market conditions switching between two or more regimes is a challenging task in financial economics. Recently, a promising approach employing the value-at-risk (VaR) measure to assign weights to risky and riskless assets has been proposed in [Jiang C., Ma Y. and An Y. "The effectiveness of the VaR-based portfolio insurance strategy: An empirical analysis" , International Review of Financial Analysis 18(4) (2009): 185-197]. In their study, the risky asset follows a geometric Brownian motion with constant drift and diffusion coefficients. In this paper, we first extend their idea to a regime-switching framework in which the expected return of the risky asset and its volatility depend on an unobservable Markovian term which describes the cyclical nature of asset returns in modern financial markets. We then analyze and compare the resulting VaR-based portfolio insurance (VBPI) strategy with the well-known constant proportion portfolio insurance (CPPI) strategy. In this respect, we employ a variety of performance evaluation criteria such as Sharpe, Omega and Kappa ratios to compare the two methods. Our results indicate that the CPPI strategy has a better risk-return tradeoff in most of the scenarios analyzed and maintains a relatively stable return profile for the resulting portfolio at the maturity.
    Date: 2023–05
  3. By: Hainaut, Donatien (Université catholique de Louvain, LIDAM/ISBA, Belgium); Akbaraly, Adnane (Detralytics)
    Abstract: The method of least squares Monte-Carlo (LSMC) has become a standard in the insurance and financial sectors for computing the exposure of a company to market risk. The sensitive point of this procedure is the non-linear regression of simulated responses on risk factors. This article proposes a novel approach for this step, based on an a-priori segmentation of responses. Using a K-means algorithm, we identify clusters of responses that are next locally regressed on corresponding risk factors. A global function of regression is obtained by combining local models and a logistic regression. The efficiency of the Local Least squares Monte-Carlo (LLSMC) is checked in two illustrations. The first one focuses on butterfly and bull trap options in a Heston stochastic volatility model. The second illustration analyzes the exposure to risks of a participating life insurance.
    Keywords: Least square Monte-Carlo ; risk management ; option valuation
    JEL: C5 G22
    Date: 2023–01–24
  4. By: Masanori Hirano; Kentaro Imajo; Kentaro Minami; Takuya Shimada
    Abstract: Deep hedging is a framework for hedging derivatives in the presence of market frictions. In this study, we focus on the problem of hedging a given target option by using multiple options. To extend the deep hedging framework to this setting, the options used as hedging instruments also have to be priced during training. While one might use classical pricing model such as the Black-Scholes formula, ignoring frictions can offer arbitrage opportunities which are undesirable for deep hedging learning. The goal of this study is to develop a nested deep hedging method. That is, we develop a fully-deep approach of deep hedging in which the hedging instruments are also priced by deep neural networks that are aware of frictions. However, since the prices of hedging instruments have to be calculated under many different conditions, the entire learning process can be computationally intractable. To overcome this problem, we propose an efficient learning method for nested deep hedging. Our method consists of three techniques to circumvent computational intractability, each of which reduces redundant computations during training. We show through experiments that the Black-Scholes pricing of hedge instruments can admit significant arbitrage opportunities, which are not observed when the pricing is performed by deep hedging. We also demonstrate that our proposed method successfully reduces the hedging risks compared to a baseline method that does not use options as hedging instruments.
    Date: 2023–05
  5. By: Ludovic Gouden\`ege; Andrea Molent; Antonino Zanette
    Abstract: In this article, we introduce an algorithm called Backward Hedging, designed for hedging European and American options while considering transaction costs. The optimal strategy is determined by minimizing an appropriate loss function, which is based on either a risk measure or the mean squared error of the hedging strategy at maturity. By appropriately reformulating this loss function, we can address its minimization by moving backward in time. The approach avoids machine learning and instead relies on traditional optimization techniques, Monte Carlo simulations, and interpolations on a grid. Comparisons with the Deep Hedging algorithm in various numerical experiments showcase the efficiency and accuracy of the proposed method.
    Date: 2023–05
  6. By: Kamil Fortuna; Janusz Szwabi\'nski
    Abstract: Systemic risk is a rapidly developing area of research. Classical financial models often do not adequately reflect the phenomena of bubbles, crises, and transitions between them during credit cycles. To study very improbable events, systemic risk methodologies utilise advanced mathematical and computational tools, such as complex systems, chaos theory, and Monte Carlo simulations. In this paper, a relatively simple mathematical formalism is applied to provide a unified framework for modeling credit cycles and systemic risk assessment. The proposed model is analyzed in detail to assess whether it can reflect very different states of the economy. Basing on those results, measures of systemic risk are constructed to provide information regarding the stability of the system. The formalism is then applied to describe the full credit cycle with the explanation of causal relationships between the phases expressed in terms of parameters derived from real-world quantities. The framework can be naturally interpreted and understood with respect to different economic situations and easily incorporated into the analysis and decision-making process based on classical models, significantly enhancing their quality and flexibility.
    Date: 2023–05
  7. By: Denuit, Michel (Université catholique de Louvain, LIDAM/ISBA, Belgium); Robert, Christian Y.
    Abstract: This paper explores a new risk-sharing vehicle, called endowment contingency fund. It targets groups of individuals exposed to the occurrence of a predefined event with adverse financial consequences such as death, survival or being diagnosed with a critical illness for instance. All members of the group agree to contribute in advance a fixed amount to a pool constituted over a reference period with the understanding that the sum of the contributions is shared in arrear among those participants having experienced the predefined event. This allocation is either uniform among claiming participants, each one receiving an equal share of total contributions, or participants are offered the choice to select a desired protection level. In the latter case, participants are free to subscribe one or several units of protection from the fund, and the total amount collected in advance is shared in arrear, equally among all units held by those participants who experienced the predefined event. Endowment contingency funds aim to provide participants with a cheap and effective protection compared to commercial insurance. The reason is that the proposed system is fully funded so that there is no risk borne by the organizer. The benefits in case the event occurs are therefore random but the volatility of the terminal payouts turns out to be limited when the number of participants gets large enough. Under independence, insurance at fair price is recovered at the limit, within infinitely large pools. As an application, the paper considers mutual aid funds and survivor funds. Several related issues are discussed, including a comparison with takaful insurance as well as the inclusion of minimum guarantees to make the system more attractive.
    Keywords: Risk pooling ; Conditional mean risk-sharing ; Actuarial fairness ; Mutual inheritance ; Insurance at fair price
    Date: 2023–02–12
  8. By: Matteo Benuzzi; Matteo Ploner
    Abstract: Recent theoretical and empirical contributions have demonstrated the sig- nificance of higher-order moments, such as skewness, in influencing financial decisions. Most current experimental literature relies on lotteries with a lim- ited number of potential outcomes, which do not accurately represent real-life investments. To address this gap, we conducted a pre-registered experiment that examines preferences toward investment opportunities with varying skew- ness using continuous distributions. Our findings reveal several key insights. Firstly, there is an overall preference for positively skewed distributions of outcomes. Secondly, we observed a substitution effect between risk-taking, as measured by variance, and the direction of skewness. Lastly, we established a positive correlation between skewness-seeking behavior and speculative be- havior and a negative correlation between skewness-seeking behavior and risk perception of positive skewness.
    Keywords: Skewness, Risk-taking, Stochastic Dominance, Experiment
    JEL: C91 D81 G11
    Date: 2023
  9. By: Abdulnasser Hatemi-J; Alan Mustafa
    Abstract: This paper introduces a software component created in Visual Basic for Applications (VBA) that can be applied for creating an optimal portfolio using two different methods. The first method is the seminal approach of Markowitz that is based on finding budget shares via the minimization of the variance of the underlying portfolio. The second method is developed by El-Khatib and Hatemi-J, which combines risk and return directly in the optimization problem and yields budget shares that lead to maximizing the risk adjusted return of the portfolio. This approach is consistent with the expectation of rational investors since these investors consider both risk and return as the fundamental basis for selection of the investment assets. Our package offers another advantage that is usually neglected in the literature, which is the number of assets that should be included in the portfolio. The common practice is to assume that the number of assets is given exogenously when the portfolio is constructed. However, the current software component constructs all possible combinations and thus the investor can figure out empirically which portfolio is the best one among all portfolios considered. The software is consumer friendly via a graphical user interface. An application is also provided to demonstrate how the software can be used using real-time series data for several assets.
    Date: 2023–05
  10. By: Mogge, Lukas
    Abstract: This paper provides novel evidence on how risk exposure shapes the demand for index-based weather insurance. The focus is on Mongolia, where index insurance is offered as a commercially marketed product to pastoralists threatened by extreme weather events that cause high livestock mortality. Using a two-way fixed effect model and country-wide district-level data spanning a period of five years, this paper shows that the demand for index insurance increases in areas exposed to adverse weather conditions occurring in the months preceding the end of the insurance sales period. The effect is neither driven by the receipt of insurance payouts nor by observing peers receiving payouts. I argue that these results can be best explained by insurance purchasers adapting their risk perception in response to recent weather risks. The findings of this paper point to a problem for policymakers as a period of mild weather conditions could cause households to lose interest in purchasing insurance, thus leading to underinvestment in insurance coverage.
    Keywords: Extreme weather events, index insurance, livestock, risk, Mongolia
    JEL: O12 O13 O14
    Date: 2023
  11. By: Matthew D. Rablen (Department of Economics, University of Sheffield, 9 Mappin Street, Sheffield, S1 4DT, UK.)
    Abstract: Loss aversion, risk aversion, and the probability weighting function (PWF) are three central concepts in explaining decision making under risk. I examine interlinkages be- tween these concepts in a model of decision making that allows for loss averse/tolerant stochastic reference dependence and optimism/pessimism over probability distribu- tions. I give a preference interpretation to commonly observed shapes of PWF and to risk aversion. In particular, I establish a connection between loss aversion and both risk aversion and the inverse-S PWF: loss aversion is a necessary condition to observe each of these phenomena. The results extend to distinct PWFs in the gain and loss domains, as under prospect theory.
    Keywords: Probability Weighting; Rank Dependent Expected Utility; Loss Aversion; Risk Aversion; Reference Dependence; Optimism; Pessimism; Prospect Theory
    JEL: D91 D81 D01
    Date: 2023–06
  12. By: Jozef Barunik; Lukas Vacha
    Abstract: This paper presents a model for smoothly varying heterogeneous persistence of economic data. We argue that such dynamics arise naturally from the dynamic nature of economic shocks with various degree of persistence. The identification of such dynamics from data is done using localised regressions. Empirically, we identify rich persistence structures that change smoothly over time in two important data sets: inflation, which plays a key role in policy formulation, and stock volatility, which is crucial for risk and market analysis.
    Date: 2023–06
  13. By: Ricardo Crisóstomo
    Abstract: We develop a comprehensive framework to measure the impact of the climate transition on investments portfolios. Our analysis is enriched by including geographical, sectorial, company an ISIN-level data o assess transition risk. We find that investment funds suffer a moderate 5.7% loss upon materialization of a high transition risk scenario. However, the risk distribution is significantly left-skewed, with the worst 1% funds excperiencing an average loss of 21.3%. Imnterms of asst classes, equities are the worst performers (12.7%), followed by corporate bonds (5.6%) and government bonds (-4.8%). We discriminate among financial instruments by considering the carbon footprint of specific counterparties and the credit rating, duration, convexity and volatility of individual exposures. We find that sustainable funds are less exposed to transitions risk and perform better than the overall fund sector in the low-carbon transition, validating their choice as green investments
    Keywords: Climate change, Low-carbon transition, Asset allocation, Investment funds, NGFS scenarios
    JEL: G11 G12 G32 G17 Q54
    Date: 2023
  14. By: Lavko, Matus; Klein, Tony; Walther, Thomas
    Abstract: We test the out-of-sample trading performance of model-free reinforcement learning (RL) agents and compare them with the performance of equally-weighted portfolios and traditional mean-variance (MV) optimization benchmarks. By dividing European and U.S. indices constituents into factor datasets, the RL-generated portfolios face different scenarios defined by these factor environments. The RL approach is empirically evaluated based on a selection of measures and probabilistic assessments. Training these models only on price data and features constructed from these prices, the performance of the RL approach yields better risk-adjusted returns as well as probabilistic Sharpe ratios compared to MV specifications. However, this performance varies across factor environments. RL models partially uncover the nonlinear structure of the stochastic discount factor. It is further demonstrated that RL models are successful at reducing left-tail risks in out-of-sample settings. These results indicate that these models are indeed useful in portfolio management applications.
    Keywords: Asset Allocation, Reinforcement Learning, Machine Learning, Portfolio Theory, Diversification
    JEL: G11 C44 C55 C58
    Date: 2023
  15. By: Alexander Gairat; Vadim Shcherbakov
    Abstract: This paper concerns a local volatility model in which volatility takes two possible values, and the specific value depends on whether the underlying price is above or below a given threshold value. The model is known, and a number of results have been obtained for it. In particular, explicit pricing formulas for European options have been recently obtained and applied to establish a power law behaviour of the implied volatility skew in the case when the threshold is taken at the money. These results have been obtained by techniques based on the Laplace transform. The purpose of the present paper is to demonstrate how to obtain the same results by another method. This alternative approach is based on the natural relationship of the model with Skew Brownian motion and consists in the systematic use of the joint distribution of this stochastic process and some of its functionals.
    Date: 2023–05
  16. By: Patrick Bolton (Imperial College); Wei Jiang (Emory University); Anastasia V. Kartasheva (University of St. Gallen; Swiss Finance Institute)
    Abstract: On March 19, 2023, the Swiss Financial Market Supervisory Authority (FINMA) announced that, as part of the Credit Suisse emergency package, the contingent convertible bonds that were part of the Credit Suisse Additional Tier 1 (AT1) regulatory capital, had been written off. We review the CoCo design and economic rationales, explaining why the Credit Suisse AT1 CoCo bondholders faced losses before shareholders were wiped out. Also we analyze the reasons for the divergent view of regulators outside of Switzerland in the aftermath of the conversion. We argue that FINMA’s decision creates a healthy precedent: restoring financial discipline in AT1 bond markets by reminding investors that their investment is exposed to credit risk and that due diligence is advised before investing in these products. Credit Suisse AT1 CoCo conversion offers lessons for the effectiveness of post-GFC too-big-to-fail reforms, emphasizing that CoCo conversion that saved Swiss taxpayers USD17 Billion is a more reliable and cost-efficient policy than government-sponsored bailouts and resolution which is inherently uncertain and prone to contagion.
    Keywords: Contingent convertible capital securities, bank fragility, recapitalization, too-big-to-fail, AT1
    JEL: G01 G21 G28
    Date: 2023–05
  17. By: Denuit, Michel (Université catholique de Louvain, LIDAM/ISBA, Belgium); Dhaene, Jan (KU Leuven); Ghossoub, Mario (University of Waterloo); Robert, Christian Y. (INSEE - CREST)
    Abstract: Two by-now folkloric results in the theory of risk sharing are that (i) any feasible allocation is convex-order-dominated by a comonotonic allocation; and (ii) an allocation is Pareto optimal for the convex order if and only if it is comonotonic. Here, comonotonicity corresponds to the no-sabotage condition, which aligns the interests of all parties involved. Several proofs of these two results have been provided in the literature, mostly based on the comonotonic improvement algorithm of Landsberger and Meilijson (1994) and a limit argument based on the Martingale Convergence Theorem. However, no proof of (i) is explicit enough to allow for an easy algorithmic implementation in practice; and no proof of (ii) provides a closed-form characterization of Pareto optima. In this paper, we provide novel proofs of these foundational results. Our proof of (i) is based on the theory of majorization and an extension of a result of Lorentz and Shimogaki (1968), which allows us to provide an explicit algorithmic construction that can be easily implemented. In addition, our proof of (ii) leads to a crisp closed-form characterization of Pareto-optimal allocations in terms of alpha-quantiles (mixed quantiles). An application to collaborative insurance, or decentralized risk sharing, illustrates the relevance of these results.
    Keywords: Risk Sharing ; Comonotonicity ; Pareto Optimality ; Convex Order ; Convex Order Improvement ; Peer-to-Peer Insurance
    JEL: C02 D86 D89 G22
    Date: 2023–01–24
  18. By: Valérie Mignon; Jamel Saadaoui
    Abstract: This paper assesses the effect of US-China political relationships and geopolitical risks on oil prices. To this end, we consider two quantitative measures — the Political Relationship Index and the Geopolitical Risk Index — and rely on structural VAR and local projections methodologies. Our findings show that improved US-China relationships, as well as higher geopolitical risks, drive up the price of oil. Positive shocks on the political relationship index are associated with optimistic expectations regarding economic activity, whereas positive shocks on the geopolitical risk index reflect fears of supply disruption. Political tensions and geopolitical risks are thus complementary factors, the former being linked to the demand side and the latter to the supply side.
    Keywords: Oil prices, political relationships, geopolitical risk, China.
    JEL: Q4 F51 C32
    Date: 2023
  19. By: Maximilian Ahrens; Deniz Erdemlioglu; Michael McMahon; Christopher J. Neely; Xiye Yang
    Abstract: Researchers have carefully studied post-meeting central bank communication and have found that it often moves markets, but they have paid less attention to the more frequent central bankers’ speeches. We create a novel dataset of US Federal Reserve speeches and use supervised multimodal natural language processing methods to identify how monetary policy news affect financial volatility and tail risk through implied changes in forecasts of GDP, inflation, and unemployment. We find that news in central bankers’ speeches can help explain volatility and tail risk in both equity and bond markets. We also find that markets attend to these signals more closely during abnormal GDP and inflation regimes. Our results challenge the conventional view that central bank communication primarily resolves uncertainty.
    Keywords: central bank communication; multimodal machine learning; natural language processing; speech analysis; high-frequency data; volatility; tail risk
    JEL: E50 E52 C45 C53 G10 G12 G14
    Date: 2023–05–31
  20. By: David Cimon
    Abstract: This paper examines the role of rewards-based and equity-based crowdfunding in funding new businesses. In this model, crowdfunding is a unique technology that serves as a real option for production and eliminates downside risk. It affords entrepreneurs who face uncertain consumer demand a viable means of funding new projects. Crowdfunding performs well for projects with a high variability in demand and a low probability of success. Conversely, crowdfunding does not perform well for large projects with little variability in demand or for projects where the production side is uncertain.
    Keywords: Digital currencies and fintech; Financial markets; Financial services
    JEL: G21 G24 G32
    Date: 2023–05
  21. By: Scott Alan Carson; Scott A. Carson
    Abstract: The oil and gas industry’s role in economic activity is hard to overstate. This study considers upstream, midstream, and downstream oil producer returns and risk compared to downstream oil consumers in airlines, ground-freight, railroads, and tire manufacturing. Between 2000 and 2020, the oil and gas industry had the lowest expected returns, greater risk, and only Integrated producer returns approached downstream oil and gas consumer risk-return profiles. Railroad companies were the least risky with the highest returns, followed by tire manufacturers, airlines, and freight companies. Equity, commodity, and distillate markets positively price risk into oil and gas producer returns, and upstream producers had greater project and equity market risk than downstream consumers. Most downstream oil consumer equity returns are positively related to equity and commodity market risk, while a few downstream commercial consumers have negative equity and commodity return variation, indicating that crude oil is an input to downstream consumers.
    Keywords: oil and gas, air transportation, ground freight, railroads, tire manufacturing
    JEL: L62 L72 L93 L91 L92
    Date: 2023
  22. By: Nina Boyarchenko; Richard K. Crump; Leonardo Elias; Ignacio Lopez Gaffney
    Abstract: The timely characterization of risks to the economic outlook plays an important role in both economic policy and private sector decisions. In a February 2023 Liberty Street Economics post, we introduced the concept of “Outlook-at-Risk”—that is, the downside risk to real activity and two-sided risks to inflation. Today we are launching Outlook-at-Risk as a regularly updated data product, with new readings for the conditional distributions of real GDP growth, the unemployment rate, and inflation to be published each month. In this post, we use the data on conditional distributions to investigate how two-sided risks to inflation and downside risks to real activity have evolved over the current and previous five monetary policy tightening cycles.
    Keywords: Outlook-at-Risk; monetary policy tightening
    JEL: E2 G1
    Date: 2023–05–17
  23. By: Nina Weber
    Abstract: Many personally risky decisions, such as innovation and entrepreneurship, have the potential to increase overall welfare by creating positive externalities for society. Rewarding such prosocial risk-taking may be an important strategy in addressing societal challenges like, for example, the climate emergency, by promoting innovation that has positive externalities for the environment. A fundamental constraint for policy makers in rewarding such behaviour are however individuals’ distributive preferences. In this paper, I provide a theoretical framework and a first experimental test of how distributive preferences are affected by potential positive externalities of risky behaviour.
    Keywords: Prosocial risk-taking, distributive preferences, fairness
    JEL: D63 D62 D81 C91
    Date: 2023
  24. By: Canta, Chiara (TBS Business School); Nilsen, Øivind A. (Dept. of Economics, Norwegian School of Economics and Business Administration); Ulsaker, Simen A. (Telenor Research)
    Abstract: This paper studies empirically the relationship between competition and risk taking in banking markets. We exploit an unique dataset providing information about all bank loans to Norwegian firms over several years. Rather than relying on observed market shares, we use the distance between bank branches and firms to measure the competitiveness of local markets. The cross-sectional and longitudinal variation in competition in local markets are used to identify the relationship between competition and risk taking, which we measure by the non-performing loans and loss provision rates of the individual banks. We find that more competition leads to more risk taking. We also examine the effects of bank competition on the availability of loans. More competition leads to lower interest rates and higher loan volumes, but also makes it more difficult for small and newly established firms to obtain a loan.
    Keywords: Competition; risk
    JEL: G21 L11 L13
    Date: 2023–05–18
  25. By: Huansang Xu; Ruyi Liu
    Abstract: Equity protection swaps (EPS) is a new type of superannuation insurance products base on swap structure. It has some developments on total return swaps (TRS) and is reminiscent of the insurance products known as registered index-linked annuities (RILA). The EPS buyer needs to share gains with EPS provider if the value of underlying reference portfolio increases and obtain protection if there is a loss. The EPS structure consists of two parts, a protection leg and a fee leg, with different participation rates negotiated by both EPS provider and buyer. We present a general pricing formula for standard EPS and obtain the corresponding static hedging strategy by using European options. To attract more investors, EPS provider are able to select an appropriate participation rate of fee leg in relation to the required protection level and levies no additional fee on investors. We also present numerical examples to illustrate.
    Date: 2023–04
  26. By: Liu, Weilong; Zhang, Yong; Liu, Kailong; Quinn, Barry; Yang, Xingyu; Peng, Qiao
    Abstract: With the advent of Big Data, managing large-scale portfolios of thousands of securities is one of the most challenging tasks in the asset management industry. This study uses an evolutionary multi objective technique to solve large-scale portfolio optimisation problems with both long-term listed and newly listed securities. The future returns of long-term listed securities are defined as random variables whose probability distributions are estimated based on sufficient historical data, while the returns of newly listed securities are defined as uncertain variables whose uncertainty distribution are estimated based on experts' knowledge. Our approach defines security returns as theoretically uncertain random variables and proposes a three-moment optimisation model with practical trading constraints. In this study, a framework for applying arbitrary multi-objective evolutionary algorithms to portfolio optimisation is established, and a novel evolutionary algorithm based on large-scale optimisation techniques is developed to solve the proposed model. The experimental results show that the proposed algorithm outperforms state-of-the-art evolutionary algorithms in large-scale portfolio optimisation.
    Keywords: Evolutionary computations, Portfolio optimisation, Large-scale investment, Uncertain random variable, Multi-objective optimisation
    Date: 2023
  27. By: Emmanuelle Auriol (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Diego Delissaint; Maleke Fourati; Josepa Miquel-Florensa (IAST - Institute for Advanced Study in Toulouse); Paul Seabright (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)
    Abstract: We conducted an experimental study in Haiti testing for the relationship between religious belief and individual risk taking behavior. 774 subjects played lotteries in a standard neutral protocol and subsequently with reduced endowments but in the presence of religious images of Catholic, Protestant and Voodoo tradition. Subjects chose between paying to play a lottery with an image of their choice, and saving their money to play with no image. Those who chose the former are dened as image buyers and those who chose the latter as non-buyers. Image buyers, who tend to be less educated, more rural, and to exhibit greater religiosity, bet more than non-buyers in all games. In addition, in the presence of religious images all participants took more risk, and buyers took more risk when playing in the presence of their chosen images than when playing with other images. We develop a theoretical model calibrated with our experimental data to explore the channels through which religious images might a ect risk-taking. Our results suggest that the presence of images tends to increase individuals' subjective probability of winning the lottery, and that subjects therefore believe in a god who intervenes actively in the world in response to their requests.
    Keywords: Risk preferences, Religion, Field Experiment
    Date: 2021–08
  28. By: Marwa Talbi (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon, LAREMFIQ - Laboratory Research for Economy, Management and Quantitative Finance - Institut des Hautes Etudes Commerciales (Université de Sousse)); Rihab Bedoui; Christian de Peretti; Lotfi Belkacem
    Date: 2021–10
  29. By: Ofentse, Goetswamang Phankie
    Abstract: Maize is an important source of food consumed in Botswana and it helps the country to achieve food security status. Food security refers to everyone always having access to healthy, dependable, and adequate food to meet their dietary requirements and live a healthy life. Botswana imports maize primarily from South Africa and is a net importer. The study evaluated how maize prices in Botswana are linked with maize prices in South Africa. To explain hedging opportunities in minimising price risk in Botswana, cointegration and vector error correction models were used in this study. Secondary data on monthly white and yellow maize prices from 2008 to 2019 were used in this study. The empirical data show that maize prices in South Africa and Botswana have a long-run equilibrium relationship. In the short run, results indicate that the previous years’ maize prices in the Botswana market positively impact all Botswana maize prices at a 1% significance level on average ceteris paribus. South Africa’s maize market does not respond to any market changes in Botswana for white maize prices lagged for one and two periods. The Botswana maize market, on the other hand, reacts to price fluctuations in the South African market for both white and yellow maize. The adjustment speed in the Botswana maize market ranged from 17% to 29% while the adjustment speed in the South African market ranged from 13% to 17%. Overall, the empirical data show that the two markets have a positive long-run equilibrium relationship and a shortrun asymmetric relationship. The empirical findings prompted the Botswana maize value chain assessment to understand how it operates as well as the existence of relationships among the actors. The study ascertained that Botswana’s maize value chain faces an array of challenges that limit the country’s food sufficient. The assessment of the Botswana maize value chain was vital to promote policy formations that will promote the development of the Botswana maize sector. The study focused on the interaction between smallholder farmers and the intermediaries focusing on the challenges and opportunities therein. The Agency and Social Network theories were used to assess the economic behaviour of the two farmers and middlemen. The investigative methods used included a thorough assessment of the literature and key informant interview. The challenges identified from the investigation included poor coordination, lack of trust, information asymmetry, lack of cooperatives, and inadequate access to finance. The study thus recommended contract farming, prioritisation of training programmes for farmers and extension workers, third-party enforcement of regulations, and revival of cooperatives to III improve the quality of the relationship between the middlemen and the smallholder farmers, and thus improve the overall performance of the chain.
    Keywords: Marketing
    Date: 2022–07

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