|
on Risk Management |
| By: | Ren, Rui; Althof, Michael; Härdle, Wolfgang Karl |
| Abstract: | Cryptocurrencies are gaining momentum in investor attention, are about to become a new asset class, and may provide a hedging alternative against the risk of devaluation of fiat currencies following the COVID-19 crisis. In order to provide a thorough understanding of this new asset class, risk indicators need to consider tail risk behaviour and the interdependencies between the cryptocurrencies not only for risk management but also for portfolio optimization. The tail risk network analysis framework proposed in the paper is able to identify individual risk characteristics and capture spillover effect in a network topology. Finally we construct tail event sensitive portfolios and consequently test the performance during an unforeseen COVID-19 pandemic. |
| Keywords: | Cryptocurrencies,Network Dynamics,Portfolio Optimization,Quantile Regression,Systemic Risk,Financial Risk Meter |
| JEL: | C00 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2020028&r=all |
| By: | Spilak, Bruno; Härdle, Wolfgang Karl |
| Abstract: | Tail risk protection is in the focus of the financial industry and requires solid mathematical and statistical tools, especially when a trading strategy is derived. Recent hype driven by machine learning (ML) mechanisms has raised the necessity to display and understand the functionality of ML tools. In this paper, we present a dynamic tail risk protection strategy that targets a maximum predefined level of risk measured by Value-At-Risk while controlling for participation in bull market regimes. We propose different weak classifiers, parametric and non-parametric, that estimate the exceedance probability of the risk level from which we derive trading signals in order to hedge tail events. We then compare the different approaches both with statistical and trading strategy performance, finally we propose an ensemble classifier that produces a meta tail risk protection strategy improving both generalization and trading performance. |
| JEL: | C00 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2020015&r=all |
| By: | Eduardo Abi Jaber (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, UP1 UFR27 - Université Paris 1 Panthéon-Sorbonne - UFR Mathématiques & Informatique - UP1 - Université Paris 1 Panthéon-Sorbonne) |
| 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: | Wishart processes,Gaussian processes,Fredholm's determinant,quadratic short rate models,rough volatility models |
| Date: | 2020–06–17 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:hal-02367200&r=all |
| By: | Ni, Xinwen; Härdle, Wolfgang Karl; Xie, Taojun |
| Abstract: | Cryptocurrencies’ values often respond aggressively to major policy changes, but none of the existing indices informs on the market risks associated with regulatory changes. In this paper, we quantify the risks originating from new regulations on FinTech and cryptocurrencies (CCs), and analyse their impact on market dynamics. Specifically, a Cryptocurrency Regulatory Risk IndeX (CRRIX) is constructed based on policy-related news coverage frequency. The unlabeled news data are collected from the top online CC news platforms and further classified using a Latent Dirichlet Allocation model and Hellinger distance. Our results show that the machine-learning-based CRRIX successfully captures major policy-changing moments. The movements for both the VCRIX, a market volatility index, and the CRRIX are synchronous, meaning that the CRRIX could be helpful for all participants in the cryptocurrency market. The algorithms and Python code are available for research purposes on www.quantlet.de. |
| Keywords: | Cryptocurrency,Regulatory Risk,Index,LDA,News Classification |
| JEL: | C45 G11 G18 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2020013&r=all |
| By: | Andrés Alonso (Banco de España); José Manuel Carbó (Banco de España) |
| Abstract: | In this paper we study the performance of several machine learning (ML) models for credit default prediction. We do so by using a unique and anonymized database from a major Spanish bank. We compare the statistical performance of a simple and traditionally used model like the Logistic Regression (Logit), with more advanced ones like Lasso penalized logistic regression, Classification And Regression Tree (CART), Random Forest, XGBoost and Deep Neural Networks. Following the process deployed for the supervisory validation of Internal Rating-Based (IRB) systems, we examine the benefits of using ML in terms of predictive power, both in classification and calibration. Running a simulation exercise for different sample sizes and number of features we are able to isolate the information advantage associated to the access to big amounts of data, and measure the ML model advantage. Despite the fact that ML models outperforms Logit both in classification and in calibration, more complex ML algorithms do not necessarily predict better. We then translate this statistical performance into economic impact. We do so by estimating the savings in regulatory capital when using ML models instead of a simpler model like Lasso to compute the risk-weighted assets. Our benchmark results show that implementing XGBoost could yield savings from 12.4% to 17% in terms of regulatory capital requirements under the IRB approach. This leads us to conclude that the potential benefits in economic terms for the institutions would be significant and this justify further research to better understand all the risks embedded in ML models. |
| Keywords: | machine learning, credit risk, prediction, probability of default, IRB system |
| JEL: | C45 C38 G21 |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:2105&r=all |
| By: | Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan, Ibadan, Nigeria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa); Qiang Ji (Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China) |
| Abstract: | In this study, we examine the predictive value of tail risks for oil returns using the longest possible data available for the modern oil industry, i.e., 1859-2020. The Conditional Autoregressive Value at Risk (CAViaR) of Engle & Manganelli (2004) is employed to generate the tail risks for both 1% and 5% VaRs across four variants (Adaptive, Symmetric absolute value, Asymmetric slope and Indirect GARCH) of the CAViaR with the best variant obtained using the Dynamic Quantile test (DQ) test and %Hits. Overall, our proposed predictive model for oil returns that jointly accommodates tail risks associated with the oil market and US financial market improves the out-of-sample forecast accuracy of oil returns in contrast with a benchmark (random walk) model as well as a one-predictor model with own tail risk only. Our results have important implications for academicians, investors and policymakers. |
| Keywords: | Oil returns, Tail risks, Forecasting, Advanced equity markets |
| JEL: | C22 C53 G15 Q02 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202120&r=all |
| By: | Calo, Silvia (Central Bank of Ireland); Emter, Lorenz (Central Bank of Ireland); Galstyan, Vahagn (Central Bank of Ireland) |
| Abstract: | In 2020Q1, emerging market economies (EMEs) experienced significant outflows of portfolio investment capital. Irish-domiciled funds contributed to these portfolio outflows through sales of EME securities in response to heightened redemptions. Consistent with previous evidence around the sensitivity of Irish-resident fund flows to changes in global risk appetite, the retrenchment by Irish-domiciled funds in 2020Q1 was greater for debt, rather than equity, securities. Relative to their initial positions, the retrenchment was bigger for hedge funds, suggesting leverage may have acted as an amplifier of asset sales. Overall, though, Irish-domiciled funds also retrenched by less than might have been expected, given the historical relationship between measures of global risk aversion and fund flows to EMEs. In part, this may be due to the fact that, in the face of large redemptions, Irish funds also sold more liquid advanced economy securities. This points to potential common creditor effects acting as a transmission channel of shocks. The analysis also finds that EME countries with stronger fundamentals were somewhat cushioned from the retrenchment. Finally, valuation effects were strongest vis-à-vis EMEs with more flexible exchange rate regimes, suggesting a major role for currency depreciations in driving the observed reduction in positions. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:cbi:fsnote:9/fs/20&r=all |
| By: | Wang, Weining; Wooldridge, Jeffrey M.; Xu, Mengshan |
| Abstract: | Modelling dynamic conditional heteroscedasticity is the daily routine in time series econometrics. We propose a weighted conditional moment estimation to potentially improve the eciency of the QMLE (quasi maximum likelihood estimation). The weights of conditional moments are selected based on the analytical form of optimal instruments, and we nominally decide the optimal instrument based on the third and fourth moments of the underlying error term. This approach is motivated by the idea of general estimation equations (GEE). We also provide an analysis of the eciency of QMLE for the location and variance parameters. Simulations and applications are conducted to show the better performance of our estimators. |
| JEL: | C00 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2020021&r=all |
| By: | Ahmed, Hanan (Tilburg University, Center For Economic Research); Einmahl, John (Tilburg University, Center For Economic Research); Zhou, Chen |
| Keywords: | Asymptotic normality; extreme value index; semi-supervised inference; tail dependence; variance reduction |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiucen:ad83a546-fb09-408e-80cc-b4b2db763d37&r=all |
| By: | Christoph Bertsch; Mike Mariathasan |
| Abstract: | We study optimal bank leverage and recapitalization in general equilibrium when the supply of specialized investment capital is imperfectly elastic. Assuming incomplete insurance against capital shortfalls and segmented financial markets, ex-ante leverage is inefficiently high, leading to excessive insolvencies during systemic capital shortfall events. Recapitalizations by equity issuance are individually and socially optimal. Additional frictions can turn asset sales individually but not necessarily socially optimal. Our results hold for different bankruptcy protocols and we offer testable predictions for banks' capital structure management. Our model provides a rationale for macroprudential capital regulation that does not require moral hazard or informational asymmetries. |
| Keywords: | Bank capital, recapitalization, macroprudential regulation, incomplete markets, financial market segmentation, constrained inefficiency |
| JEL: | D5 D6 G21 G28 |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:923&r=all |
| By: | T. Carraro; Edoardo Gaffeo (Department of Economics and Management, Universita' degli Studi di Trento (Italy).); Marco Gallegati (Dipartimento di Scienze Economiche e Sociali - Universita' Politecnica delle Marche) |
| Abstract: | In this paper we present a model where frictions in the supervision process may set the stage for strategic complementarities among banks. We derive the conditions for strategic complementarities in the behavior of banks in a banking system in which the supervisory authority has a budget constraint on the resources to allocate for monitoring, and supervision is costly for banks. In such a framework, the goal of macroprudential policies consists in simultaneously restraining the incentive of banks in extending risky loans, without forcing the system towards a corner solution where all or none of the banks provide credit. We point out that the countercyclical bu er is a proper tool to reduce the number of banks issuing a higher amount of credit during booms, while a loan-support-program can increase the number of banks issuing higher credit during downturns. |
| Keywords: | Banking Crisis; Strategic complementarity; Macroprudential Supervision |
| JEL: | C49 E44 G32 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:anc:wpaper:452&r=all |
| By: | Charles Smith; Peter Van Tassel |
| Abstract: | Investors can buy volatility hedges on the stock market using variance swaps or VIX futures. One motivation for hedging volatility is its negative relationship with the stock market. When volatility increases, stock returns tend to decline contemporaneously, a result known as the leverage effect. In this post, we measure the cost of volatility hedging by decomposing the prices of variance swaps and VIX futures into volatility forecasts and estimates of expected returns (“equity volatility term premia”) from January 1996 to June 2020. |
| Keywords: | variance swaps; VIX futures; term structure; variance risk premium; return predictability |
| JEL: | G1 |
| Date: | 2021–02–03 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednls:89758&r=all |
| By: | Charles Smith; Peter Van Tassel |
| Abstract: | Can option traders take a square root? Surprisingly, maybe not. This post shows that VIX futures prices exhibit significant deviations from their option-implied upper bounds—the square root of variance swap forward rates—thus violating the law of one price, a fundamental concept in economics and finance. The deviations widen during periods of market stress and predict the returns of VIX futures. Just as the stock market struggles with multiplication, the equity volatility market appears unable to take a square root at times. |
| Keywords: | variance swaps; VIX futures; term structure; variance risk premium; return predictability |
| JEL: | G1 |
| Date: | 2021–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednls:89616&r=all |
| By: | Henri Fraisse; Matthias Laporte |
| Abstract: | Taking advantage of granular data we measure the change in bank capital requirement resulting from the implementation of AI techniques to predict corporate defaults. For each of the largest banks operating in France we design an algorithm to build pseudo-internal models of credit risk management for a range of methodologies extensively used in AI (random forest, gradient boosting, ridge regression, deep learning). We compare these models to the traditional model usually in place that basically relies on a combination of logistic regression and expert judgement. The comparison is made along two sets of criterias capturing : the ability to pass compliance tests used by the regulators during on-site missions of model validation (i), and the induced changes in capital requirement (ii). The different models show noticeable differences in their ability to pass the regulatory tests and to lead to a reduction in capital requirement. While displaying a similar ability than the traditional model to pass compliance tests, neural networks provide the strongest incentive for banks to apply AI models for their internal model of credit risk of corporate businesses as they lead in some cases to sizeable reduction in capital requirement.[1] |
| Keywords: | Artificial Intelligence, Credit Risk, Regulatory Requirement |
| JEL: | C4 C55 G21 K35 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:809&r=all |
| By: | Inoue, Atsushi; Kilian, Lutz |
| Abstract: | We derive the Bayes estimator of vectors of structural VAR impulse responses under a range of alternative loss functions. We also derive joint credible regions for vectors of impulse responses as the lowest posterior risk region under the same loss functions. We show that conventional impulse response estimators such as the posterior median response function or the posterior mean response function are not in general the Bayes estimator of the impulse response vector obtained by stacking the impulse responses of interest. We show that such pointwise estimators may imply response function shapes that are incompatible with any possible parameterization of the underlying model. Moreover, conventional pointwise quantile error bands are not a valid measure of the estimation uncertainty about the impulse response vector because they ignore the mutual dependence of the responses. In practice, they tend to understate substantially the estimation uncertainty about the impulse response vector. |
| Keywords: | Loss function,joint inference,median response function,mean response function,modal model,posterior risk |
| JEL: | C22 C32 C52 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfswop:650&r=all |
| By: | Matteo Burzoni; Marco Frittelli; Federico Zorzi |
| Abstract: | In this note we consider a system of financial institutions and study systemic risk measures in the presence of a financial market and in a robust setting, namely, where no reference probability is assigned. We obtain a dual representation for convex robust systemic risk measures adjusted to the financial market and show its relation to some appropriate no-arbitrage conditions. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.02920&r=all |
| By: | Shota Imaki; Kentaro Imajo; Katsuya Ito; Kentaro Minami; Kei Nakagawa |
| Abstract: | Deep hedging (Buehler et al. 2019) is a versatile framework to compute the optimal hedging strategy of derivatives in incomplete markets. However, this optimal strategy is hard to train due to action dependence, that is, the appropriate hedging action at the next step depends on the current action. To overcome this issue, we leverage the idea of a no-transaction band strategy, which is an existing technique that gives optimal hedging strategies for European options and the exponential utility. We theoretically prove that this strategy is also optimal for a wider class of utilities and derivatives including exotics. Based on this result, we propose a no-transaction band network, a neural network architecture that facilitates fast training and precise evaluation of the optimal hedging strategy. We experimentally demonstrate that for European and lookback options, our architecture quickly attains a better hedging strategy in comparison to a standard feed-forward network. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.01775&r=all |
| By: | Suss, Joel (Bank of England); Bholat, David (Bank of England); Gillespie, Alex (London School of Economics and Political Science); Reader, Tom (London School of Economics and Political Science) |
| Abstract: | Existing research has largely relied on employee surveys to measure organisational culture despite the significant shortcomings of this approach. We use multiple, unobtrusive sources of data to gain rich insights into bank culture without ever having to ask employees to ‘show us your culture’. Our measure is based on 20 individual indicators from six different sources, including information on internal fraud cases, customer complaints, and the quality of regulatory submissions. We use this data to investigate the hypothesised relationship between organisational culture and bank risk. We find robust evidence that poor culture leads to substantially higher risk, demonstrating the importance of bank culture for prudential outcomes. |
| Keywords: | Culture; bank risk; supervision |
| JEL: | G21 G30 L25 |
| Date: | 2021–03–05 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0912&r=all |
| By: | Branka Hadji Misheva; Joerg Osterrieder; Ali Hirsa; Onkar Kulkarni; Stephen Fung Lin |
| Abstract: | Artificial Intelligence (AI) has created the single biggest technology revolution the world has ever seen. For the finance sector, it provides great opportunities to enhance customer experience, democratize financial services, ensure consumer protection and significantly improve risk management. While it is easier than ever to run state-of-the-art machine learning models, designing and implementing systems that support real-world finance applications have been challenging. In large part because they lack transparency and explainability which are important factors in establishing reliable technology and the research on this topic with a specific focus on applications in credit risk management. In this paper, we implement two advanced post-hoc model agnostic explainability techniques called Local Interpretable Model Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to machine learning (ML)-based credit scoring models applied to the open-access data set offered by the US-based P2P Lending Platform, Lending Club. Specifically, we use LIME to explain instances locally and SHAP to get both local and global explanations. We discuss the results in detail and present multiple comparison scenarios by using various kernels available for explaining graphs generated using SHAP values. We also discuss the practical challenges associated with the implementation of these state-of-art eXplainabale AI (XAI) methods and document them for future reference. We have made an effort to document every technical aspect of this research, while at the same time providing a general summary of the conclusions. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.00949&r=all |
| By: | Busch, Christopher; Ludwig, Alexander |
| Abstract: | We extend the canonical income process with persistent and tran- sitory risk to cyclical shock distributions with left-skewness and excess kurtosis. We estimate our income process by GMM for US household data. We find countercyclical variance and procyclical skewness of per- sistent shocks. All shock distributions are highly leptokurtic. The tax and transfer system reduces dispersion and left-skewness. We then show that in a standard incomplete-markets life-cycle model, first, higher- order risk has sizable welfare implications, which depend on risk atti- tudes; second, it matters quantitatively for the welfare costs of cyclical idiosyncratic risk; third, it has non-trivial implications for self-insurance against shocks. |
| Keywords: | Idiosyncratic Income Risk,Cyclical Income Risk,Life-Cycle Model |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:icirwp:3621&r=all |
| By: | Asgharian, Hossein; Christiansen, Charlotte; Hou, Ai Jun; Wang, Weining |
| Abstract: | We propose a bivariate component GARCH-MIDAS model to estimate the long- and short-run components of the variances and covariances. The advantage of our model to the existing DCC-based models is that it uses the same form for both the variances and covariances and that it estimates these moments simultaneously. We apply this model to obtain long- and short-run factor betas for industry test portfolios, where the risk factors are the market, SMB, and HML portfolios. We use these betas in cross-sectional analysis of the risk premia. Among other things, we find that the risk premium related to the short- run market beta is significantly positive, irrespective of the choice of test portfolio. Further, the risk premia for the short-run betas of all the risk factors are significant outside recessions. |
| Keywords: | long-run betas,short-run betas,risk premia,business cycles,component GARCH model,MIDAS |
| JEL: | G12 C58 C51 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2020020&r=all |
| By: | Feng, Yuanhua; Härdle, Wolfgang Karl |
| Abstract: | Penalized spline smoothing of time series and its asymptotic properties are studied. A data-driven algorithm for selecting the smoothing parameter is developed. The proposal is applied to de ne a semiparametric extension of the well-known Spline- GARCH, called a P-Spline-GARCH, based on the log-data transformation of the squared returns. It is shown that now the errors process is exponentially strong mixing with nite moments of all orders. Asymptotic normality of the P-spline smoother in this context is proved. Practical relevance of the proposal is illustrated by data examples and simulation. The proposal is further applied to value at risk and expected shortfall. |
| Keywords: | P-spline smoother,smoothing parameter selection,P-Spline-GARCH,strong mixing,value at risk,expected shortfall |
| JEL: | C14 C51 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2020016&r=all |
| By: | Lubben, Bradley |
| Keywords: | Farm Management, Production Economics |
| Date: | 2020–08–12 |
| URL: | http://d.repec.org/n?u=RePEc:ags:nbaece:309749&r=all |
| By: | Nassim Dehouche |
| Abstract: | A reputation of high volatility accompanies the emergence of Bitcoin as a financial asset. This paper intends to nuance this reputation and clarify our understanding of Bitcoin's volatility. Using daily, weekly, and monthly closing prices and log-returns data going from September 2014 to January 2021, we find that Bitcoin is a prime example of an asset for which the two conceptions of volatility diverge. We show that, historically, Bitcoin allies both high volatility (high Standard Deviation) and high predictability (low Approximate Entropy), relative to Gold and S&P 500. Moreover, using tools from Extreme Value Theory, we analyze the convergence of moments, and the mean excess functions of both the closing prices and the log-returns of the three assets. We find that the closing price of Bitcoin is consistent with a generalized Pareto distribution, when the closing prices of the two other assets (Gold and S&P 500) present thin-tailed distributions. However, returns for all three assets are heavy tailed and second moments (variance, standard deviation) non-convergent. In the case of Bitcoin, lower sampling frequencies (monthly vs weekly, weekly vs daily) drastically reduce the Kurtosis of log-returns and increase the convergence of empirical moments to their true value. The opposite effect is observed for Gold and S&P 500. These properties suggest that Bitcoin's volatility is essentially an intra-day and intra-week phenomenon that is strongly attenuated on a weekly time-scale, and make it an attractive store of value to investors and speculators, but its high standard deviation excludes its use a currency. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.00395&r=all |
| By: | Huber, Christoph (University of Innsbruck); Huber, Juergen; Kirchler, Michael |
| Abstract: | In this paper we investigate how volatility shocks influence investors’ perceptions about a stock's risk, its future development, and investors' investment propensity. We ran artefactual field experiments with two participant pools (finance professionals and students) that had to take investment decisions, differing in (i) the direction of the shock (down, up, straight) and (ii) the presentation format of the time series (prices or returns). We find that finance professionals perceive all shocks to increase risk similarly, while students do not perceive upwardly-trending shocks to increase the riskiness of the stock. Furthermore, we show that investment propensity is negatively associated with the direction of the shock and professionals do not show differences in price forecasts between presentation formats, but students do. |
| Date: | 2021–03–03 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:jr4eb&r=all |
| By: | Wahyudi, Christanto Arief; Arbay, Evi Aryati |
| Abstract: | The COVID-19 pandemic, which is spreading rapidly throughout the world, has seriously harmed many countries, including Indonesia. Many things have been detrimental due to COVID-19, one of which is the economic aspect. This pandemic made it difficult for many debtors to fulfil their credit obligations that led the government to issue a countercyclical policy to provide a stimulus to the national economy. This study aims to determine the impact of OJK Regulation No.48 of 2020 on credit quality and control of banking credit risk in Indonesia. The research method used is descriptive qualitative with a literature approach using secondary data. This OJK regulation regulates economic stimulus through credit restructuring and regulates the implementation of credit risk management in banks. The existence of this regulation can maintain the stability of banking performance by keeping the Non-Performing Loan (NPL) number below 5% and providing a reference for banks in risk management with a model that is relevant to economic conditions during the COVID-19 pandemic. |
| Date: | 2021–03–03 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:ue2bw&r=all |
| By: | Kim Christensen; Mikkel Slot Nielsen; Mark Podolskij |
| Abstract: | In this paper, we develop a penalized realized variance (PRV) estimator of the quadratic variation (QV) of a high-dimensional continuous It\^{o} semimartingale. We adapt the principle idea of regularization from linear regression to covariance estimation in a continuous-time high-frequency setting. We show that under a nuclear norm penalization, the PRV is computed by soft-thresholding the eigenvalues of realized variance (RV). It therefore encourages sparsity of singular values or, equivalently, low rank of the solution. We prove our estimator is minimax optimal up to a logarithmic factor. We derive a concentration inequality, which reveals that the rank of PRV is -- with a high probability -- the number of non-negligible eigenvalues of the QV. Moreover, we also provide the associated non-asymptotic analysis for the spot variance. We suggest an intuitive data-driven bootstrap procedure to select the shrinkage parameter. Our theory is supplemented by a simulation study and an empirical application. The PRV detects about three-five factors in the equity market, with a notable rank decrease during times of distress in financial markets. This is consistent with most standard asset pricing models, where a limited amount of systematic factors driving the cross-section of stock returns are perturbed by idiosyncratic errors, rendering the QV -- and also RV -- of full rank. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.03237&r=all |
| By: | Christoph Huber; Jürgen Huber; Michael Kirchler |
| Abstract: | In this paper we investigate how volatility shocks influence investors' perceptions about a stock's risk, its future development, and investors' investment propensity. We ran artefactual field experiments with two participant pools (finance professionals and students) that had to take investment decisions, differing in (i) the direction of the shock (down, up, straight) and (ii) the presentation format of the time series (prices or returns). We find that finance professionals perceive all shocks to increase risk similarly, while students do not perceive upwardlytrending shocks to increase the riskiness of the stock. Furthermore, we show that investment propensity is negatively associated with the direction of the shock and professionals do not show differences in price forecasts between presentation formats, but students do. |
| Keywords: | Risk perception, experimental finance, finance professionals, volatility shocks |
| JEL: | C91 G11 G41 |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:inn:wpaper:2021-06&r=all |
| By: | Wu, Desheng Dang; Härdle, Wolfgang Karl |
| Abstract: | With growing economic globalization, the modern service sector is in great need of business intelligence for data analytics and computational statistics. The joint application of big data analytics, computational statistics and business intelligence has great potential to make the engineering of advanced service systems more efficient. The purpose of this COST issue is to publish high- quality research papers (including reviews) that address the challenges of service data analytics with business intelligence in the face of uncertainty and risk. High quality contributions that are not yet published or that are not under review by other journals or peer-reviewed conferences have been collected. The resulting topic oriented special issue includes research on business intelligence and computational statistics, data-driven financial engineering, service data analytics and algorithms for optimizing the business engineering. It also covers implementation issues of managing the service process, computational statistics for risk analysis and novel theoretical and computational models, data mining algorithms for risk management related business applications. |
| Keywords: | Data Analytics,Business Intelligence Systems |
| JEL: | C00 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2020002&r=all |
| By: | Yanhong Chen; Zachary Feinstein |
| Abstract: | The relationship between set-valued risk measures for processes and vectors on the optional filtration is investigated. The equivalence of risk measures for processes and vectors and the equivalence of their penalty function formulations are provided. In contrast with scalar risk measures, this equivalence requires an augmentation of the set-valued risk measures for processes. We utilize this result to deduce a new dual representation for risk measures for processes in the set-valued framework. Finally, the equivalence of multiportfolio time consistency between set-valued risk measures for processes and vectors are provided; to accomplish this, an augmented definition for multiportfolio time consistency of set-valued risk measures for processes is proposed. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.00905&r=all |
| By: | Ayten Kahya; Bhaskar Krishnamachari; Seokgu Yun |
| Abstract: | In the wake of financial crises, stablecoins are gaining adoption among digital currencies. We discuss how stablecoins help reduce the volatility of cryptocurrencies by surveying different types of stablecoins and their stability mechanisms. We classify different approaches to stablecoins in three main categories i) fiat or asset backed, ii) crypto-collateralized and iii) algorithmic stablecoins, giving examples of concrete projects in each class. We assess the relative tradeoffs between the different approaches. We also discuss challenges associated with the future of stablecoins and their adoption, their adoption and point out future research directions. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.01340&r=all |
| By: | Ahmed, Hanan (Tilburg University, School of Economics and Management); Einmahl, John (Tilburg University, School of Economics and Management); Zhou, Chen |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiutis:ad83a546-fb09-408e-80cc-b4b2db763d37&r=all |
| By: | Gaetan Bakalli (University of Geneva); Stéphane Guerrier (University of Geneva); Olivier Scaillet (University of Geneva and Swiss Finance Institute) |
| Abstract: | We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second pass delivers risk premia estimates to predict equity excess returns. Our Monte Carlo results and our empirical results on a large cross-sectional data set of US individual stocks show that penalization without grouping can yield to nearly all estimated time-varying models violating the no arbitrage restrictions. Moreover, our results demonstrate that the proposed method reduces the prediction errors compared to a penalized approach without appropriate grouping or a time-invariant factor model. |
| Keywords: | two-pass regression, predictive modeling, large panel, factor model, LASSO penalization. |
| JEL: | C13 C23 C51 C52 C53 C55 C58 G12 G17 |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2109&r=all |
| By: | Hunt, Eric D.; Walters, Cory; Klemm, Toni; Eronmwon, Iyore |
| Keywords: | Farm Management, Production Economics |
| Date: | 2020–11–04 |
| URL: | http://d.repec.org/n?u=RePEc:ags:nbaece:309761&r=all |
| By: | Petr Jakubik (European Insurance and Occupational Pensions Authority (EIOPA), Germany; Charles University in Prague, Faculty of Social Sciences, Institute of Economic Studies, Czech Republic); Saida Teleu (Maltese Financial Services Authority, Malta; Charles University in Prague, Faculty of Social Sciences, Institute of Economic Studies, Czech Republic) |
| Abstract: | The recent Covid-19 outbreak with significant increase of global uncertainties poses many challenges for financial sectors. Many supervisors took the measures aiming to safeguard resilience of financial institutions by requesting postponements any dividend distributions until uncertainties about further development will be reduced. In this respect, the European Insurance and Occupational Pensions Authority issued on Thursday 2nd April 2020 a statement requesting (re)insurers to suspend all discretionary dividend distributions and share buy backs aimed at remunerating shareholders. Although this should have a positive impact on the overall financial stability of the sector, it could also negatively influence insurers’ equity prices. Hence, this paper empirically investigates this potential effect using an event study methodology. Despite negative drops were observed in some cases, the obtained empirical results suggest that they were not statistically significant for the overall European insurers’ equity market when considering the event windows covering a few days after the statement was published. |
| Keywords: | European insurance sector; suspension of dividend distributions, event study, EIOPA statement, equity market |
| JEL: | G22 G28 G35 G01 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2021_05&r=all |
| By: | Kroencke, Tim-Alexander; Schmeling, Maik; Schrimpf, Andreas |
| Abstract: | We identify a component of monetary policy news that is extracted from high-frequency changes in risky asset prices. These surprises, which we call "risk shifts", are uncorrelated, and therefore complementary, to risk-free rate surprises. We show that (i) risk shifts capture the lion's share of stock price movements around FOMC announcements; (ii) that they are accompanied by significant investor fund flows, suggesting that investors react heterogeneously to monetary policy news; and (iii) that price pressure amplifies the stock market response to monetary policy news. Our results imply that central bank information effects are overshadowed by short-term dynamics stemming from investor rebalancing activities and are likely to be more difficult to identify than previously thought. |
| Keywords: | Monetary Policy Surprises,Equity Premium,Fund Flows,Portfolio Rebalancing,Price Pressures |
| JEL: | G10 G12 E44 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safewp:302&r=all |
| By: | John H. Cochrane |
| Abstract: | How should long-term investors form portfolios in our time-varying, multifactor and friction-filled world? Two conceptual frameworks may help: looking directly at the stream of payments that a portfolio and payout policy can produce, and including a general equilibrium view of the markets’ economic purpose, and the nature of investors’ differences. These perspectives can rationalize some of investors’ behaviors, suggest substantial revisions to standard portfolio theory, and help us to apply portfolio theory in a way that is practically useful for investors. |
| JEL: | G11 G12 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28513&r=all |
| By: | Youngsoo Jang; Soyoung Lee |
| Abstract: | Default risk models have been widely employed to assess the ability of households and sovereigns to insure themselves against shocks. Grid search has often been used to solve these models because the complexity of the problem prevents the use of faster but less general methods. In this paper, we propose an extension of the endogenous grid method for default risk models, which is faster and more accurate than grid search. In particular, we find that our solution method leads to a more accurate bond price function, thus making substantial differences in the model’s main predictions. When applied to Arellano’s (2008) model, our approach predicts a standard deviation of the interest rate spread one-third lower and defaults 3 to 5 times less frequently than does the conventional approach. On top of that, our method is efficient. It is approximately 4 to 7 times faster than grid search when applied to a canonical model of Arellano (2008) and 19 to 27 times faster than grid search when applied to the richer model of Nakajima and Ríos-Rull (2014). Finally, we show that our method is applicable to a broad class of default risk models by characterizing sufficient conditions. |
| Keywords: | Credit and credit aggregates; Credit risk management |
| JEL: | C63 E37 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:21-11&r=all |
| By: | Ercument Cahan; Jushan Bai; Serena Ng |
| Abstract: | Economists are blessed with a wealth of data for analysis, but more often than not, values in some entries of the data matrix are missing. Various methods have been proposed to impute missing observations with iterative estimates of their unconditional or conditional means. We exploit the factor structure in panel data of large dimensions. We first use a series of projections of variable specific length to impute the common component associated with missing observations and show that this immediately yields a consistent estimate without further iteration. But setting the idiosyncratic errors to zero will under-estimate variability. Hence in a second imputation, we inject the missing idiosyncratic noise by resampling to obtain a consistent estimator for the convariance matrix, which plays an important role in risk management. Simulations calibrated to CRSP returns over the sample 1990-2018 are used to show that the double imputation methodology significantly improves various performance measures over single imputation. Implications for using |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.03045&r=all |
| By: | Philipp Eisenhauer; Jano\'s Gabler; Lena Janys |
| Abstract: | The ex-ante evaluation of policies using structural microeconometric 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 an approach that deals with parametric uncertainty and properly frames model-informed policy-making as a decision problem under uncertainty. We use the seminal human capital investment model by Keane and Wolpin (1997) as 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: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.01115&r=all |