|
on Risk Management |
| By: | Ren, Rui; Lu, Meng-Jou; Li, Yingxing; Härdle, Wolfgang |
| Abstract: | The Financial Risk Meter (FRM) is an established mechanism that, based on conditional Value at Risk (VaR) ideas, yields insight into the dynamics of network risk. Originally, the FRM has been composed via Lasso based quantile regression, but we here extend it by incorporating the idea of expectiles, thus indicating not only the tail probability but rather the actual tail loss given a stress situation in the network. The expectile variant of the FRM enjoys several advantages: Firstly, the coherent and multivariate tail risk indicator conditional expectile-based VaR (CoEVaR) can be derived, which is sensitive to the magnitude of extreme losses. Next, FRM index is not restricted to an index compared to the quantile based FRM mechanisms, but can be expanded to a set of systemic tail risk indicators, which provide investors with numerous tools in terms of diverse risk preferences. The power of FRM also lies in displaying FRM distribution across various entities every day. Two distinct patterns can be discovered under high stress and during stable periods from the empirical results in the United States stock market. Furthermore, the framework is able to identify individual risk characteristics and capture spillover effects in a network. |
| Keywords: | expectiles,EVaR,CoEVaR,expectile lasso regression,network analysis,systemicrisk,Financial Risk Meter |
| JEL: | C00 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2021008&r= |
| By: | Christian Haddad; Lars Hornuf |
| Abstract: | We study the impact fintech startups have on the performance and the default risk of traditional financial institutions. We find a positive relationship between fintech startup formations and incumbent institutions’ performance for the period from 2005 to 2018 and a large sample of financial institutions from 87 countries. We further analyze the link between fintech startup formations and the default risk of traditional financial institutions. Fintech startup formations decreases stock return volatility of incumbent institutions and decreases the systemic risk exposure of financial institutions. Our findings indicate that the development of fintech startups should be monitored very closely by legislators and financial supervisory authorities, because fintechs not only have a positive effect on the financial sector’s performance, but can also improve financial stability relative to the status quo. |
| Keywords: | fintech, bank performance, default risk, financial stability |
| JEL: | K00 L26 O30 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9050&r= |
| By: | Lech A. Grzelak |
| Abstract: | Every x-adjustment in the so-called xVA financial risk management framework relies on the computation of exposures. Considering thousands of Monte Carlo paths and tens of simulation steps, a financial portfolio needs to be evaluated numerous times during the lifetime of the underlying assets. This is the bottleneck of every simulation of xVA. In this article, we explore numerical techniques for improving the simulation of exposures. We aim to decimate the number of portfolio evaluations, particularly for large portfolios involving multiple, correlated risk factors. The usage of the Stochastic Collocation (SC) method, together with Smolyaks sparse grid extension, allows for a significant reduction in the number of portfolio evaluations, even when dealing with many risk factors. The proposed model can be easily applied to any portfolio and size. We report that for a realistic portfolio comprising linear derivatives, the expected reduction in the portfolio evaluations may exceed 6000 times, depending on the dimensionality and the required accuracy. We give illustrative examples and examine the method with realistic multi-currency portfolios. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.14319&r= |
| By: | Buckley , Ross (University of New South Wales); Avgouleas, Emilios (University of Edinburgh); Arner , Douglas (University of Hong Kong) |
| Abstract: | Fragility that periodically erupts into a full-blown financial crisis appears to be an integral feature of market-based financial systems in spite of the emergence of sophisticated risk management tools and regulatory systems. If anything, the increased frequency of modern crises underscores how difficult it is to diversify away systemic risk and that perceptions of perfectly stable financial systems are normally flawed, even if the source of the next crisis remains well concealed to the expert eye. Although it is impossible to forecast a financial crisis with a high degree of accuracy and certainty, earlier crises always leave lessons useful in preparation for future crises, from whatever source. It is thus clear that the best way to deal with preventing and addressing major financial crises is to build the defenses of the financial system, including effective institutions, while at the same time trying to identify potential sources of crisis. We should take every opportunity to learn and work to build stronger and more effective financial systems. This paper compares and contrasts the three major crises of the past 3 decades, both to distill the lessons to be learned from them and to identify what more can be done to strengthen our financial systems. As the world addresses the financial impact of the COVID-19 pandemic, the centrality of these lessons is clear. |
| Keywords: | Asian financial crisis; COVID-19 crisis; eurozone debt crisis; financial stability; global financial crisis; systemic risk |
| JEL: | F31 F34 G01 G32 |
| Date: | 2020–06–19 |
| URL: | http://d.repec.org/n?u=RePEc:ris:adbewp:0615&r= |
| By: | Christian Brownlees; Gu{\dh}mundur Stef\'an Gu{\dh}mundsson |
| Abstract: | This paper establishes oracle inequalities for the prediction risk of the empirical risk minimizer for large-dimensional linear regression. We generalize existing results by allowing the data to be dependent and heavy-tailed. The analysis covers both the cases of identically and heterogeneously distributed observations. Our analysis is nonparametric in the sense that the relationship between the regressand and the regressors is assumed to be unknown. The main results of this paper indicate that the empirical risk minimizer achieves the optimal performance (up to a logarithmic factor) in a dependent data setting. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.12127&r= |
| By: | Bahareh Afhami; Mohsen Rezapour; Mohsen Madadi; Vahed Maroufy |
| Abstract: | In this paper, we are concerned with the optimization of a dynamic investment portfolio when the securities which follow a multivariate Merton model with dependent jumps are periodically invested and proceed by approximating the Condition-Value-at-Risk (CVaR) by comonotonic bounds and maximize the expected terminal wealth. Numerical studies as well as applications of our results to real datasets are also provided. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.11594&r= |
| By: | Narayan Ganesan; Bernhard Hientzsch |
| Abstract: | Predicting future values at risk (fVaR) is an important problem in finance. They arise in the modelling of future initial margin requirements for counterparty credit risk and future market risk VaR. One is also interested in derived quantities such as: i) Dynamic Initial Margin (DIM) and Margin Value Adjustment (MVA) in the counterparty risk context; and ii) risk weighted assets (RWA) and Capital Value Adjustment (KVA) for market risk. This paper describes several methods that can be used to predict fVaRs. We begin with the Nested MC-empirical quantile method as benchmark, but it is too computationally intensive for routine use. We review several known methods and discuss their novel applications to the problem at hand. The techniques considered include computing percentiles from distributions (Normal and Johnson) that were matched to parametric moments or percentile estimates, quantile regressions methods, and others with more specific assumptions or requirements. We also consider how limited inner simulations can be used to improve the performance of these techniques. The paper also provides illustrations, results, and visualizations of intermediate and final results for the various approaches and methods. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.11768&r= |
| By: | Zhibin Niu; Junqi Wu; Dawei Cheng; Jiawan Zhang |
| Abstract: | Financial regulatory agencies are struggling to manage the systemic risks attributed to negative economic shocks. Preventive interventions are prominent to eliminate the risks and help to build a more resilient financial system. Although tremendous efforts have been made to measure multi-risk severity levels, understand the contagion behaviors and other risk management problems, there still lacks a theoretical framework revealing what and how regulatory intervention measurements can mitigate systemic risk. Here we demonstrate regshock, a practical visual analytical approach to support the exploration and evaluation of financial regulation measurements. We propose risk-island, an unprecedented risk-centered visualization algorithm to help uncover the risk patterns while preserving the topology of financial networks. We further propose regshock, a novel visual exploration and assessment approach based on the simulation-intervention-evaluation analysis loop, to provide a heuristic surgical intervention capability for systemic risk mitigation. We evaluate our approach through extensive case studies and expert reviews. To our knowledge, this is the first practical systemic method for the financial network intervention and risk mitigation problem; our validated approach potentially improves the risk management and control capabilities of financial experts. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.11863&r= |
| By: | Philipp Eisenhauer (University of Bonn); Janos Gabler (University of Bonn and IZA); Lena Janys (University of Bonn) |
| Abstract: | The ex-ante evaluation of policies using structural econometric models is based on estimated parameters as a stand-in for the truth. This practice ignores uncertainty in the counterfactual policy predictions of the model. We develop a generic approach that deals with parametric uncertainty using uncertainty sets and frames model-informed policymaking as a decision problem under uncertainty. The seminal human capital investment model by Keane and Wolpin (1997) provides us with a well-known, influential, and empirically-grounded test case. We document considerable uncertainty in their policy predictions and highlight the resulting policy recommendations from using different formal rules on decision-making under uncertainty. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:ajk:ajkdps:082&r= |
| By: | Pashchenko, Svetlana; Porapakkarm, Ponpoje |
| Abstract: | How does the value of life affect annuity demand? To address this question, we construct a portfolio choice problem with three key features: i) agents have access to life-contingent assets, ii) they always prefer living to dying, iii) agents have non-expected utility preferences. We show that as utility from being alive increases, annuity demand decreases (increases) if agents are more (less) averse to risk rather than to intertemporal fluctuations. Put differently, if people prefer early resolution of uncertainty, they are less interested in annuities when the value of life is high. Our findings have two important implications. First, we get better understanding of the well-known annuity puzzle. Second, we argue that the observed low annuity demand provides evidence that people prefer early rather than late resolution of uncertainty. |
| Keywords: | annuities, value of a statistical life, portfolio choice problem, life-contingent assets, longevity insurance |
| JEL: | D91 G11 G22 |
| Date: | 2021–04–15 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:107378&r= |
| By: | Anton Koshelev |
| Abstract: | This paper aims at solving FX market volatility modeling problem and finding the most becoming approach to this task. Validity of two competing approaches, classical econometric generalized conditional heteroscedasticity and mathematical (singular spectrum analysis and dynamical systems stability analysis) are tested on major currency pairs (EUR/USD, USD/JPY, GBP/USD) and unique high-frequency USD/RUB data. The study shows that both mathematical tools, understudied in econometric discourse, have a great potential in scope of discussed problematic, as for all experiments covered in this research, both of them show promising results. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.14190&r= |
| By: | Dirk van Straaten (Paderborn University); René Fahr (Paderborn University) |
| Abstract: | Ambiguity aversion guides decision makers to choose a risky rather than an ambiguous prospect, a pattern that is not always beneficial. For example, even nowadays, private pensions often build on savings accounts, which are risky prospects with known probabilities, instead of stocks as the former ensure safe returns with fixed interest rates. In comparison, expected returns of stocks, which are ambiguous prospects with unknown probabilities, are significantly higher. This study aims at facilitating a better understanding of ambiguity aversion and suggests measures to improve decision-making. In our experiment, subjects are confronted with either decisions under risk or decisions under ambiguity. Controlling for risk attitudes, we estimate category weights in both domains and find significant differences, which indicate the present of ambiguity aversion. Contrary to our predictions on the amplifying effect of multiple sources of ambiguity, we find that category weights of ambiguity and risk converge each other when a second source of ambiguity is implemented. That is, we point out another option to deal with ambiguity when people have to choose between risky and ambiguous prospects. Instead of minimizing ambiguity, the introduction of a second source of ambiguity might help to compare alternatives with less biases through ambiguity aversion. |
| Keywords: | Unknown source credibility, Risk, Ambiguity aversion, Uncertainty, Customer ratings |
| JEL: | C91 D01 D81 |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:pdn:dispap:73&r= |
| By: | Darren Shannon; Grigorios Fountas |
| Abstract: | We present an alternative approach to the forecasting of motor vehicle collision rates. We adopt an oft-used tool in mathematical finance, the Heston Stochastic Volatility model, to forecast the short-term and long-term evolution of motor vehicle collision rates. We incorporate a number of extensions to the Heston model to make it fit for modelling motor vehicle collision rates. We incorporate the temporally-unstable and non-deterministic nature of collision rate fluctuations, and introduce a parameter to account for periods of accelerated safety. We also adjust estimates to account for the seasonality of collision patterns. Using these parameters, we perform a short-term forecast of collision rates and explore a number of plausible scenarios using long-term forecasts. The short-term forecast shows a close affinity with realised rates (95% accuracy). The long-term scenarios suggest that modest targets to reduce collision rates (1.83% annually) and targets to reduce the fluctuations of month-to-month collision rates (by half) could have significant benefits for road safety. The median forecast in this scenario suggests a 50% fall in collision rates, with 75% of simulations suggesting that an effective change in collision rates is observed before 2044. The main benefit the model provides is eschewing the necessity for setting unreasonable safety targets that are often missed. Instead, the model presents the effects that modest and achievable targets can have on road safety over the long run, while incorporating random variability. Examining the parameters that underlie expected collision rates will aid policymakers in determining the effectiveness of implemented policies. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.11461&r= |
| By: | Maximilian Blesch; Philipp Eisenhauer |
| Abstract: | Economists often estimate a subset of their model parameters outside the model and let the decision-makers inside the model treat these point estimates as-if they are correct. This practice ignores model ambiguity and opens the door for model misspecification and post-decision disappointment. We develop a framework to explore and evaluate decision rules that explicitly account for the uncertainty in the first step estimation and assess their performance in a decision-theoretic setting. We show how to operationalize our analysis by studying a stochastic dynamic investment model where the decision-maker takes ambiguity about the model's transition dynamics directly into account. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.12573&r= |
| By: | Candelon, Bertrand (Université catholique de Louvain, LIDAM/LFIN, Belgium); Fuerst, Franz; Hasse, Jean-Baptiste |
| Abstract: | Real estate, despite its spatial fixity, is subject to considerable cross-border investment flows. However, it may be surmised that the diversification potential of international real esta te investments dwindles if markets become increasingly interlinked. Building on a unique dataset of direct real estate markets covering 16 OECD countries over the period 1999-2018, we compare country-level and sector-level diversification potential. We apply a relative Sharpe ratio loss approach and develop a modified version of this measure, relying on the modified Value-at-Risk, which is robust to non-normality. Using a studentized circular blockbootstrap procedure, robust confidence intervals for both measures are built. This new diversification test provides investors and analysts with a valuable tool as it delivers both estimates and robust significance levels. The empirical findings broadly reveal that international diversification strategies outperform sectoral diversification of real estate assets. |
| Keywords: | Portfolio diversification ; Real estate markets |
| Date: | 2021–02–12 |
| URL: | http://d.repec.org/n?u=RePEc:ajf:louvlf:2021001&r= |
| By: | Wang, Bingling; Li, Yingxing; Härdle, Wolfgang |
| Abstract: | K-means clustering is one of the most widely-used partitioning algorithm in cluster analysis due to its simplicity and computational efficiency, but it may not provide ideal clustering results when applying to data with non-spherically shaped clusters. By considering the asymmetrically weighted distance, We propose the K-expectile clustering and search the clusters via a greedy algorithm that minimizes the within cluster τ -variance. We provide algorithms based on two schemes: the fixed τ clustering, and the adaptive τ clustering. Validated by simulation results, our method has enhanced performance on data with asymmetric shaped clusters or clusters with a complicated structure. Applications of our method show that the fixed τ clustering can bring some flexibility on segmentation with a decent accuracy, while the adaptive τ clustering may yield better performance. |
| Keywords: | clustering,expectiles,asymmetric quadratic loss,image segmentation |
| JEL: | C00 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2021003&r= |
| By: | Musaab Mousa; Saeed Nosratabadi; Judit Sagi; Amir Mosavi |
| Abstract: | Analyzing the financial benefit of marketing is still a critical topic for both practitioners and researchers. Companies consider marketing costs as a type of investment and expect this investment to be returned to the company in the form of profit. On the other hand, companies adopt different innovative strategies to increase their value. Therefore, this study aims to test the impact of marketing investment on firm value and systematic risk. To do so, data related to four Arabic emerging markets during the period 2010-2019 are considered, and firm share price and beta share are considered to measure firm value and systematic risk, respectively. Since a firm's ownership concentration is a determinant factor in firm value and systematic risk, this variable is considered a moderated variable in the relationship between marketing investment and firm value and systematic risk. The findings of the study, using panel data regression, indicate that increasing investment in marketing has a positive effect on the firm value valuation model. It is also found that the ownership concentration variable has a reinforcing role in the relationship between marketing investment and firm value. It is also disclosed that it moderates the systematic risk aligned with the monitoring impact of controlling shareholders. This study provides a logical combination of governance-marketing dimensions to interpret performance indicators in the capital market. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.14301&r= |
| By: | Gärtner, Manja (DIW Berlin); Tinghög, Gustav (Linköping University); Västfjäll, Daniel (Linköping University) |
| Abstract: | We test the effects of dual processing differences in both individual traits and decision states on risk taking. In an experiment with a large representative sample (N = 1,832), we vary whether risky choices are induced to be based on either emotion or reason, while simultaneously measuring individual decision-making traits. Our results show that decision-making traits are strong and robust determinants of risk taking: a more intuitive trait is associated with more risk taking, while a more deliberative trait is associated with less risk taking. Experimentally induced states, on the other hand, have no effect on risk taking. A test of state-trait interactions shows that the association between an intuitive trait and risk taking becomes weaker in the emotion-inducing state and in the loss domain. In contrast, the association between a deliberative trait and risk taking is stable across states. These findings highlight the importance of considering state-trait interactions when using dual processing theories to predict individual differences in risk taking. |
| Keywords: | risk preferences; intuition; emotion; reason; experiment; |
| JEL: | C91 D81 D91 |
| Date: | 2019–10–23 |
| URL: | http://d.repec.org/n?u=RePEc:rco:dpaper:195&r= |
| By: | Luciana Juvenal; Paulo Santos Monteiro |
| Abstract: | We consider the canonical trade model with heterogeneous firms, love for variety and trade costs, and integrate it in the consumption CAPM model. This yields a structural gravity equation that includes an additional factor related to risk premia. Empirical evidence based on firm-level data confirms the importance of cross-sectional heterogeneity in risk and time-varying risk premia to shape bilateral trade flows. The structural gravity model augmented to account for fluctuations in risk premia offers a compelling explanation for trade collapses during abrupt economic downturns. |
| Keywords: | Risk premia, Gravity equation, Trade collapse |
| JEL: | F12 F41 F44 |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:yor:yorken:21/02&r= |
| By: | Erniel B. Barrios; Paolo Victor T. Redondo |
| Abstract: | Contagion arising from clustering of multiple time series like those in the stock market indicators can further complicate the nature of volatility, rendering a parametric test (relying on asymptotic distribution) to suffer from issues on size and power. We propose a test on volatility based on the bootstrap method for multiple time series, intended to account for possible presence of contagion effect. While the test is fairly robust to distributional assumptions, it depends on the nature of volatility. The test is correctly sized even in cases where the time series are almost nonstationary. The test is also powerful specially when the time series are stationary in mean and that volatility are contained only in fewer clusters. We illustrate the method in global stock prices data. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.14412&r= |