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on Risk Management |
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Issue of 2019‒12‒09
nineteen papers chosen by |
| By: | Vaclav Broz (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic); Evzen Kocenda (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic) |
| Abstract: | We analyze link between mortgage-related regulatory penalties levied on banks and the level of systemic risk in the U.S. banking industry. We employ a frequency decomposition of volatility spillovers to draw conclusions about system-wide risk transmission with short-, medium-, and long-term dynamics. We find that after the possibility of a penalty is first announced to the public, long-term systemic risk among banks tends to increase. In contrast, a settlement with regulatory authorities leads to a decrease in the long-term systemic risk. Our analysis is relevant both to authorities imposing penalties as well as to those in charge of financial stability. |
| Keywords: | Bank, financial stability, global financial crisis, mortgage, penalty, systemic risk |
| JEL: | C14 C58 G14 G21 G28 K41 |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2019_25&r=all |
| By: | Jozef Baruník (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic); Matěj Nevrla (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic; Department of Econometrics, IITA, The Czech Academy of Sciences, Pod Vodarenskou Vezi 4, 182 00, Prague, Czech Republic) |
| Abstract: | We examine how extreme market risks are priced in the cross-section of asset returns at various horizons. Based on the frequency decomposition of covariance between indicator functions, we define the quantile cross-spectral beta of an asset capturing tail-specific as well as horizon-, or frequency-specific risks. Further, we work with two notions of frequency-specific extreme market risks. First, we define tail market risk that captures dependence between extremely low market as well as asset returns. Second, extreme market volatility risk is characterized by dependence between extremely high increments of market volatility and extremely low asset return. Empirical findings based on the datasets with long enough history, 30 Fama-French Industry portfolios, and 25 Fama-French portfolios sorted on size and book-to-market support our intuition. Results suggest that both frequency-specific tail market risk and extreme volatility risks are significantly priced and our five-factor model provides improvement over specifications considered by previous literature. |
| Keywords: | Asset pricing, downside risk, frequency-specific risk, tail risk |
| JEL: | C21 C58 G12 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2019_10&r=all |
| By: | Barbora Máková (Institute of Economic Studies, Faculty of Social Science, Charles University, Prague, Czech Republic; Credit Benchmark, London, UK) |
| Abstract: | This study provides new insights into banks' credit risk models by exploring features of their credit risk estimates and assessing practicalities of transition matrix estimation and related assumptions. Using a unique dataset of internal credit risk estimates from twelve global A-IRB banks, covering monthly observations on 20,000 North American and EU large corporates over the 2015-2018 time period, the study empirically tests the widely used assumptions of the Markovian property and time homogeneity at a larger scale than previously documented in the literature. The results show that internal credit risk estimates do not satisfy these assumptions as they show evidence of both path-dependency and time heterogeneity. In addition, contradicting previous findings on credit rating agency data, banks tend to revert their rating actions. |
| Keywords: | Risk management, credit risk, transition matrices |
| JEL: | C12 G12 G21 G32 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2019_03&r=all |
| By: | Eduardo Abi Jaber (UP1 - Université Paris 1, Panthéon-Sorbonne - UP1 - Université Panthéon-Sorbonne - Pres Hesam) |
| Abstract: | We establish an explicit expression for the conditional Laplace transform of the integrated Volterra Wishart process in terms of a certain resolvent of the covariance function. The core ingredient is the derivation of the conditional Laplace transform of general Gaussian processes in terms of Fredholm's determinant and resolvent. Furthermore , we link the characteristic exponents to a system of non-standard infinite dimensional matrix Riccati equations. This leads to a second representation of the Laplace transform for a special case of convolution kernel. In practice, we show that both representations can be approximated by either closed form solutions of conventional Wishart distributions or finite dimensional matrix Riccati equations stemming from conventional linear-quadratic models. This allows fast pricing in a variety of highly flexible models, ranging from bond pricing in quadratic short rate models with rich autocorrelation structures, long range dependence and possible default risk, to pricing basket options with covariance risk in multivariate rough volatility models. |
| Keywords: | Gaussian processes,Wishart processes,Fredholm's determinant,quadratic short rate models,rough volatility models |
| Date: | 2019–11–17 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-02367200&r=all |
| By: | Xiao, Tim |
| Abstract: | The incremental risk charge (IRC) is a new regulatory requirement from the Basel Committee in response to the recent financial crisis. Notably few models for IRC have been developed in the literature. This paper proposes a methodology consisting of two Monte Carlo simulations. The first Monte Carlo simulation simulates default, migration, and concentration in an integrated way. Combining with full re-valuation, the loss distribution at the first liquidity horizon for a subportfolio can be generated. The second Monte Carlo simulation is the random draws based on the constant level of risk assumption. It convolutes the copies of the single loss distribution to produce one year loss distribution. The aggregation of different subportfolios with different liquidity horizons is addressed. Moreover, the methodology for equity is also included, even though it is optional in IRC. |
| Date: | 2018–08–16 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:y43dx&r=all |
| By: | Xiao, Tim |
| Abstract: | This article presents a new model for valuing financial contracts subject to credit risk and collateralization. Examples include the valuation of a credit default swap (CDS) contract that is affected by the trilateral credit risk of the buyer, seller and reference entity. We show that default dependency has a significant impact on asset pricing. In fact, correlated default risk is one of the most pervasive threats in financial markets. We also show that a fully collateralized CDS is not equivalent to a risk-free one. In other words, full collateralization cannot eliminate counterparty risk completely in the CDS market. |
| Date: | 2019–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:osf:arabix:86xhw&r=all |
| By: | Lerby Ergun |
| Abstract: | Financial markets can experience sudden and extreme downward movements. Investors are highly concerned about the performance of their assets in such scenarios. Some assets perform badly in a downturn in the market; others have milder reactions. The assets that react mildly are desirable and should sell at a premium. But determining how reactive individual stocks are to extreme market downturns is a difficult task given the small sample of these events. This paper uses a simple methodology to measure the sensitivity of individual stocks to extreme market movements. I count the number of times the market and the individual stock simultaneously pass their individual extreme threshold. I divide the number of these occurrences by the number of times the market is extreme. This measure can be seen as the probability that the asset value will have an extremely negative reaction when the market experiences an extremely negative episode. By sorting individual stocks based on this measure and analyzing the direction and increase in the average return of the sorted stocks, I measure the compensation investors demand for exposure to this risk. I find that investors demand a 3.5 percent risk premium for investing in a stock with high sensitivity to the market relative to one with low sensitivity. This measure characterizes the riskiness of a stock not captured by existing risk factors. |
| Keywords: | Asset Pricing; Econometric and statistical methods |
| JEL: | C14 G11 G12 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:19-46&r=all |
| By: | Pancost, N. Aaron; Robatto, Roberto |
| Abstract: | We study optimal capital requirement regulation in a dynamic quantitative model in which nonfinancial firms, as well as households, hold deposits. Firms hold deposits for precautionary reasons and to facilitate the acquisition of production inputs. Our theoretical analysis identifies a novel general equilibrium channel that operates through firms’ deposits and mitigates the cost of increasing capital requirements. We calibrate our model and find that the optimal capital requirement is 18.7% but only 13.6% in a comparable model in which only households hold deposits. Our novel channel accounts for most of the difference. JEL Classification: E21, G21, G32 |
| Keywords: | capital requirements, deposit insurance, idiosyncratic risk, safe assets |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:srk:srkwps:2019104&r=all |
| By: | Sheikh Rabiul Islam; William Eberle; Sheikh K. Ghafoor; Sid C. Bundy; Douglas A. Talbert; Ambareen Siraj |
| Abstract: | In the area of credit risk analytics, current Bankruptcy Prediction Models (BPMs) struggle with (a) the availability of comprehensive and real-world data sets and (b) the presence of extreme class imbalance in the data (i.e., very few samples for the minority class) that degrades the performance of the prediction model. Moreover, little research has compared the relative performance of well-known BPM's on public datasets addressing the class imbalance problem. In this work, we apply eight classes of well-known BPMs, as suggested by a review of decades of literature, on a new public dataset named Freddie Mac Single-Family Loan-Level Dataset with resampling (i.e., adding synthetic minority samples) of the minority class to tackle class imbalance. Additionally, we apply some recent AI techniques (e.g., tree-based ensemble techniques) that demonstrate potentially better results on models trained with resampled data. In addition, from the analysis of 19 years (1999-2017) of data, we discover that models behave differently when presented with sudden changes in the economy (e.g., a global financial crisis) resulting in abrupt fluctuations in the national default rate. In summary, this study should aid practitioners/researchers in determining the appropriate model with respect to data that contains a class imbalance and various economic stages. |
| Date: | 2019–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1911.09858&r=all |
| By: | Yurchenko, Yurii |
| Abstract: | The study examines the impact of macroeconomic factors on the expected credit losses of a financial instrument related to changes in the value of collateral. The author has developed a method of calculating this impact on the basis of econometric models, as well as simulated the effect on expected credit losses and reserves on a financial instrument. Based on the proposed approach, appropriate models have been constructed based on the data of the US and Ukrainian economies for the maximum period available, taking into account the adequacy of the data. In particular, it has been shown that applying the methodology of adjusting collateral value to macroeconomic factors can lead to a reduction of the reserve according to the requirements of the regulator, i.e. from the financial institution's point of view it is possible to release some of the funds additionally. |
| Keywords: | LGD, Collateral value, OLS, Credit risk, valuation, GLM |
| JEL: | C22 G21 G32 |
| Date: | 2019–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:97135&r=all |
| By: | Gruener, Sven; Hirschauer, Norbert; Krüger, Felix |
| Abstract: | We compare three procedures for eliciting individual risk attitudes: Holt-and-Laury (2002), Eckel-and-Grossman (2002), and the general willingness-to-take-risks question of the German socio-economic panel (Dohmen et al. 2011). Using a within-subject design, we carry out a classroom experiment with students who are enrolled in the degree programs Physics, Computer Sciences, Agricultural Sciences, Law, and History. We find that the risk attitudes as measured by the three procedures diverge substantially. This poses a serious challenge to the validity of these measurement instruments. Reconnoitering the room for improvement in the elicitation of risk atti-tudes, we discuss potential reasons for this divergence such as flawed design, overhasty interpretations, and confusion on the conceptual level. |
| Date: | 2018–08–21 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:7bwyq&r=all |
| By: | Tzougas, G.; Hoon, W. L.; Lim, J. M. |
| Abstract: | This paper presents the Negative Binomial-Inverse Gaussian regression model for approximating the number of claims as an alternative to mixed Poisson regression models that have been widely used in various disciplines including actuarial applications. The Negative Binomial-Inverse Gaussian regression model can be considered as a plausible model for highly dispersed claim count data and this is the first time that it is used in a statistical or actuarial context. The main achievement is that we propose a quite simple Expectation-Maximization type algorithm for maximum likelihood estimation of the model. Finally, a real data application using motor insurance data is examined and both the a priori and a posteriori, or Bonus-Malus, premium rates resulting from the Negative Binomial-Inverse Gaussian model are calculated via the net premium principle and compared to those determined by the Negative Binomial Type I and the Poisson-Inverse Gaussian regression models that have been traditionally used for a priori and a posteriori ratemaking. |
| Keywords: | Negative binomial-inverse Gaussian regression model; EM algorithm; Motor third party liability insurance; Ratemaking |
| JEL: | E6 |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:101728&r=all |
| By: | Xiao, Tim |
| Abstract: | This paper attempts to assess the economic significance and implications of collateralization in different financial markets, which is essentially a matter of theoretical justification and empirical verification. We present a comprehensive theoretical framework that allows for collateralization adhering to bankruptcy laws. As such, the model can back out differences in asset prices due to collateralized counterparty risk. This framework is very useful for pricing outstanding defaultable financial contracts. By using a unique data set, we are able to achieve a clean decomposition of prices into their credit risk factors. We find empirical evidence that counterparty risk is not overly important in credit-related spreads. Only the joint effects of collateralization and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of financial contracts. We also analyze the difference between cleared and OTC markets. |
| Date: | 2018–06–18 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:zw6xq&r=all |
| By: | Dominika Kolcunová (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic; Czech National Bank, Na Prikope 28, 115 03 Prague 1, Czech Republic); Simona Malovaná (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic; Czech National Bank, Na Prikope 28, 115 03 Prague 1, Czech Republic) |
| Abstract: | This paper studies the impact of higher additional capital requirements on loan growth to private sector of banks in the Czech Republic. The empirical results indicate that there is a negative effect of higher additional capital requirements on loan growth of banks with relatively low capital surplus. In addition, the results confirm that the relationship between capital surplus and loan growth is important also in the period of stable capital requirements, i.e. it does not serve only as an intermediate channel of higher additional capital requirements. |
| Keywords: | Bank lending, banks’ capital surplus, regulatory capital requirements |
| JEL: | C22 E32 G21 G28 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2019_05&r=all |
| By: | Grozavu, Adrian; MIHAI, Florin Constantin |
| Abstract: | Changes in the contemporary world materialized in particular through population growth and mobility, urbanization, and economic expansion also result in an increased exposure of people and assets to extreme events and impose, implicitly, adequate management of induced risks. The occurrence of natural and anthropogenic risk phenomena, known as hazards, puts a heavy tribute on disaster-sensitive human communities regardless of their level of development. The magnitude of the disasters and their increasing frequency and severity imply the need for their approach by the entire world community and for global action. Knowledge of risks becomes a sine qua condition in carrying out impact studies, risk prevention plans, spatial planning plans, and, in general, a condition for effective management of natural resources or sustainable development projects. |
| Date: | 2018–10–09 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:9t6mw&r=all |
| By: | Moura, Guilherme V.; Ruiz, Esther; Santos, André A. P. |
| Abstract: | Modelling and forecasting high dimensional covariance matrices is a key challenge in data-richenvironments involving even thousands of time series since most of the available models sufferfrom the curse of dimensionality. In this paper, we challenge some popular multivariate GARCH(MGARCH) and Stochastic Volatility (MSV) models by fitting them to forecast the conditionalcovariance matrices of financial portfolios with dimension up to 1000 assets observed daily over a30-year time span. The time evolution of the conditional variances and covariances estimated bythe different models is compared and evaluated in the context of a portfolio selection exercise. Weconclude that, in a realistic context in which transaction costs are taken into account, modelling thecovariance matrices as latent Wishart processes delivers more stable optimal portfolio compositionsand, consequently, higher Sharpe ratios. |
| Keywords: | Stochastic Volatility; Risk-Adjusted Return; Portfolio Turnover; Minimum-Variance Portfolio; Garch; Covariance Forecasting |
| JEL: | G17 C53 |
| Date: | 2019–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:29291&r=all |
| By: | Shiyi Wang |
| Abstract: | Volatile international capital flows increase the risk of financial crises and reduce economic growth. The theoretical literature predicts that financial globalization will make capital flows more volatile. Importantly, the deepening of financial globalization has led to the emergence of the global financial cycle, which makes taming capital flows even more challenging. It is important to measure capital flow volatility and examine what factors affect it. In this paper, I estimate the time-varying capital flow volatility of 39 countries, including both advanced and emerging economies since 2000, and find that bank flows are the most volatile while foreign direct investment flows are the most stable. Panel regressions show that higher local financial development and more volatile and riskier global financial conditions increase capital flow volatility. I also find that there exists a threshold effect: financial volatility and risk in the global financial center are transmitted more strongly to countries that are more financially developed. The impulse responses of state-dependent local projections confirm the threshold effect and indicate that it is stronger for bank flows than for FDI and portfolio flows. These empirical findings provide insights into international capital flow management. |
| JEL: | E44 E52 F32 F36 |
| Date: | 2019–12–02 |
| URL: | http://d.repec.org/n?u=RePEc:jmp:jm2019:pwa945&r=all |
| By: | Yulia V. Milshina (National Research University Higher School of Economics); Daria A. Pavlova (National Research University Higher School of Economics); Konstantin O. Vishnevskiy (National Research University Higher School of Economics) |
| Abstract: | Manufacturing today is undergoing fast and fundamental changes due to the introduction of Industry 4.0 technologies. Still, the effects of their applications on the global economic and social structure (in terms of risks and benefits) are highly uncertain. This paper is aimed to suggest a special methodology for conducting industry foresight based on timelines construction, which reflects future vision of technological development, and wild cards detection, which represents an advanced technique for technological trends risk management. In this way, we analyze the existing practices of timelines building and wild cards analysis for industry purposes in the academic literature, and suggest own methodology for conducting industry foresight by using these tools. To demonstrate its application, we choose a particular industry with great potential for technological innovation and high degree of uncertainty – the textile and apparel industry. Finally, we discuss future development of this industry at a national level in the context of global technological trends. |
| Keywords: | technological trends, timelines construction, wild cards detection, industry foresight, textile and apparel industry |
| JEL: | O14 O32 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:hig:wpaper:105sti2019&r=all |
| By: | Iqbal, Muhammad; Ullah, Raza; Abbas, Azhar; Afil, Sultan; Sadaf, Tahira |
| Abstract: | Agriculture operates in an ever changing environment which makes this sector vulnerable to a number of risks and uncertainties. Among the various risks farmers face, production risks (particularly catastrophic risks) presents the most dominant sources of risks and uncertainties in agriculture. Farmers use available risk management tools to mitigate/minimize the potential adverse impacts of such risks and uncertainties at farm level. This study highlight important factors (internal, external and behavioral factors) affecting risk management decisions at farm level and provide some methodological approaches for quantification of these variables and analyzing their effect on farmers’ decisions of adopting risk management tools. The risk management decisions depend on various factors which can be broadly categorized into internal and external factors. Internal factors include farm (farm size, ownership of land etc.) and farm household characteristics (gender, age, education, income, family size etc.). External factors consist of availability and access to information and credit sources and input/output markets. Besides the internal and external factors, there are some behavioral attributes that also effect farmers’ risk management decisions. Farmers’ risk perceptions and their attitude towards risks are significant factors affecting farmers’ decisions of adopting various risk management tools. Another important aspect of the decision process is the simultaneous adoption of multiple tools at the same time i.e. adoption of one risk management tool may make it more likely to adopt other available risk management tool(s). Various methodological approaches are used to quantify these variables and fetch some meaningful results from the field data. The internal and external factors are relatively easy to measure however eliciting farmers’ perceptions and their risk attitude is tricky and require sound methodological approaches. Perceptions can be recorded using a likert scale and can be processed using a risk matrix while Equally Likely Certainty Equivalent (ELCE) or Toss Method can be used to elicit risk attitude from an economic agent. Similarly the simultaneous adoption effect can be best captured using a multivariate probit/logit model. |
| Keywords: | Farm Management, Risk and Uncertainty |
| Date: | 2018–04 |
| URL: | http://d.repec.org/n?u=RePEc:ags:icfae2:296704&r=all |