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on Risk Management |
| By: | Qiuqi Wang; Ruodu Wang; Ricardas Zitikis |
| Abstract: | The Expected Shortfall (ES) is one of the most important regulatory risk measures in finance, insurance, and statistics, which has recently been characterized via sets of axioms from perspectives of portfolio risk management and statistics. Meanwhile, there is large literature on insurance design with ES as an objective or a constraint. A visible gap is to justify the special role of ES in insurance and actuarial science. To fill this gap, we study characterization of risk measures induced by efficient insurance contracts, i.e., those that are Pareto optimal for the insured and the insurer. One of our major results is that we characterize a mixture of the mean and ES as the risk measure of the insured and the insurer, when contracts with deductibles are efficient. Characterization results of other risk measures, including the mean and distortion risk measures, are also presented by linking them to different sets of contracts. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.00314&r= |
| By: | Saeed Marzban; Erick Delage; Jonathan Yumeng Li |
| Abstract: | Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider dynamic risk measures. However, all current implementations either employ a static risk measure that violates time consistency, or are based on traditional dynamic programming solution schemes that are impracticable in problems with a large number of underlying assets (due to the curse of dimensionality) or with incomplete asset dynamics information. In this paper, we extend for the first time a famous off-policy deterministic actor-critic deep reinforcement learning (ACRL) algorithm to the problem of solving a risk averse Markov decision process that models risk using a time consistent recursive expectile risk measure. This new ACRL algorithm allows us to identify high quality time consistent hedging policies (and equal risk prices) for options, such as basket options, that cannot be handled using traditional methods, or in context where only historical trajectories of the underlying assets are available. Our numerical experiments, which involve both a simple vanilla option and a more exotic basket option, confirm that the new ACRL algorithm can produce 1) in simple environments, nearly optimal hedging policies, and highly accurate prices, simultaneously for a range of maturities 2) in complex environments, good quality policies and prices using reasonable amount of computing resources; and 3) overall, hedging strategies that actually outperform the strategies produced using static risk measures when the risk is evaluated at later points of time. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.04001&r= |
| By: | Alexander Groh; Cay Oertel |
| Abstract: | Securitized Real Estate is known for extreme return movements in secondary capital markets. Accordingly, the Gaussian assumption of normality has been empirically falsified for public equity positions including REITs. Thus, literature has intensified the research on Extreme Value Theory and Generalized Pareto Distributions for exceedances above a certain threshold. These observations in the tail of the return distribution can be empirically characterized by scale and shape parameters. Nonetheless, descriptive statistics are regularly enriched by inductive models to provide explanatory power of independent covariates. The central aim of the present study is the establishment of statistical significance between explanatory covariates to model scale and shape parameters statistically across time. Time-variant parameterization appears to be highly important, since empirical literature has shown volatility clustering, implying differently volatile market phases. |
| Keywords: | Extreme Value Theory; Generalized Additive Models; Risk Management; Tail Risk |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_37&r= |
| By: | Luitel, Kishor P.; Adhikari, Shyam |
| Keywords: | Risk and Uncertainty, Agricultural and Food Policy, Agricultural Finance |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea21:312926&r= |
| By: | Alejandro Drexler; Thomas B. King |
| Abstract: | When firms approach distress, whether they engage in asset substitution (risk shifting) or rebuild equity (risk management) may depend on their access to capital markets. The property-casualty insurance industry has two features that make it ideal for testing this hypothesis: (1) the main losses for insurers are exogenous events like hurricanes that provide a strong instrument for financial distress; and (2) many insurers are organized as mutual companies, which cannot issue stock. Consistent with the importance of capital constraints, stock companies issue new equity following a negative shock, while mutual companies increase the riskiness of their investment portfolios. |
| Keywords: | Risk shifting; insurance; reinsurance; capital structure |
| JEL: | G22 G32 |
| Date: | 2021–09–02 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedhwp:93018&r= |
| By: | Carsten Lausberg; Tobias Schultheiß |
| Abstract: | GIF, the Society of Property Researchers in Germany, recently published a catalog of key measures for managing real estate risk. It contains a comprehensive list with standardized descriptions along with sample applications. While some of the figures are well-known to everybody working in this field, several others are uncommon either for academic researchers or for practitioners. The prime goal of the catalog is to contribute to the professionalization of real estate risk management. The goal of our paper is different: We want to present the catalog to the scientific community and to suggest paths for further research on risk measures. One direction could be the systematic investigation of heuristic risk measures such as the Herfindahl-Hirschmann-Index (for concentration risk) or the weighted average remaining lease term (for vacancy risk). Another course could be the standardization of risk measures in such a way that benchmarking becomes possible for real estate investors. |
| Keywords: | benchmarking; real estate; risk measure |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_113&r= |
| By: | Erasmo Giambona; Anil Kumar; Gordon M. Phillips |
| Abstract: | We study how risk management through hedging impacts firms and competition among firms in the life insurance industry - an industry with over 7 Trillion in assets and over 1,000 private and public firms. We show that firms that are likely to face costly external finance increase hedging after staggered state-level financial reform that reduces the costs of hedging. Post reform impacted firms have lower risk and fewer negative income shocks. Product market competition is also impacted. Firms that previously are more likely to face costly external finance, lower price, increase policy sales and increase their market share post reform. The results are consistent with hedging allowing firms that face potential costly financial distress to decrease risk and become more competitive. |
| JEL: | D0 D22 D43 G22 G28 G31 G32 G33 |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29207&r= |
| By: | Söhnke M. Bartram; Jürgen Branke; Mehrshad Motahari (Cambridge Judge Business School, University of Cambridge) |
| Abstract: | Artificial intelligence (AI) has a growing presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and returns forecasts and under more complex constraints. Trading algorithms utilize AI to devise novel trading signals and execute trades with lower transaction costs, and AI improves risk modelling and forecasting by generating insights from new sources of data. Finally, robo-advisors owe a large part of their success to AI techniques. At the same time, the use of AI can create new risks and challenges, for instance as a result of model opacity, complexity, and reliance on data integrity. |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:jbs:wpaper:20202001&r= |
| By: | Stephen Lee |
| Abstract: | The theoretical model of Merton (1987) predicts a positive relation between idiosyncratic risk and returns, for investors who are not fully diversified. Investors in the private real estate market hold particularly undiversified portfolios due to lack of information, transaction costs, liquidity requirements, taxes, etc.. Therefore, it is especially important to see whether private real estate returns are significantly related to idiosyncratic risk. The lack of research in the private real estate market due to the lack of high frequency data needed to construct measures of idiosyncratic risk. To overcome this problem we use the cross sectional variance (CSV) as our measure of idiosyncratic risk, as it is calculably at any frequency and is model free. Using monthly data for 35 real estate market segments over the period 1987:1 to 2019:12 the results indicate that CSV is highly correlated with idiosyncratic risk measured by the average variance of errors from the market model. Therefore, we consider CSV a good proxy for idiosyncratic risk in the private real estate market. Then using quantile regression methodology we find that there is a positive relationship in the higher quantiles but an insignificant negative effect in the low quantiles for average market returns 1, 3, 6, 9 and 12 months ahead. Lastly, we find high idiosyncratic risk portfolios produce significantly higher returns than low idiosyncratic risk portfolios. The results indicate that idiosyncratic risk significantly affects private real estate returns. The study therefore provides important implications for investors and fund managers, as well as researchers. |
| Keywords: | cross section variance; Idiosyncratic risk; monthly data; quantile regressions |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_219&r= |
| By: | Jiamin Yu |
| Abstract: | Since Claude Shannon founded Information Theory, information theory has widely fostered other scientific fields, such as statistics, artificial intelligence, biology, behavioral science, neuroscience, economics, and finance. Unfortunately, actuarial science has hardly benefited from information theory. So far, only one actuarial paper on information theory can be searched by academic search engines. Undoubtedly, information and risk, both as Uncertainty, are constrained by entropy law. Today's insurance big data era means more data and more information. It is unacceptable for risk management and actuarial science to ignore information theory. Therefore, this paper aims to exploit information theory to discover the performance limits of insurance big data systems and seek guidance for risk modeling and the development of actuarial pricing systems. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.03541&r= |
| By: | Felix Brandt; Carsten Lausberg |
| Abstract: | This paper explores the stock returns of German real estate companies from 1991 to 2019. In contrast to previous studies we use a forward-looking approach and alternative risk measures to better reflect investor behavior. At first the paper constructs a traditional five-factor Arbitrage Pricing Theory model to measure the sensitivity of real estate stock returns to the stock, bond and real estate markets as well as to inflation and the overall economic development. The analysis shows that German real estate stocks are more impacted by changes in the economy and the stock market than by changes in the real estate market. We then apply a pseudo ge-ometric Brownian motion concept combined with a Monte Carlo simulation to model future asset prices. Value at risk and conditional value at risk are used to quantify the downside risk for an investor in listed real estate. The paper finds that listed real estate is less risky than the general stock market, which is in line with our expectations. |
| Keywords: | Asset Pricing; Germany; Monte Carlo Simulation; real estate |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_86&r= |
| By: | Julius O. Campeci\~no |
| Abstract: | This manuscript presents a mathematical relationship between the coefficient of variation (CV) and security investment risk, defined herein as the probability of occurrence of negative returns. The equation suggests that there exists a range of CV where risk is zero and that risk never crosses 50% for securities with positive returns. It was found that at least for stocks, there is a strong correlation between CV and stock performance when the CV is derived from annual returns calculated for each month (as opposed to using, for example, only annual returns based on end-of-the-year closing prices). It was found that a low nonnegative CV of up to ~ 1.0 (~ 15% risk) correlates well with strong and consistent stock performance. Beyond this CV, share price growth gradually shows plateaus and/or large peaks and valleys. The efficient frontier was also re-examined based on CV analysis, and it was found that the direct relationship between risk and return (e.g., high risk, high return) is only robust when the correlation of returns among the portfolio securities is sufficiently negative. At low negative to positive correlation, the efficient frontier hypothesis breaks down, and risk analysis based on CV becomes an important consideration. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.03977&r= |
| By: | Louis Johner; Martin Hoesli |
| Abstract: | We assess the benefits of diversifying a portfolio of commercial real estate assets across gateway and non-gateway markets, a topic of significant relevance to institutional investors. Using simulation analysis and property-level data for the U.S., we compare various performance metrics for portfolios containing buildings in gateway markets only, both in gateway and non-gateway markets, and in non-gateway markets only, respectively. Our results suggest that the risk-adjusted performance is similar across types of markets. Gateway markets have higher appreciation and total returns, while non-gateway markets exhibit higher income returns even after accounting for capital expenditures. Downside risk appears to be slightly greater for gateway markets than for non-gateway markets; however, full drawdown and recovery lengths tend to be shorter for gateway markets. Our results further show evidence of momentum in appreciation returns, although no differences exist across types of markets. Income returns also appear to affect real estate pricing significantly, this effect being stronger for non-gateway than for gateway markets. By considering a large spectrum of performance metrics in a realistic investment setting, the results of the paper should provide investors with valuable information when allocating funds across gateway and non-gateway markets. The paper also provides important insights regarding how best to define gateway markets. |
| Keywords: | commercial real estate; Downside risk; Gateway Markets; Risk-Adjusted Performance |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_214&r= |
| By: | Natalia A. Van Heerden; Juan B. Cabral; Nadia Luczywo |
| Abstract: | In recent years, cryptocurrencies have gone from an obscure niche to a prominent place, with investment in these assets becoming increasingly popular. However, cryptocurrencies carry a high risk due to their high volatility. In this paper, criteria based on historical cryptocurrency data are defined in order to characterize returns and risks in different ways, in short time windows (7 and 15 days); then, the importance of criteria is analyzed by various methods and their impact is evaluated. Finally, the future plan is projected to use the knowledge obtained for the selection of investment portfolios by applying multi-criteria methods. |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.00130&r= |
| By: | Cho, Thummim |
| Abstract: | Using data on asset pricing anomalies, I test the idea that the act of arbitrage turns “alphas” into “betas”: Assets with high initial abnormal returns attract more arbitrage and covary endogenously more with systematic factors that arbitrage capital is exposed to. This channel explains the exposures of 40 anomaly portfolios to aggregate funding liquidity shocks and arbitrageur wealth portfolio shocks. My results highlight that financial intermediaries that act as asset market arbitrageurs not only price assets given risks, but also actively shape these risks through their trades. |
| Keywords: | endogenous risk; factor beta; financial intermediaries; arbitrage; asset pricing anomalies; Paul Woolley Centre at the LSE |
| JEL: | G11 G12 G23 |
| Date: | 2020–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:102085&r= |
| By: | Bastien Lextrait |
| Abstract: | Small and Medium Size Enterprises (SMEs) are critical actors in the fabric of the economy. Their growth is often limited by the difficulty in obtaining fi nancing. Basel II accords enforced the obligation for banks to estimate the probability of default of their obligors. Currently used models are limited by the simplicity of their architecture and the available data. State of the art machine learning models are not widely used because they are often considered as black boxes that cannot be easily explained or interpreted. We propose a methodology to combine high predictive power and powerful explainability using various Gradient Boosting Decision Trees (GBDT) implementations such as the LightGBM algorithm and SHapley Additive exPlanation (SHAP) values as post-prediction explanation model. SHAP values are among the most recent methods quantifying with consistency the impact of each input feature over the credit score. This model is developed and tested using a nation-wide sample of French companies, with a highly unbalanced positive event ratio. The performances of GBDT models are compared with traditional credit scoring algorithms such as Support Vector Machine (SVM) and Logistic Regression. LightGBM provides the best performances over the test sample, while being fast to train and economically sound. Results obtained from SHAP values analysis are consistent with previous socio-economic studies, in that they can pinpoint known influent economical factors among hundreds of other features. Providing such a level of explainability to complex models may convince regulators to accept their use in automated credit scoring, which could ultimately benefi t both borrowers and lenders. |
| Keywords: | Credit scoring, SMEs, Machine Learning, Gradient Boosting, Interpretability |
| JEL: | C53 C63 M21 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:drm:wpaper:2021-25&r= |
| By: | Salisu, Afees; Ogbonna, Ahamuefula; Oloko, Tirimisiyu |
| Abstract: | This study examines the effect of a pandemic-induced uncertainty on cryptocurrencies (specifically, Bitcoin, Ethereum and Ripple). It employs a predictive model by Westerlund and Narayan (2012, 2015) to examine the predictability of a pandemic-induced uncertainty as a predictor, as well as the forecast performance of our predictive model for cryptocurrency returns. We examine the role of asymmetry in uncertainty and the sensitivity of our results to alternative measures of uncertainty due to pandemics, using the recently developed Global Fear Index (GFI) by Salisu and Akanni (2020). Our results indicate that cryptocurrencies could act as hedge against uncertainty due to pandemics, albeit with reduced hedging effectiveness in the COVID-19 period. Accounting for asymmetry is found to improve the predictability and forecast performance of the model, which indicates that failure to account for asymmetry in modeling the effect of a pandemic-induced uncertainty on cryptocurrency may lead to incorrect conclusion. The results seem to be sensitive to the choice of measure of pandemic-induced uncertainty. |
| Keywords: | COVID-19; Cryptocurrency; Distributed Lag Model; Pandemic; Uncertainty |
| JEL: | C5 G1 |
| Date: | 2020–07–12 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:109597&r= |
| By: | Juselius, Mikael; Tarashev, Nikola A. |
| Abstract: | While corporate credit losses have been low since the start of the Covid-19 pandemic, their future evolution is quite uncertain. Using a forecasting model with a solid track record, we find that the baseline scenario ("expected losses") is benign up to 2024. This is due to policy support measures that have kept debt service costs low. However, high indebtedness, built up when the pandemic impaired real activity, suggests increased tail risks: plausible deviations from the baseline scenario ("unexpected losses") feature ballooning corporate insolvencies. Taken at face value, the low expected loss forecasts are consistent with low bank provisions, whereas the high unexpected loss forecasts call for substantial capital. |
| Keywords: | COVID-19 |
| JEL: | E44 E47 E65 G17 G21 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bofecr:32021&r= |
| By: | Marcos Escobar-Anel; Maximilian Gollart; Rudi Zagst |
| Abstract: | This paper develops the first closed-form optimal portfolio allocation formula for a spot asset whose variance follows a GARCH(1,1) process. We consider an investor with constant relative risk aversion (CRRA) utility who wants to maximize the expected utility from terminal wealth under a Heston and Nandi (2000) GARCH (HN-GARCH) model. We obtain closed formulas for the optimal investment strategy, the value function and the optimal terminal wealth. We find the optimal strategy is independent of the development of the risky asset, and the solution converges to that of a continuous-time Heston stochastic volatility model, albeit under additional conditions. For a daily trading scenario, the optimal solutions are quite robust to variations in the parameters, while the numerical wealth equivalent loss (WEL) analysis shows good performance of the Heston solution, with a quite inferior performance of the Merton solution. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.00433&r= |
| By: | Maureen Cowhey; Jane E. Ihrig; Cindy M. Vojtech; Gretchen C. Weinbach |
| Abstract: | Banks need sufficient liquidity—cash and other assets that may be easily and immediately converted into cash—to meet their financial obligations, such as when households withdraw deposits or businesses tap credit lines. One key takeaway from the Global Financial Crisis of 2007–09 was that continuity of bank intermediation is particularly important in times of stress to limit pressure on the financial system, and that banks need to consistently maintain sufficient liquidity to achieve that outcome. |
| Date: | 2021–08–30 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2021-08-30-1&r= |
| By: | Riëtte Carstens; Julia Freybote |
| Abstract: | Risk awareness generally increases during times of uncertainty. REITs’ riskiness has been known to be affected by its debt and capital structure decisions. We investigate if the text in REIT financial statements can improve the risk-related information environment by predicting credit behavior. Using a REIT-specific dictionary we extract the net positive tone from REIT financial statements from the first quarter of 1997 to the fourth quarter of 2017. We then use a panel VAR to assess whether the net positive REIT tone can predict credit variables such as leverage, cash and short term investments, unsecured debt and secured debt. In addition, we investigate the predictive value of tone for high and low growth firms based on its book-to-market value. Overall, our findings suggest that 1) REIT-specific tone predicts firm credit behavior and 2) firms differ in their reporting behavior based on their book-to-market value. Firms with profitable growth opportunities, characterized by a low book-to-market value, increase (decrease) leverage and decrease (increase) cash and short term investments following an increase (decrease) in the net positive tone. On the other hand, REITs with limited growth opportunities (high book-to-market value) display an inverse relationship between tone and leverage. Our study contributes to the literature on REIT text analysis specifically within the context of extracting risk related information. Furthermore, our study may have value for REIT practitioners including investors, analysts, financiers, and credit rating agencies who are sensitive to firm risk. |
| Keywords: | Information Environment; REITs; Risk; Textual Tone |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_85&r= |
| By: | Mark Whitmeyer |
| Abstract: | This paper investigates stochastic continuous time contests with a twist: the designer requires that contest participants incur some cost to submit their entries. When the designer wishes to maximize the (expected) performance of the top performer, a strictly positive submission fee is optimal. When the designer wishes to maximize total (expected) performance, either the highest submission fee or the lowest submission fee is optimal. |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2108.13506&r= |
| By: | Shotaro Watanabe; Gianluca Marcato; Bing Zhu |
| Abstract: | This paper investigates the relationship between strategic default in the US residential mortgage market and household portfolio composition after the Great Recession in 2007. Following the definition of strategic default proposed by Gerardi et al. (2018), we find that in addition to the well-known ‘Double Triggers’ – negative equity and payment ability – households’ portfolio composition can also affect their strategic default decision. Holding a larger amount of non-housing durable assets increases the probability of strategic default, while owning more liquid assets can reduce it through two channels: portfolio rebalancing and relative cost of default. |
| Keywords: | Household Finance; Mortgage; Negative Equity; Strategic Default |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_203&r= |
| By: | Benjamin Avanzi; Gregory Clive Taylor; Melantha Wang |
| Abstract: | In this paper, we first introduce a simulator of cases estimates of incurred losses, called `SPLICE` (Synthetic Paid Loss and Incurred Cost Experience). In three modules, case estimates are simulated in continuous time, and a record is output for each individual claim. Revisions for the case estimates are also simulated as a sequence over the lifetime of the claim, in a number of different situations. Furthermore, some dependencies in relation to case estimates of incurred losses are incorporated, particularly recognizing certain properties of case estimates that are found in practice. For example, the magnitude of revisions depends on ultimate claim size, as does the distribution of the revisions over time. Some of these revisions occur in response to occurrence of claim payments, and so `SPLICE` requires input of simulated per-claim payment histories. The claim data can be summarized by accident and payment "periods" whose duration is an arbitrary choice (e.g. month, quarter, etc.) available to the user. `SPLICE` is built on an existing simulator of individual claim experience called `SynthETIC` available on CRAN (Avanzi et al., 2021a,b), which offers flexible modelling of occurrence, notification, as well as the timing and magnitude of individual partial payments. This is in contrast with the incurred losses, which constitute the additional contribution of `SPLICE`. The inclusion of incurred loss estimates provides a facility that almost no other simulators do. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.04058&r= |
| By: | Eric Djeutcha (UMa - University of Maroua); Jules Sadefo Kamdem (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier) |
| Abstract: | We price options so as to take into account the existence of memory (short or long) characterizing the stochastic processes that generate prices, volatility and interest rates. In particular, we propose a model for Bull Spread options in a Mixed Modified Fractional Hull-White-Vasicek stochastic volatility and stochastic interest rate model. We propose a specific Bull Spread Vulnerable option pricing based on MMFHWV model. |
| Date: | 2021–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03327512&r= |