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on Risk Management |
| By: | Wei Wang; Huifu Xu; Tiejun Ma |
| Abstract: | When estimating the risk of a financial position with empirical data or Monte Carlo simulations via a tail-dependent law invariant risk measure such as the Conditional Value-at-Risk (CVaR), it is important to ensure the robustness of the statistical estimator particularly when the data contain noise. Kratscher et al. [1] propose a new framework to examine the qualitative robustness of estimators for tail-dependent law invariant risk measures on Orlicz spaces, which is a step further from earlier work for studying the robustness of risk measurement procedures by Cont et al. [2]. In this paper, we follow the stream of research to propose a quantitative approach for verifying the statistical robustness of tail-dependent law invariant risk measures. A distinct feature of our approach is that we use the Fortet-Mourier metric to quantify the variation of the true underlying probability measure in the analysis of the discrepancy between the laws of the plug-in estimators of law invariant risk measure based on the true data and perturbed data, which enables us to derive an explicit error bound for the discrepancy when the risk functional is Lipschitz continuous with respect to a class of admissible laws. Moreover, the newly introduced notion of Lipschitz continuity allows us to examine the degree of robustness for tail-dependent risk measures. Finally, we apply our quantitative approach to some well-known risk measures to illustrate our theory. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.15491&r=all |
| By: | Wenlong Hu |
| Abstract: | This paper presents a novel framework for valuation and hedging of the insurer's net liability on a Guaranteed Minimum Maturity Benefit (GMMB) embedded in variable annuity (VA) contracts whose underlying mutual fund dynamics evolve under the influence of the regime-switching model. Numerical solutions for valuations and Greeks (i.e. valuation sensitivities with respect to model parameters) of GMMB under stochastic mortality are derived. Valuation and hedging is performed using an accurate, fast and efficient Fourier Space Time-stepping (FST) algorithm. The mortality component of the model is calibrated to the American male population. Sensitivity analysis is performed with respect to various parameters. The hedge effectiveness is assessed by comparing profit-and-loss performances for an unhedged and three statically hedged portfolios. The results provide a comprehensive analysis on valuation and hedging the longevity risk, interest rate risk and equity risk for the GMMB embedded in VAs, and highlight the benefits to insurance providers who offer those products. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.15483&r=all |
| By: | Becker, Bo; Opp, Marcus M.; Saidi, Farzad |
| Abstract: | This paper documents the long-run effects of an important reform of capital regulation for U.S. insurance companies in 2009. We show that its design effectively eliminates capital requirements for (non-agency) MBS, implying an aggregate capital relief of over $18bn at the time of the reform. By 2015, 40% of all high-yield assets in the overall fixed-income portfolio are MBS investments. This result is primarily driven by insurers' reduced propensity to sell poorly-rated legacy assets. Using a regression discontinuity framework, we can attribute this behavior to capital requirements. We also provide evidence that the insurance industry, driven by large life insurers, crowds out other investors in the new issuance of (high-yield) MBS post reform. Our findings are consistent with the view that the regulation and supervision of the U.S. insurance sector is influenced by short-term industry interests. |
| Keywords: | Capital regulation; insurance industry; NAIC; Regulatory Reform; risk- based capital requirements |
| JEL: | G20 G22 G23 G28 |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14373&r=all |
| By: | Carlos Andr\'es Araiza Iturria; Fr\'ed\'eric Godin; M\'elina Mailhot |
| Abstract: | We propose a stochastic model allowing property and casualty insurers with multiple business lines to measure their liabilities for incurred claims risk and calculate associated capital requirements. Our model includes many desirable features which enable reproducing empirical properties of loss ratio dynamics. For instance, our model integrates a double generalized linear model relying on accident semester and development lag effects to represent both the mean and dispersion of loss ratio distributions, an autocorrelation structure between loss ratios of the various development lags, and a hierarchical copula model driving the dependence across the various business lines. The model allows for a joint simulation of loss triangles and the quantification of the overall portfolio risk through risk measures. Consequently, a diversification benefit associated to the economic capital requirements can be measured, in accordance with IFRS 17 standards which allow for the recognition of such benefit. The allocation of capital across business lines based on the Euler allocation principle is then illustrated. The implementation of our model is performed by estimating its parameters based on a car insurance data obtained from the General Insurance Statistical Agency (GISA), and by conducting numerical simulations whose results are then presented. |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2007.07068&r=all |
| By: | J. Arismendi-Zambrano (Department of Economics, Finance and Accounting, Maynooth University, Ireland & ICMA Centre, Henley Business School, University of Reading, Whiteknights, Reading, United Kingdom.); R. Azevedo (Bradesco Asset Management - BRAM, Sao Paulo, Brazil) |
| Abstract: | Implicit in interest rate derivatives are Arrow-Debreu prices (or state price densities, SPDs) that contain fundamental information for risk and portfolio management in interest rate markets. To extract such information from interest rate derivatives, we propose a nonparametric method to estimate state prices based on the minimization of the Cressie-Read (Entropic) family function between potential SPDs and the empirical probability measure. An empirical application of the method, in the US interest rates and derivatives market, shows that the entropic based risk-neutral density measure highlight potential risks previous to the 2007/2008 financial crisis, and the potential arbitrage burden during the Quantitative Easing period. |
| Keywords: | Risk management, Risk analysis, Nonparametric Asset Pricing, State Price Density, Interest Rate Derivatives |
| JEL: | C14 G12 G13 G14 G18 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:may:mayecw:n303-20.pdf&r=all |
| By: | Gropp, Reint; Koetter, Michael; McShane, William |
| Abstract: | We conduct stress tests for a large sample of German banks across different recoveries from the Corona recession. We find that, depending on how quickly the economy recovers, between 6% to 28% of banks could become distressed from defaulting corporate borrowers alone. Many of these banks are likely to require regulatory intervention or may even fail. Even in our most optimistic scenario, bank capital ratios decline by nearly 24%. The sum of total loans held by distressed banks could plausibly range from 127 to 624 billion Euros and it may take years before the full extent of this stress is observable. Hence, the current recession could result in an acute contraction in lending to the real economy, thereby worsening the current recession , decelerating the recovery, or perhaps even causing a "double dip" recession. Additionally, we show that the corporate portfolio of savings and cooperative banks is more than five times as exposed to small firms as that of commercial banks and Landesbanken. The preliminary evidence indicates small firms are particularly exposed to the current crisis, which implies that cooperative and savings banks are at especially high risk of becoming distressed. Given that the financial difficulties may seriously impair the recovery from the Covid-19 crisis, the pressure to bail out large parts of the banking system will be strong. Recent research suggests that the long run benefits of largely resisting these pressures may be high and could result in a more efficient economy. |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwhonl:42020&r=all |
| By: | Max Nendel; Maren Diane Schmeck; Frank Riedel |
| Abstract: | In this paper, we provide an axiomatic approach to general premium priciples giving rise to a decomposition into risk, as a generalization of the expected value, and deviation, as a generalization of the variance. We show that, for every premium priciple, there exists a maximal risk measure capturing all risky components covered by the insurance prices. In a second step, we consider dual representations of convex risk measures consistent with the premium priciple. In particular, we show that the convex conjugate of the aforementioned maximal risk measure coincides with the convex conjugate of the premium principle on the set of all finitely additive probability measures. In a last step, we consider insurance prices in the presence of a not neccesarily frictionless market, where insurance claims are traded. In this setup, we discuss premium principles that are consistent with hedging using securization products that are traded in the market. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.14272&r=all |
| By: | Nendel, Max (Center for Mathematical Economics, Bielefeld University); Schmeck, Maren Diane (Center for Mathematical Economics, Bielefeld University); Riedel, Frank (Center for Mathematical Economics, Bielefeld University) |
| Abstract: | In this paper, we provide an axiomatic approach to general premium priciples giving rise to a decomposition into risk, as a generalization of the expected value, and deviation, as a generalization of the variance. We show that, for every premium priciple, there exists a maximal risk measure capturing all risky components covered by the insurance prices. In a second step, we consider dual representations of convex risk measures consistent with the premium priciple. In particular, we show that the convex conjugate of the aforementioned maximal risk measure coincides with the convex conjugate of the premium principle on the set of all finitely additive probability measures. In a last step, we consider insurance prices in the presence of a not neccesarily frictionless market, where insurance claims are traded. In this setup, we discuss premium principles that are consistent with hedging using securization products that are traded in the market. |
| Keywords: | Principles of premium calculation, risk measure, deviation measure, convex duality, superhedging |
| Date: | 2020–06–26 |
| URL: | http://d.repec.org/n?u=RePEc:bie:wpaper:638&r=all |
| By: | Mendicino, Caterina; Nikolov, Kalin; Rubio-Ramírez, Juan Francisco; Suarez, Javier; Supera, Dominik |
| Abstract: | We study the interaction between borrowers' and banks' solvency in a quantitative macroeconomic model with financial frictions in which bank assets are a portfolio of defaultable loans. We show that ex-ante imperfect diversification of bank lending generates bank asset returns with limited upside but significant downside risk. The asymmetric distribution of these returns and their implications for the evolution of bank net worth are important for capturing the frequency and severity of twin default crises -simultaneous rises in firm and bank defaults associated with sizeable negative effects on economic activity. As a result, our model implies higher optimal capital requirements than common specifications of bank asset returns, which neglect or underestimate the impact of borrower default on bank solvency. |
| Keywords: | Bank Fragility; Capital requirements; Default Risk; loan returns; non-diversifiable risk |
| JEL: | E3 E44 G01 G21 |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14427&r=all |
| By: | Chakrabarti, Arnab; Chakrabarti, Anindya S. |
| Abstract: | Computation of spectral structure and risk measures from networks of multivariate financial time series data has been at the forefront of the statistical finance literature for a long time. A standard mode of analysis is to consider log returns from the equity price data, which is akin to taking first difference ($d = 1$) of the log of the price data. Sometimes authors have considered simple growth rates as well. Either way, the idea is to get rid of the nonstationarity induced by the {\it unit root} of the data generating process. However, it has also been noted in the literature that often the individual time series might have a root which is more or less than unity in magnitude. Thus first differencing leads to under-differencing in many cases and over differencing in others. In this paper, we study how correcting for the order of differencing leads to altered filtering and risk computation on inferred networks. In summary, our results are: (a) the filtering method with extreme information loss like minimum spanning tree as well as filtering with moderate information loss like triangulated maximally filtered graph are very susceptible to such d-corrections, (b) the spectral structure of the correlation matrix is quite stable although the d-corrected market mode almost always dominates the uncorrected (d = 1) market mode indicating under-estimation in the standard analysis, and (c) the PageRank-based risk measure constructed from Granger-causal networks shows an inverted U-shape evolution in the relationship between d-corrected and uncorrected return data over the period of analysis 1972-2018 for historical data of NASDAQ. |
| Date: | 2020–07–08 |
| URL: | http://d.repec.org/n?u=RePEc:iim:iimawp:14629&r=all |
| By: | Rama Cont; Artur Kotlicki; Laura Valderrama |
| Abstract: | The traditional approach to the stress testing of financial institutions focuses on capital adequacy and solvency. Liquidity stress tests have been applied in parallel to and independently from solvency stress tests, based on scenarios which may not be consistent with those used in solvency stress tests. We propose a structural framework for the joint stress testing of solvency and liquidity: our approach exploits the mechanisms underlying the solvency-liquidity nexus to derive relations between solvency shocks and liquidity shocks. These relations are then used to model liquidity and solvency risk in a coherent framework, involving external shocks to solvency and endogenous liquidity shocks arising from these solvency shocks. We define the concept of ‘Liquidity at Risk’, which quantifies the liquidity resources required for a financial institution facing a stress scenario. Finally, we show that the interaction of liquidity and solvency may lead to the amplification of equity losses due to funding costs which arise from liquidity needs. The approach described in this study provides in particular a clear methodology for quantifying the impact of economic shocks resulting from the ongoing COVID-19 crisis on the solvency and liquidity of financial institutions and may serve as a useful tool for calibrating policy responses. |
| Date: | 2020–06–05 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:20/82&r=all |
| By: | Adams, Patrick; Adrian, Tobias; Boyarchenko, Nina; Giannone, Domenico |
| Abstract: | We construct risks around consensus forecasts of real GDP growth, unemployment and inflation. We find that risks are time-varying, asymmetric and partly predictable. Tight financial conditions forecast downside growth risk, upside unemployment risk and increased uncertainty around the inflation forecast. Growth vulnerability arises as the conditional mean and conditional variance of GDP growth are negatively correlated: downside risks are driven by lower mean and higher variance when financial conditions tighten. Similarly, employment vulnerability arises as the conditional mean and conditional variance of unemployment are positively correlated, with tighter financial conditions corresponding to higher forecasted unemployment and higher variance around the consensus forecast. |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14436&r=all |
| By: | James B. Bullard |
| Keywords: | COVID-19 |
| Date: | 2020–06–23 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlps:88235&r=all |
| By: | Joseph P. Byrne; Boulis M. Ibrahim; Xiaoyu Zong |
| Abstract: | An asset pricing model using long-run capital share growth risk has recently been found to successfully explain U.S. stock returns. Our paper adopts a recursive preference utility framework to derive an heterogeneous asset pricing model with capital share risks.While modeling capital share risks, we account for the elevated consumption volatility of high income stockholders. Capital risks have strong volatility effects in our recursive asset pricing model. Empirical evidence is presented in which capital share growth is also a source of risk for stock return volatility. We uncover contrasting unconditional and conditional asset pricing evidence for capital share risks. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.14023&r=all |
| By: | Chambers, David; Dimson, Elroy; Spaenjers, Christophe |
| Abstract: | The risk-return characteristics of art as an asset have been previously studied through aggregate price indexes. By contrast, we examine the long-run buy-and-hold performance of an actual portfolio, namely, the collection of John Maynard Keynes. We find that its performance has substantially exceeded existing estimates of art market returns. Our analysis of the collection identifies general attributes of art portfolios crucial in explaining why investor returns can substantially diverge from market returns: transaction-specific risk, buyer heterogeneity, return skewness, and portfolio concentration. Furthermore, our findings highlight the limitations of art price indexes as a guide to asset allocation or performance benchmarking. |
| JEL: | B26 C43 G11 G12 G14 Z11 |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14357&r=all |
| By: | Hasenzagl, Thomas; Reichlin, Lucrezia; Ricco, Giovanni |
| Abstract: | We evaluate the role of financial conditions as predictors of macroeconomic risk first in the quantile regression framework of Adrian et al. (2019b), which allows for non-linearities, and then in a novel linear semi-structural model as proposed by Hasenzagl et al. (2018). We distinguish between price variables such as credit spreads and stock variables such as leverage. We find that (i) although the spreads correlate with the left tail of the conditional distribution of GDP growth, they provide limited advanced information on growth vulnerability; (ii) nonfinancial leverage provides a leading signal for the left quantile of the GDP growth distribution in the 2008 recession; (iii) measures of excess leverage conceptually similar to the Basel gap, but cleaned from business cycle dynamics via the lenses of the semi-structural model, point to two peaks of accumulation of risks - the eighties and the first eight years of the new millennium, with an unstable relationship with business cycle chronology. |
| Keywords: | Business cycle; credit; Downside risk; entropy; financial crises; financial cycle; quantile regressions |
| JEL: | C32 C53 E32 E44 |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14322&r=all |
| By: | Aslanidis, Nektarios; Christiansen, Charlotte; Kouretas, George |
| Abstract: | We conduct an international analysis of the cross-sectional risk premiums of uncertainty risk factors in addition to traditional risk factors. We consider the stock markets in five regions separately. Internationally, uncertainty has negative risk premiums which is similar to previous findings for the US. This implies that investors get lower returns for assets with high uncertainty betas. We further contribute with an analysis of downside un- certainty risk. Here, the downside uncertainty risk factor is high uncertainty which has additional risk premiums. We measure uncertainty by the logs of the local and US economic policy uncertainty indices. Keywords: International stock returns; economic policy uncertainty; Fama- French factor models; downside risk. JEL Classifications: G12; G15 |
| Keywords: | Mercats financers, 33 - Economia, |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:urv:wpaper:2072/376032&r=all |
| By: | Persada, Pena; Gusti, Girang Permata |
| Abstract: | The writing of this book is motivated by the author's curiosity about the performance of a long-standing company, with a large number of assets and with a large number of branch offices and even received full support from the government of the Republic of Indonesia, but instead showed a disappointing performance. All the resources that are owned and with long-term experience can not be a guarantee that the company will have a good financial performance and can be accounted for in every financial statement preparation. History will continue to experience the same repetition and events in the future. It takes real effort if you want to change the situation for the better than before. Learning from the experiences of companies that are wrong in managing corporate risk management, the community and potential investors should have adequate knowledge when deciding to buy investment products. In order not to become a victim of the promotion method with the scheme gives a promise of great profits, but the result is a loss. The public must be aware and critical of the various offers made by the company's marketing, so as not to get caught up in investment schemes that provide big returns. Communities must invest their time in advance to be well informed so that they have sufficient knowledge when deciding to invest. This book provides an example of a case where the application of risk management is wrong, so the company is experiencing losses due to incorrect decisions. It is hoped that we can learn from the mistakes they have made. |
| Date: | 2020–05–19 |
| URL: | http://d.repec.org/n?u=RePEc:osf:thesis:2rvgx&r=all |
| By: | Zhongjun Wang; Mengye Sun; A. M. Elsawah |
| Abstract: | With the aggravation of the global economic crisis and inflation, the precious metals with safe-haven function have become more popular. An improved MF-DFA method is proposed to analyze price fluctuations of the precious metals market. Based on the widely used multifractal detrended fluctuation analysis method (MF-DFA), we compare these two methods and find that the Bi-OSW-MF-DFA method possesses better efficiency. This article analyzes the degree of multifractality between spot gold market and spot silver market as well as their risks. From the numerical results and figures, it is found that two elements constitute the contributions in the formation of multifractality in time series and the risk of the spot silver market is higher than that of the spot gold market. This attempt could lead to a better understanding of complicated precious metals market. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.15214&r=all |
| By: | Bischof, Jannis; Laux, Christian; Leuz, Christian |
| Abstract: | This paper examines banks' disclosures and loss recognition in the financial crisis and identifies several core issues for the link between accounting and financial stability. Our analysis suggests that, going into the financial crisis, banks' disclosures about relevant risk exposures were relatively sparse. Such disclosures came later after major concerns about banks' exposures had arisen in markets. Similarly, the recognition of loan losses was relatively slow and delayed relative to prevailing market expectations. Among the possible explanations for this evidence, our analysis suggests that banks' reporting incentives played a key role, which has important implications for bank supervision and the new expected loss model for loan accounting. We also provide evidence that shielding regulatory capital from accounting losses through prudential filters can dampen banks' incentives for corrective actions. Overall, our analysis reveals several important challenges if accounting and financial reporting are to contribute to financial stability. |
| Keywords: | Banks,Financial crisis,Financial stability,Disclosure,Loan loss accounting,Expected credit losses,Incurred loss model,Prudential filter,Fair valueaccounting |
| JEL: | G21 G22 G28 G32 G38 K22 M41 M42 M48 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safewp:283&r=all |
| By: | Altig, Dave (Federal Reserve Bank of Atlanta); Baker, Scott (Kellogg School of Management, Northwestern University); Barrero, Jose Maria (ITAM Business School); Bloom, Nick (Stanford University); Bunn, Philip (Bank of England); Chen, Scarlet (Stanford University); Davis, Steven J (University of Chicago Booth School of Business); Leather, Julia (University of Nottingham); Meyer, Brent (Federal Reserve Bank of Atlanta); Mihaylov, Emil (Federal Reserve Bank of Atlanta); Mizen, Paul (University of Nottingham); Parker, Nick (Federal Reserve Bank of Atlanta); Renault, Thomas (University Paris 1 Panthéon-Sorbonne); Smietanka, Pawel (Bank of England); Thwaites, Greg (LSE Centre for Macroeconomics) |
| Abstract: | We consider several economic uncertainty indicators for the US and UK before and during the Covid-19 pandemic: implied stock market volatility, newspaper-based economic policy uncertainty, twitter chatter about economic uncertainty, subjective uncertainty about future business growth, and disagreement among professional forecasters about future GDP growth. Three results emerge. First, all indicators show huge uncertainty jumps in reaction to the pandemic and its economic fallout. Indeed, most indicators reach their highest values on record. Second, peak amplitudes differ greatly — from a rise of around 100% (relative to January 2020) in two-year implied volatility on the S&P 500 and subjective uncertainty around year-ahead sales for UK firms to a 20-fold rise in forecaster disagreement about UK growth. Third, time paths also differ: implied volatility rose rapidly from late February, peaked in mid-March, and fell back by late March as stock prices began to recover. In contrast, broader measures of uncertainty peaked later and then plateaued, as job losses mounted, highlighting the difference in uncertainty measures between Wall Street and Main Street. |
| Keywords: | Forward-looking uncertainty measures; volatility; Covid-19; coronavirus |
| JEL: | D80 E22 E66 G18 L50 |
| Date: | 2020–06–26 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0876&r=all |
| By: | Le-Yu Chen; Sokbae Lee |
| Abstract: | We consider both $\ell _{0}$-penalized and $\ell _{0}$-constrained quantile regression estimators. For the $\ell _{0}$-penalized estimator, we derive an exponential inequality on the tail probability of excess quantile prediction risk and apply it to obtain non-asymptotic upper bounds on the mean-square parameter and regression function estimation errors. We also derive analogous results for the $\ell _{0}$-constrained estimator. The resulting rates of convergence are minimax-optimal and the same as those for $\ell _{1} $-penalized estimators. Further, we characterize expected Hamming loss for the $\ell _{0}$-penalized estimator. We implement the proposed procedure via mixed integer linear programming and also a more scalable first-order approximation algorithm. We illustrate the finite-sample performance of our approach in Monte Carlo experiments and its usefulness in a real data application concerning conformal prediction of infant birth weights (with $% n\approx 10^{3}$ and up to $p>10^{3}$). In sum, our $\ell _{0}$-based method produces a much sparser estimator than the $\ell _{1}$-penalized approach without compromising precision. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.11201&r=all |
| By: | Kim, Hyonok; Fukukawa, Hironori; Routledge, James |
| Abstract: | This paper compares management and auditor going concern risk disclosures. It exploits a unique regulatory change in Japan that impacted the going concern risk disclosure practice. Prior to 2009, managers were directed to make financial statement note disclosures if they considered there was substantial doubt about the going concern status. The note disclosures were required to be audited. After 2009, substantial doubt disclosures by management are not audited and can be considered voluntary. We test whether going concern risk disclosure is enhanced by requiring managers rather than auditors to make the disclosure voluntarily. Analysis shows increased overall levels of going concern risk disclosure after the 2009 regulatory change, which is substantially attributable to voluntary disclosure in the Business Risk section of annual reports. The results are of interest to regulators because they suggest that it is appropriate for managers to be assigned primary responsibility for going concern risk disclosure. |
| Keywords: | going concern, business risk disclosure, voluntary disclosure |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:hit:hmicwp:234&r=all |
| By: | Michal Andrle; Benjamin L Hunt |
| Abstract: | This paper outlines an approach to assess uncertainty around a forecast baseline as well as the impact of alternative policy rules on macro variability. The approach allows for non-Gaussian shock distributions and non-linear underlying macroeconomic models. Consequently, the resulting distributions for macroeconomic variables can exhibit skewness and fat tails. Several applications are presented that illustrate the practical implementation of the technique including confidence bands around a baseline forecast, the probabilities of global growth falling below a specified threshold, and the impact of alternative fiscal policy reactions functions on macro variability. |
| Keywords: | Economic models;Economic policy;Business cycles;Monetary policy;Fiscal policy;DSGE models,predictive density,nonlinear,non-Gaussian,skew,fat tails,WP,economic shock,policy space,ELB,nominal interest rate,risk assessment |
| Date: | 2020–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:20/64&r=all |
| By: | Parisa Golbayani; Ionu\c{t} Florescu; Rupak Chatterjee |
| Abstract: | Credit ratings are one of the primary keys that reflect the level of riskiness and reliability of corporations to meet their financial obligations. Rating agencies tend to take extended periods of time to provide new ratings and update older ones. Therefore, credit scoring assessments using artificial intelligence has gained a lot of interest in recent years. Successful machine learning methods can provide rapid analysis of credit scores while updating older ones on a daily time scale. Related studies have shown that neural networks and support vector machines outperform other techniques by providing better prediction accuracy. The purpose of this paper is two fold. First, we provide a survey and a comparative analysis of results from literature applying machine learning techniques to predict credit rating. Second, we apply ourselves four machine learning techniques deemed useful from previous studies (Bagged Decision Trees, Random Forest, Support Vector Machine and Multilayer Perceptron) to the same datasets. We evaluate the results using a 10-fold cross validation technique. The results of the experiment for the datasets chosen show superior performance for decision tree based models. In addition to the conventional accuracy measure of classifiers, we introduce a measure of accuracy based on notches called "Notch Distance" to analyze the performance of the above classifiers in the specific context of credit rating. This measure tells us how far the predictions are from the true ratings. We further compare the performance of three major rating agencies, Standard $\&$ Poors, Moody's and Fitch where we show that the difference in their ratings is comparable with the decision tree prediction versus the actual rating on the test dataset. |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2007.06617&r=all |
| By: | Fleitas, Sebastian; Gowrisankaran, Gautam; Lo Sasso, Anthony |
| Abstract: | We evaluate reclassification risk in the small group health insurance market from a period before ACA community rating regulations. Reclassification risk in this setting is of key policy relevance and also a matter of debate. We use detailed claims and premiums data from a large insurance company and control non-parametrically for selection. We find a pass through of 16% from changes in health risk to changes in premiums, with a stronger equilibrium relationship between premiums and risk. This pattern is consistent with the insurer implicitly offering "guaranteed renewability'' contracts with one-sided pricing commitment. We further find that groups whose health risk decreases have premiums that are more responsive to risk, which the guaranteed renewability model attributes to ex post renegotiation. The observed pricing policy adds 60% of the consumer welfare gain from community rating relative to experience rating. The welfare gains are limited because employers and employees switch coverage frequently. |
| Keywords: | Adverse Selection; experience rating; guaranteed renewability; inertia; Pass through |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14394&r=all |
| By: | Ricardo Correa; Linda S. Goldberg |
| Abstract: | Bank holding companies (BHCs) can be complex organizations, conducting multiple lines of business through many distinct legal entities and across a range of geographies. While such complexity raises the costs of bank resolution when organizations fail, the effect of complexity on BHCs’ broader risk profiles is less well understood. Business, organizational, and geographic complexity can engender explicit trade-offs between the agency problems that increase risk and the diversification, liquidity management, and synergy improvements that reduce risk. The outcomes of such trade-offs may depend on bank governance arrangements. We test these conjectures using data on large U.S. BHCs for the 1996-2018 period. Organizational complexity and geographic scope tend to provide diversification gains and reduce idiosyncratic and liquidity risks while also increasing BHCs’ exposure to systematic and systemic risks. Regulatory changes focused on organizational complexity have significantly reduced this type of complexity, leading to a decrease in systemic risk and an increase in liquidity risk among BHCs. While bank governance structures have, in some cases, significantly affected the buildup of BHC complexity, better governance arrangements have not moderated the effects of complexity on risk outcomes. |
| Keywords: | too big to fail; diversification; bank complexity; regulation; corporate governance; global banks; liquidity; agency problem; risk taking |
| JEL: | G21 G28 G32 |
| Date: | 2020–06–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:88198&r=all |
| By: | Michael C. Fu; Bingqing Li; Rongwen Wu; Tianqi Zhang |
| Abstract: | We consider option pricing using a discrete-time Markov switching stochastic volatility with co-jump model, which can model volatility clustering and varying mean-reversion speeds of volatility. For pricing European options, we develop a computationally efficient method for obtaining the probability distribution of average integrated variance (AIV), which is key to option pricing under stochastic-volatility-type models. Building upon the efficiency of the European option pricing approach, we are able to price an American-style option, by converting its pricing into the pricing of a portfolio of European options. Our work also provides constructive guidance for analyzing derivatives based on variance, e.g., the variance swap. Numerical results indicate our methods can be implemented very efficiently and accurately. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.15054&r=all |
| By: | Ralph Rudd; Thomas A. McWalter; Joerg Kienitz; Eckhard Platen |
| Abstract: | Recursive marginal quantization (RMQ) allows the construction of optimal discrete grids for approximating solutions to stochastic differential equations in d-dimensions. Product Markovian quantization (PMQ) reduces this problem to d one-dimensional quantization problems by recursively constructing product quantizers, as opposed to a truly optimal quantizer. However, the standard Newton-Raphson method used in the PMQ algorithm suffers from numerical instabilities, inhibiting widespread adoption, especially for use in calibration. By directly specifying the random variable to be quantized at each time step, we show that PMQ, and RMQ in one dimension, can be expressed as standard vector quantization. This reformulation allows the application of the accelerated Lloyd's algorithm in an adaptive and robust procedure. Furthermore, in the case of stochastic volatility models, we extend the PMQ algorithm by using higher-order updates for the volatility or variance process. We illustrate the technique for European options, using the Heston model, and more exotic products, using the SABR model. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.15823&r=all |
| By: | Herv\'e Andres; Pierre-Edouard Arrouy; Paul Bonnefoy; Alexandre Boumezoued; Sophian Mehalla |
| Abstract: | We propose to take advantage of the common knowledge of the characteristic function of the swap rate process as modelled in the LIBOR Market Model with Stochastic Volatility and Displaced Diffusion (DDSVLMM) to derive analytical expressions of the gradient of swaptions prices with respect to the model parameters. We use this result to derive an efficient calibration method for the DDSVLMM using gradient-based optimization algorithms. Our study relies on and extends the work by (Cui et al., 2017) that developed the analytical gradient for fast calibration of the Heston model, based on an alternative formulation of the Heston moment generating function proposed by (del Ba{\~n}o et al., 2010). Our main conclusion is that the analytical gradient-based calibration is highly competitive for the DDSVLMM, as it significantly limits the number of steps in the optimization algorithm while improving its accuracy. The efficiency of this novel approach is compared to classical standard optimization procedures. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.13521&r=all |
| By: | Archil Gulisashvili |
| Abstract: | We introduce stochastic volatility models, in which the volatility is described by a time-dependent nonnegative function of a reflecting diffusion. The idea to use reflecting diffusions as building blocks of the volatility came into being because of a certain volatility misspecification in the classical Stein and Stein model. A version of this model that uses the reflecting Ornstein-Uhlenbeck process as the volatility process is a special example of a stochastic volatility model with reflection. The main results obtained in the present paper are sample path and small-noise large deviation principles for the log-price process in a stochastic volatility model with reflection under rather mild restrictions. We use these results to study the asymptotic behavior of binary barrier options and call prices in the small-noise regime. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.15431&r=all |
| By: | Martin Bladt; Alexander J. McNeil |
| Abstract: | An approach to modelling volatile financial return series using d-vine copulas combined with uniformity preserving transformations known as v-transforms is proposed. By generalizing the concept of stochastic inversion of v-transforms, models are obtained that can describe both stochastic volatility in the magnitude of price movements and serial correlation in their directions. In combination with parametric marginal distributions it is shown that these models can rival and sometimes outperform well-known models in the extended GARCH family. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.11088&r=all |
| By: | Heinrich, Markus |
| Abstract: | Macroeconomic forecasting in recessions is not easy due to the inherent asymmetry of business cycle phases and the increased uncertainty about the future path of the teetering economy. I propose a mixed-frequency threshold vector autoregressive model with common stochastic volatility in mean (MF-T-CSVM-VAR) that enables to condition on the current state of the business cycle and to account for time-varying macroeconomic uncertainty in form of common stochastic volatility in a mixed-frequency setting. A real-time forecasting experiment highlights the advantage of including the threshold feature for the asymmetry as well as the common stochastic volatility in mean in MF-VARs of different size for US GDP, inflation and unemployment. The novel mixed-frequency threshold model delivers better forecasts for short-term point and density forecasts with respect to GDP and unemployment--particularly evident for nowcasts during recessions. In fact, it delivers a better nowcast than the US Survey of Professional Forecasters for the sharp drop in GDP during the Great Recession in 2008Q4. |
| Keywords: | Threshold VAR,Stochastic Volatility,Forecasting,Mixed-frequency Models,Business Cycle,Bayesian Methods |
| JEL: | C11 C32 C34 C53 E32 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:219312&r=all |