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on Risk Management |
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Issue of 2020‒03‒16
eleven papers chosen by |
| By: | Jiri Panos; Petr Polak |
| Abstract: | This paper aims to introduce a contemporary, computing-power-driven approach to econometric modeling in a stress-testing framework. The presented approach explicitly takes into account model uncertainty of satellite models used for projecting forward paths of financial variables employing the constrained Bayesian model averaging (BMA) technique. The constrained BMA technique allows for selecting models with reasonably severe but plausible trajectories conditional on given macro-financial scenarios. It also ensures that the modeling is conducted in a sufficiently robust and prudential manner despite the limited time-series length for the explained and/or explanatory variables. |
| Keywords: | Bayesian model averaging, model selection, model uncertainty, probability of default, stress testing |
| JEL: | C11 C22 C51 C52 E58 G21 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:cnb:wpaper:2019/9&r=all |
| By: | Fernandez Lafuerza,Luis Gonzalo; Serven,Luis |
| Abstract: | This paper assesses the international comovement of gross capital flows in a setting simultaneously encompassing aggregate inflows and outflows. It uses as empirical framework a multilevel latent factor model, implemented on flow data for a large sample of countries over more than three decades. On average, common shocks account for over 40 percent of the variance of both inflows and outflows, although with major differences between advanced countries and the rest. Among the former, global and group shocks dominate capital flows, and the same shocks drive gross inflows and outflows. Among the latter countries, idiosyncratic shocks tend to play the leading role, and gross inflows exhibit less commonality with outflows. The latent factors configure an international financial cycle that closely tracks the trends in a handful of global"push"variables. Recursive estimation of the factor model reveals a rising trend in the exposure of countries'flows to the international cycle?especially for advanced economies?up to the global financial crisis. Exposure to the cycle is robustly related to countries'external financial openness and the (lack of) flexibility of their exchange rate regime. |
| Keywords: | Investment and Investment Climate,Commodity Risk Management,International Trade and Trade Rules,Macroeconomic Management,Financial Regulation&Supervision |
| Date: | 2019–03–21 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wbrwps:8787&r=all |
| By: | Tursoy, Turgut; Berk, Niyazi |
| Abstract: | The finance theory suggests that there might be a relationship between the stock return and the risk premium. Theoretically, stock return defined as the change of the market price, and it is related to the scope of the financial system, which is consisting of the financial institution and financial markets. The way, possibly will be, to contribute the existing literature is to propose a new measurement and this study try to do so. The aim of this study and its motivation is that investigates a new measure of stock return and attempt to establish a new relationship between return and risk premium. To realize this aim, this study uses geometric mean to calculate return and standard deviation, and after all, construct panel data analysis to analyze the return and standard deviation relationship. In this study, seven commercial banks’ data analyzed to the relationship between return and standard deviation with panel data analyses between 1991 and 2010. Also, the geometric mean and value relative concept used to estimate return and the monthly stock prices to yearly basis. |
| Keywords: | Asset Pricing, Stock Return, Risk |
| JEL: | G11 G12 G21 |
| Date: | 2020–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:98877&r=all |
| By: | Lefebvre, Marianne; Midler, Estelle; Bontems, Philippe |
| Abstract: | Farmers choose to avoid some risks by not engaging into practices with uncertain profits. Yet, they still face background risk beyond their control, such as climate change. The impact of background risk on decisions to adopt risky environment-friendly agricultural practices is analysed through a theoretical model and a public good experiment. We find that background risk discourages adoption, despite the fact that it affects both environmentally-friendly and conventionally farmed land equally. An incentive payment increases adoption but is significantly less efficient in the presence of both foreground and background risks. Results shed light on potential synergies between greening the CAP and supporting risk management. |
| Keywords: | Common Agricultural Policy; Agri-environmental measures; Background risk,;Lab; experiment; Public good game |
| JEL: | C93 D81 Q18 Q12 |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:124137&r=all |
| By: | Romain Gauchon (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Stéphane Loisel (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Jean-Louis Rullière (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Julien Trufin (Département de mathématiques Université Libre de Bruxelles - ULB - Université Libre de Bruxelles [Bruxelles]) |
| Abstract: | In this paper, we propose and study a first risk model in which the insurer may invest into a prevention plan which decreases claim intensity. We determine the optimal prevention investment for different risk indicators. In particular, we show that the prevention amount minimizing the ruin probability maximizes the adjustment coefficient in the classical ruin model with prevention, as well as the expected dividends until ruin in the model with dividends. We also show that the optimal prevention strategy is different if one aims at maximizing the average surplus at a fixed time horizon. A sensitivity analysis is carried out. We also prove that our results can be extended to the case where prevention starts to work only after a minimum prevention level threshold. |
| Keywords: | Ruin theory,Optimal prevention strategy,Prevention,Insurance |
| Date: | 2020–02–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-02314899&r=all |
| By: | Michael Pinelis; David Ruppert |
| Abstract: | We find economically and statistically significant gains from using machine learning to dynamically allocate between the market index and the risk-free asset. We model the market price of risk to determine the optimal weights in the portfolio: reward-risk market timing. This involves forecasting the direction of next month's excess return, which gives the reward, and constructing a dynamic volatility estimator that is optimized with a machine learning model, which gives the risk. Reward-risk timing with machine learning provides substantial improvements in investor utility, alphas, Sharpe ratios, and maximum drawdowns, after accounting for transaction costs, leverage constraints, and on a new out-of-sample test set. This paper provides a unifying framework for machine learning applied to both return- and volatility-timing. |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2003.00656&r=all |
| By: | Kristoffer Andersson; Cornelis Oosterlee |
| Abstract: | In this paper, we propose a neural network-based method for approximating expected exposures and potential future exposures of Bermudan options. In a first phase, the method relies on the Deep Optimal Stopping algorithm (DOS) proposed in \cite{DOS}, which learns the optimal stopping rule from Monte-Carlo samples of the underlying risk factors. Cashflow paths are then created by applying the learned stopping strategy on a new set of realizations of the risk factors. Furthermore, in a second phase the risk factors are regressed against the cashflow-paths to obtain approximations of pathwise option values. The regression step is carried out by ordinary least squares as well as neural networks, and it is shown that the latter performs more accurate approximations. The expected exposure is formulated, both in terms of the cashflow-paths and in terms of the pathwise option values and it is shown that a simple Monte-Carlo average yields accurate approximations in both cases. The potential future exposure is estimated by the empirical $\alpha$-percentile. Finally, it is shown that the expected exposures, as well as the potential future exposures can be computed under either, the risk neutral measure, or the real world measure, without having to re-train the neural networks. |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2003.01977&r=all |
| By: | Jose J. Canals-Cerda |
| Abstract: | The Current Expected Credit Loss (CECL) framework represents a new approach for calculating the allowance for credit losses. Credit cards are the most common form of revolving consumer credit and are likely to present conceptual and modeling challenges during CECL implementation. We look back at nine years of account-level credit card data, starting with 2008, over a time period encompassing the bulk of the Great Recession as well as several years of economic recovery. We analyze the performance of the CECL framework under plausible assumptions about allocations of future payments to existing credit card loans, a key implementation element. Our analysis focuses on three major themes: defaults, balances, and credit loss. Our analysis indicates that allowances are significantly impacted by specific payment allocation assumptions as well as downturn economic conditions. We also compare projected allowances with realized credit losses and observe a significant divergence resulting from the revolving nature of credit card portfolios. We extend our analysis across segments of the portfolio with different risk profiles. Interestingly, less risky segments of the portfolio are proportionally more impacted by specific payment assumptions and downturn economic conditions. We also analyze the impact of macroeconomic forecast error and find that it can be substantial and can be impacted by CECL implementation design features. Overall, our findings suggest that the effect of the new allowance framework on a specific credit card portfolio will depend critically on its risk profile. Thus, our findings should be interpreted qualitatively, rather than quantitatively. Finally, the goal is to gain a better understanding of the sensitivity of allowances to plausible variations in assumptions about the allocation of future payments to present credit card loans. Thus, we do not offer specific best practice guidance. |
| Keywords: | expected credit losses; allowances; unconditionally cancellable; revolving credit; credit loss |
| JEL: | G21 G28 M41 |
| Date: | 2020–03–02 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedpwp:87558&r=all |
| By: | Ceballos, Francisco; Kramer, Berber |
| Abstract: | Production risk is pervasive in agriculture, yet smallholder farmers lack access to quality insurance. This is due to asymmetric information in markets for indemnity insurance, and high basis risk, limited trust, and poor understanding of index-based insurance. Digital technologies can help overcome these challenges by improving crop monitoring and yield prediction, allowing insurers to provide products that move towards indemnity insurance. Although this can potentially improve demand, it also comes at the risk of introducing adverse selection. We analyze this trade-off by eliciting willingness to pay for both index-based insurance and picture-based insurance (PBI) for visible crop damage through incentivized auctions with smallholder farmers in northwestern India. Participants reveal a higher willingness to pay for PBI than for index-based coverage. Although at commercial rates, demand remains low for either product, PBI improves demand at the subsidized premium levels maintained by India’s national insurance scheme. Moreover, we find no evidence of adverse selection. We conclude that digital technologies can facilitate a shift from index-based insurance to indemnity insurance. By reducing basis risk and strengthening trust and understanding, this can improve demand for crop insurance. |
| Keywords: | INDIA, SOUTH ASIA, ASIA, willingness to pay, technology, crop insurance, mobile equipment, risk, mobile telephones, photography, insurance, assessment, innovation, losses, digital technology, mobile technology, adverse selection, G22 Insurance, Insurance Companies, Actuarial Studies, O13 Economic Development: Agriculture, Natural Resources, Energy, Environment, Other Primary Product, O16 Economic Development: Financial Markets, Saving and Capital Investment, Corporate Finance and Governance, Q14 Agricultural Finance, |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:fpr:ifprid:1890&r=all |
| By: | Dräger, Lena; Nguyen, Duc Binh Benno; Prokopczuk, Marcel; Sibbertsen, Philipp |
| Abstract: | This paper examines long memory volatility in international stock markets. We show that long memory volatility is widespread in a panel dataset of eighty-two countries and that the degree of memory in the panel can be related to macroeconomic variables such as short- and long-run interest rates and unemployment. Moreover, we find that developed economies possess longer memory in volatility than emerging and frontier countries and that stock market jumps are negatively correlated with long memory of volatility. Overall, our results provide some evidence of a link between stock market uncertainty and macroeconomic conditions, which is prevalent across a large range of countries. |
| Keywords: | International; Long Memory; Volatility |
| JEL: | G15 C22 F30 F40 |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-667&r=all |
| By: | Mendez Ramos,Fabian |
| Abstract: | This paper studies future poverty, inequality, and shared prosperity outcomes using a panel data set with 150 countries over 1980-2014. The findings suggest that global extreme poverty will decrease in absolute and relative terms in the period 2015-2030. However, absolute poverty is likely to increase by 2030 in resource-output oriented countries and economies with low rates of output per capita growth. Countries with high growth rates of output are expected to achieve poverty levels below 3 percent by 2030. Global and country aggregations show a decrease in income inequality by 2030; though, significant downside risks could increase wealth inequality in high- and low-output growth economies by 2030. Substantial uncertainty, as measured by the variability of the simulated outcomes, exists on shared prosperity gaps across the studied country typologies. |
| Keywords: | Inequality,Poverty Reduction Strategies,Industrial Economics,Economic Theory&Research,Economic Growth,Commodity Risk Management,Global Environment |
| Date: | 2019–05–02 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wbrwps:8841&r=all |