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on Risk Management |
| By: | Taras Bodnar; Mathias Lindholm; Vilhelm Niklasson; Erik Thors\'en |
| Abstract: | We study the optimal portfolio allocation problem from a Bayesian perspective using value at risk (VaR) and conditional value at risk (CVaR) as risk measures. By applying the posterior predictive distribution for the future portfolio return, we derive relevant quantiles needed in the computations of VaR and CVaR, and express the optimal portfolio weights in terms of observed data only. This is in contrast to the conventional method where the optimal solution is based on unobserved quantities which are estimated, leading to suboptimality. We also obtain the expressions for the weights of the global minimum VaR and CVaR portfolios, and specify conditions for their existence. It is shown that these portfolios may not exist if the confidence level used for the VaR or CVaR computation are too low. Moreover, analytical expressions for the mean-VaR and mean-CVaR efficient frontiers are presented and the extension of theoretical results to general coherent risk measures is provided. One of the main advantages of the suggested Bayesian approach is that the theoretical results are derived in the finite-sample case and thus they are exact and can be applied to large-dimensional portfolios. By using simulation and real market data, we compare the new Bayesian approach to the conventional method by studying the performance and existence of the global minimum VaR portfolio and by analysing the estimated efficient frontiers. It is concluded that the Bayesian approach outperforms the conventional one, in particular at predicting the out-of-sample VaR. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.01819&r=all |
| By: | Daniel Felix Ahelegbey (University of Pavia); |
| Abstract: | We extend the extreme downside hedge methodology to model sensitivity interconnectedness of market returns to the tail risk of other markets under turbulent conditions. We derive the interconnectedness via Bayesian graph structural learning. The empirical application examines the dynamic interconnectedness among 15 major markets, including G10 economies, during turbulent times. We investigate whether downside risk connections among these major markets are merely anecdotal or provide evidence of contagion and the most central market for spillover propagation. The result shows that the Covid-19 induced downside risk connections record the highest density, suggesting stronger evidence of contagion in the coronavirus pandemic than during the financial and eurozone crisis. Central to the spillover propagation is the finding that most of the transmitters and recipients of downside risk are EU markets. |
| Keywords: | Bayesian Inference, Centrality, Contagion, Conditional VaR, Downside Risk, Extreme downside hedge, Financial Crises, Financial Networks. |
| JEL: | C31 C58 G01 G12 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:pav:demwpp:demwp0193&r=all |
| By: | Shige Peng; Shuzhen Yang |
| Abstract: | Financial time series admits inherent uncertainty and randomness that changes over time. To clearly describe volatility uncertainty of the time series, we assume that the volatility of risky assets holds value between the minimum volatility and maximum volatility of the assets. This study establishes autoregressive models to determine the maximum and minimum volatilities, where the ratio of minimum volatility to maximum volatility can measure volatility uncertainty. By utilizing the value at risk (VaR) predictor model under volatility uncertainty, we introduce the risk and uncertainty, and show that the autoregressive model of volatility uncertainty is a powerful tool in predicting the VaR for a benchmark dataset. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.09226&r=all |
| By: | Gian Paolo Clemente; Alessandra Cornaro |
| Abstract: | We provide a framework for detecting relevant insurance companies in a systemic risk perspective. Among the alternative methodologies for measuring systemic risk, we propose a complex network approach where insurers are linked to form a global interconnected system. We model the reciprocal influence between insurers calibrating edge weights on the basis of specific risk measures. Therefore, we provide a suitable network indicator, the Weighted Effective Resistance Centrality, able to catch which is the effect of a specific vertex on the network robustness. By means of this indicator, we assess the prominence of a company in spreading and receiving risk from the others. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.11394&r=all |
| By: | Bahman Angoshtari; Virginia R. Young |
| Abstract: | We find the optimal indemnity to minimize the probability of ruin when premium is calculated according to the distortion premium principle with a proportional risk load, and admissible indemnities are such that both the indemnity and retention are non-decreasing functions of the underlying loss. We reformulate the problem with the inverse survival function as the control variable and show that deductible insurance with maximum limit is optimal. Our main contribution is in solving this problem via the inverse survival function. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.03798&r=all |
| By: | Daniel Felix Ahelegbey (University of Pavia); Paolo Giudici (University of Pavia) |
| Abstract: | We construct a network volatility index (NetVIX) via market interconnectedness and volatilities to measure global market turbulence. The NetVIX multiplicatively decomposes into an average volatility and a network amplifier index. It also additively decomposes into marginal volatility indices for measuring individual contribution to global turmoil. We apply our measure to study the relationship between the interconnectedness among 20 major stock markets and global market risks over the last two decades. The NetVIX is shown to be a novel approach to measuring global market risk, and an alternative to the VIX. The result shows that during crisis periods, particularly the tech-bubble, sub-prime, and COVID-19 pandemic, the interconnectedness of the markets amplifies average market risk more than 700 percent to cause a global meltdown. We find evidence that the highest risk-contributing markets to global meltdown are the US, Brazil, Hong Kong, France, and Germany. |
| Keywords: | Centrality, COVID-19, Financial Crises, NetVIX, Turbulence, VAR, VIX |
| JEL: | C11 C15 C51 C52 C55 C58 G01 G12 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:pav:demwpp:demwp0192&r=all |
| By: | Laurence M. Ball |
| Abstract: | This paper studies liquidity risk at the six largest U.S. banks. The starting point is the stress tests performed under the Liquidity Coverage Ratio (LCR) regulation, which compare a bank’s liquid assets to its loss of cash in a stress scenario that regulators say is based on the 2008 financial crisis. These tests find that all of the large banks could endure a liquidity crisis for 30 days without running out of cash. This paper argues, however, that some of the assumptions in the LCR stress scenario are not pessimistic enough to capture what could happen in a crisis like 2008. The paper then proposes changes in the dubious assumptions and performs revised stress tests. For 2019 Q4, the revised tests suggest it is unlikely that any of the six banks would survive a liquidity crisis for 30 days. This negative finding is most clear-cut for Goldman Sachs and Morgan Stanley. |
| JEL: | G21 G24 G28 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28124&r=all |
| By: | Anish Rai; Ajit Mahata; Md Nurujjaman; Om Prakash |
| Abstract: | Every unique crisis, a new and novel risk factor, leads to a rapid, synchronous and panic sell-off by the investors that lead to a massive stock market crash, termed as mainshock, which usually continues for more than one day. Though most of the stocks start recovering from the crash within a short period, the effect of the crash remains throughout the recovery phase. During the recovery, as the market remains in stress, any small perturbation leads to a relatively smaller aftershock, which may also occur for a few days. Statistical analysis of the mainshock and the aftershocks for the crash of 1987, the financial crisis of 2008 and the COVID-19 pandemic shows that they follow the Gutenberg-Richter (G-R) power law. The duration of the influence of the mainshock, within which aftershocks are considered, has been calculated using structural break analysis. The analysis shows that high magnitude aftershocks comparable to the mainshock are rare but low magnitude aftershocks can be seen frequently till the full recovery of the market. It is also consistent with the psychology of the investors that when the unique crisis becomes known, the market does not react too irrationally as it did initially, and hence subsequent crashes become relatively smaller. The results indicate that there is a possibility of the occurrence of future low magnitude aftershocks due to the ongoing COVID-19 pandemic. The analysis may help investors make rational investment decisions during the stressed period after a major market crash. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.03012&r=all |
| By: | Dominika Ehrenbergerova; Martin Hodula; Zuzana Rakovska |
| Abstract: | This paper tests whether a series of changes to capital requirements transmitted to a change to banks' pricing policy. We compile a rich bank-level supervisory dataset covering the banking sector in the Czech Republic over the period 2004-2019. We estimate that the changes to the overall capital requirements did not force banks to alter their pricing policy. The impact on bank interest margins and loan rates is found to lie in a narrow range around zero irrespective of loan category. Our estimates allow us to rule out effects even for less-capitalised banks and small banks. The results obtained contradict estimates from other studies reporting significant transmission of capital regulation to lending rates and interest margins. We therefore engage in a deeper discussion of why this might be the case. Our estimates may be used in the ongoing discussion of the benefits and costs of capital-based regulation in banking. |
| Keywords: | Bank pricing policy, capital requirements, interest margins, loan rates |
| JEL: | E58 G21 G28 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:cnb:wpaper:2020/5&r=all |
| By: | Shuzhen Yang |
| Abstract: | In this paper, we attempt to introduce the Bellman principle for a discrete time multi-period mean-variance model. Based on this new take on the Bellman principle, we obtain a dynamic time-consistent optimal strategy and related efficient frontier. Furthermore, we develop a varying investment period discrete time multi-period mean-variance model and obtain a related dynamic optimal strategy and an optimal investment period. This paper compares the highlighted dynamic optimal strategies of this study with the 1/n equality strategy, and shows that we can secure a higher return with a smaller risk based on the dynamic optimal strategies. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.10966&r=all |
| By: | Harris, Timothy F. (Illinois State University); Yelowitz, Aaron (University of Kentucky); Courtemanche, Charles (Georgia State University) |
| Abstract: | The profitability of life insurance offerings is contingent on accurate projections and pricing of mortality risk. The COVID-19 pandemic created significant uncertainty, with dire mortality predictions from early forecasts resulting in widespread government intervention and greater individual precaution that reduced the projected death toll. We analyze how life insurance companies changed pricing and offerings in response to COVID-19 using monthly data on term life insurance policies from Compulife. We estimate event-study models that exploit well-established variation in the COVID-19 mortality rate based on age and underlying health status. Despite the increase in mortality risk and significant uncertainty, we find limited evidence that life insurance companies increased premiums or decreased policy offerings due to COVID-19. |
| Keywords: | 2019 novel coronavirus, SARS-CoV-2, COVID-19, term life insurance, severe acute respiratory syndrome 2 |
| JEL: | D81 I13 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp13912&r=all |
| By: | H. Fink; S. Geissel; J. Herbinger; F. T. Seifried |
| Abstract: | The purpose of this article is to evaluate optimal expected utility risk measures (OEU) in a risk-constrained portfolio optimization context where the expected portfolio return is maximized. Wecompare the portfolio optimization with OEU constraint to a portfolio selection model using valueat risk as constraint. The former is a coherent risk measure for utility functions with constantrelative risk aversion and allows individual specifications to the investor’s risk attitude and timepreference. In a case study with three indices we investigate how these theoretical differences in-fluence the performance of the portfolio selection strategies. A copula approach with univariateARMA-GARCH models is used in a rolling forecast to simulate monthly future returns and cal-culate the derived measures for the optimization. The results of this study illustrate that bothoptimization strategies perform considerably better than an equally weighted portfolio and a buyand hold portfolio. Moreover, our results illustrate that portfolio optimization with OEU con-straint experiences individualized effects, e.g. less risk averse investors lose more portfolio value inthe financial crises but outperform their more risk averse counterparts in bull markets. |
| Keywords: | optimal expected utility, portfolio optimization, risk measures, value at risk |
| JEL: | G11 D81 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:trr:qfrawp:201907&r=all |
| By: | Filippo Giovannelli (Bank of Italy); Alessandra Iannamorelli (Bank of Italy); Aviram Levy (Bank of Italy); Marco Orlandi (Bank of Italy) |
| Abstract: | Banca d’Italia’s In-house Credit Assessment System (ICAS) is one of the sources for the valuation of collateral agreed upon within the Eurosystem’s monetary policy framework. It helps to provide liquidity to those Italian banks that cannot rely on an internal model (IRB). Its role has become all the more important in the aftermath of the financial crisis relating to the COVID-19 pandemic of 2020. The paper first outlines the Eurosystem’s collateral framework and describes Banca d’Italia’s ICAS in terms of architecture and governance. It then presents in detail the underlying statistical model, including the definition of default adopted, and the validation process for the statistical model and for the expert system. The paper concludes by providing data on the amount of collateral pledged with an ICAS rating and on the main features, including the probabilities of default, of the Italian non-financial companies rated by the system. |
| Keywords: | collateral framework, credit risk |
| JEL: | G32 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:opques:qef_586_20&r=all |
| By: | ; Joseph E. Stiglitz |
| Abstract: | The concentration of risk within financial system is considered to be a source of systemic instability. We propose a theory to explain the structure of the financial system and show how it alters the risk taking incentives of financial institutions. We build a model of portfolio choice and endogenous contracts in which the government optimally intervenes during crises. By issuing financial claims to other institutions, relatively risky institutions endogenously become large and interconnected. This structure enables institutions to share the risk of systemic crisis in a privately optimal way, but channels funds to relatively risky investments and creates incentives even for smaller institutions to take excessive risks. Constrained efficiency can be implemented with macroprudential regulation designed to limit the interconnectedness of risky institutions. |
| Keywords: | Systemic risk; Systemically important financial institutions; Interbank markets; Financial crises; Bailouts; Macroprudential supervision |
| JEL: | E61 G01 G18 G21 G28 |
| Date: | 2020–12–02 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2020-98&r=all |
| By: | J-C Gerlach (ETH Zürich - Department of Management, Technology, and Economics (D-MTEC)); Dongshuai Zhao, CFA (ETH Zürich - Department of Management, Technology, and Economics (D-MTEC)); Didier Sornette (ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); S wiss Finance Institute; Southern University of Science and Technology; Tokyo Institute of Technology) |
| Abstract: | Since 2009, stock markets have resided in a long bull market regime. Passive investment strategies have succeeded during this low-volatility growth period. From 2018 on, however, there was a transition into a more volatile market environment interspersed by corrections increasing in amplitude and frequency. This calls for more adaptive dynamic risk management strategies, as opposed to static buy-and-hold strategies. To hedge against market drawdowns, the greatest source of risk that should accurately be estimated is crash risk. This article applies the Log-Periodic Power Law Singularity (LPPLS) model of endogenous asset price bubbles to monitor crash risk. The model is calibrated to 15 years market history for five relevant equity country indices. Particular emphasis is put on the US S&P 500 Composite Index and the recent market history of the "Corona" year 2020. The results show that relevant historical bubble events, including the Corona crash, could be detected with the model and derived indicators. Many of these events were predicted in advance in monthly reports by the Financial Crisis Observatory (FCO) at ETH Zurich. The Corona crash, as the most recent event of interest, is discussed in further detail. Our conclusion is that unsustainable price dynamics leading to an unstable bubble, fuelled by quantitative easing and other policies, already existed well before the pandemic started. Thus, the bubble bursting in February 2020 as a reaction to the Corona pandemic was of endogenous nature and burst in response to the exogenous Corona crisis, which was predictable to some degree based on the endogenous price dynamics. Following the crash, a fast recovery of the price to pre-crisis levels ensued in the following months. This lets us conclude that, as long as the underlying origins and the macroeconomic environment that created this bubble do not change, the bubble will continue to grow and potentially spread to other sectors. This may cause even more hectic market behaviour, overreaction and volatile corrections in the future. |
| Keywords: | Financial Bubbles, Crashes, Forecasting, LPPLS Model, Dynamic Risk Management, Confidence Indicator |
| JEL: | C01 C53 C58 G01 G32 |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp20103&r=all |
| By: | Lars A. Lochstoer; Tyler Muir |
| Abstract: | We provide evidence that agents have slow-moving beliefs about stock market volatility that lead to initial underreaction to volatility shocks followed by delayed overreaction. These dynamics are mirrored in the VIX and variance risk premiums which reflect investor expectations about volatility and are also supported in surveys and in firm-level option prices. We embed these expectations into an asset pricing model and find that the model can account for a number of stylized facts about market returns and return volatility which are difficult to reconcile, including a weak, or even negative, risk-return tradeoff. |
| JEL: | G0 G12 G4 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28102&r=all |
| By: | Sebastian Infante; Guillermo Ordoñez |
| Abstract: | We show that aggregate volatility affects the extent to which agents can share idiosyncratic risks through the valuation of collateral. Both private and public assets are used in insurance markets as collateral, but their exposure to volatility differs. While aggregate volatility decreases the value of private assets—they are exposed to more variation—it increases the value of public assets—they become more valuable to smooth consumption intertemporally. Hence, a more volatile economy tends to damage risk sharing when the composition of collateral is biased toward private assets. As we show that a stable economy is more propitious to the creation of private collateral, stability makes risk sharing increasingly fragile to volatility shocks. We find empirical evidence that the higher use of private assets in the U.S. has affected the sensitivity of risk sharing to aggregate volatility as predicted by our model. |
| JEL: | E44 G12 G18 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28119&r=all |
| By: | Sara Almadani; Michael M. Batty; Danielle Nemschoff; Wayne Passmore |
| Abstract: | A safe asset is a debt instrument that is expected to maintain its value over time, especially during adverse systemic events. Changes in the supply of safe assets can have a significant influence on short-term, risk-free interest rates. (Ferreira & Shousha, 2020) "When the scarcity of safe asset[s] is acute, the zero lower bound (ZLB) becomes binding and the safe asset market equilibrates via a reduction in output…" |
| Date: | 2020–11–09 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2020-11-09-3&r=all |
| By: | Jack McCoy; Francisco J. Palomino; Ander Perez; Charles Press; Gerardo Sanz-Maldonado |
| Abstract: | This note examines whether the ability of nonfinancial corporations to meet their interest expenses out of earnings is a vulnerability for financial stability under current economic conditions. We measure this ability using the interest coverage ratio (ICR)—the ratio of earnings before interest and taxes relative to interest expenses—and project this ratio under different scenarios. |
| Date: | 2020–12–03 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2020-12-03-1&r=all |
| By: | Florian Exler; Igor Livshits; James MacGee; Michèle Tertilt |
| Abstract: | There is active debate over whether borrowers’ cognitive biases create a need for regulation to limit the misuse of credit. To tackle this question, we incorporate overoptimistic borrowers into an incomplete markets model with consumer bankruptcy. Lenders price loans, forming beliefs—type scores—about borrowers’ types. Since over-optimistic borrowers face worse income risk but incorrectly believe they are rational, both types behave identically. This gives rise to a tractable theory of type scoring as lenders cannot screen borrower types. Since rationals default less often, the partial pooling of borrowers generates cross-subsidization whereby overoptimists face lower than actuarially fair interest rates. Over-optimists make financial mistakes: they borrow too much and default too late. We calibrate the model to the US and quantitatively evaluate several policies to address these frictions: reducing the cost of default, increasing borrowing costs, imposing debt limits, and providing financial literacy education. While some policies lower debt and filings, they do not reduce overborrowing. Financial literacy education can eliminate financial mistakes, but it also reduces behavioral borrowers’ welfare by ending crosssubsidization. Score-dependent borrowing limits can reduce financial mistakes but lower welfare. |
| Keywords: | Consumer Credit, Over-Optimism, Financial Mistakes, Bankruptcy, Financial Literacy, Financial Regulation, Type Score, Cross-Subsidization |
| JEL: | E21 E49 G18 K35 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2020_245&r=all |
| By: | Young Shin Kim; Kum-Hwan Roh; Raphaël Douady (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | In this paper, we introduce a new time series model having a stochastic exponential tail. This model is constructed based on the Normal Tempered Stable distribution with a time-varying parameter. The model captures the stochastic exponential tail, which generates the volatility smile effect and volatility term structure in option pricing. Moreover, the model describes the time-varying volatility of volatility. We empirically show the stochastic skewness and stochastic kurtosis by applying the model to analyze S\&P 500 index return data. We present the Monte-Carlo simulation technique for the parameter calibration of the model for the S\&P 500 option prices. We can see that the stochastic exponential tail makes the model better to analyze the market option prices by the calibration. |
| Keywords: | Levy Process,Normal tempered stable distribution,Volatility of volatility,Stochastic exponential tail,Option Pricing |
| Date: | 2020–11–22 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:hal-03018495&r=all |
| By: | Riza Demirer (Department of Economics & Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany) |
| Abstract: | Yes, they do. Utilizing a machine-learning technique known as random forests to compute forecasts of realized (good and bad) stock market volatility, we show that incorporating the information in lagged industry returns can help improve out-of sample forecasts of aggregate stock market volatility. While the predictive contribution of industry level returns is not constant over time, industrials and materials play a dominant predictive role during the aftermath of the 2008 global financial crisis, highlighting the informational value of real economic activity on stock market volatility dynamics. Finally, we show that incorporating lagged industry returns in aggregate level volatility forecasts benefits forecasters who are particularly concerned about under-predicting market volatility, yielding greater economic benefits for forecasters as the degree of risk aversion increases. |
| Keywords: | Stock market; Realized volatility; Industry returns, Market efficiency and information |
| JEL: | G17 Q02 Q47 |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:2020107&r=all |
| By: | Xiaowei Chen; Wing Fung Chong; Runhuan Feng; Linfeng Zhang |
| Abstract: | Repeated history of pandemics, such as SARS, H1N1, Ebola, Zika, and COVID-19, has shown that pandemic risk is inevitable. Extraordinary shortages of medical resources have been observed in many parts of the world. Some attributing factors include the lack of sufficient stockpiles and the lack of coordinated efforts to deploy existing resources to the location of greatest needs. The paper investigates contingency planning and resources allocation from a risk management perspective, as opposed to the prevailing supply chain perspective. The key idea is that the competition of limited critical resources is not only present in different geographical locations but also at different stages of a pandemic. This paper draws on an analogy between risk aggregation and capital allocation in finance and pandemic resources planning and allocation for healthcare systems. The main contribution is to introduce new strategies for optimal stockpiling and allocation balancing spatio-temporal competitions of medical supply and demand. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.03200&r=all |
| By: | Felix Haase; Matthias Neuenkirch |
| Abstract: | Stock market recessions are often early warning signals for financial or economiccrises. Hence, forecasting bear markets is important for investors, policymak-ers, and economic agents in general. In our two-step procedure, we first iden-tify stock market regimes in the US using three different techniques (Markov-switching models, dating rules, and a na ̈ıve moving average). Second, we predictrecessions in the S&P 500 with the help of several modeling approaches, utilizingthe information of 92 macro-financial variables. Our results suggest that severalvariables are suitable for forecasting recessions in stock markets in-sample andout-of-sample. Our early warning models for the US equity market, in particu-lar those using principal components to aggregate the information in the macro-financial variables, provide a statistical improvement over several benchmarks. Inaddition, these generate economic value by boosting returns, improving the sharpratio and the omega, and substantially reducing drawdowns. |
| Keywords: | Dating Algorithms; Markov-Switching Models; Predictions; PrincipalComponent Analysis; Specific-to-General Approach; Stock Market Recessions. |
| JEL: | C53 G11 G17 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:trr:qfrawp:202003&r=all |
| By: | M. Casares (UPNA - Universidad Pública de Navarra [Espagne]); H. Khan (Carleton University); Jean-Christophe Poutineau (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR1 - Université de Rennes 1 - UNIV-RENNES - Université de Rennes - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We report empirical evidence indicating that US net business formation has recently turned more volatile, procyclical and persistent. To study these stylized facts, we estimate a DSGE model with endogenous entry and exit. Business units feature heterogeneous productivity and they shut down if the present value of expected future dividends falls below the current liquidation value. The model provides a better fit than a constant exit rate model with the fluctuations of US business formation. The introduction of the extensive margin amplifies the effects of technology and risk-premium shocks, and reduces the procyclicality of firm-level production. The main sources of variability of the US aggregate fluctuations during the Great Recession are countercyclical technology shocks, persistent adverse risk-premium shocks, and expansionary monetary policy shocks. |
| Keywords: | DSGE Models,Entry and exit,Extensive margin,US Business cycles,entry and exit,DSGE models,US business cycles |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03004552&r=all |
| By: | Nicola, Giancarlo; Cerchiello, Paola; Aste, Tomaso |
| Abstract: | In this work we investigate whether information theory measures like mutual information and transfer entropy, extracted from a bank network, Granger cause financial stress indexes like LIBOR-OIS (London Interbank Offered Rate-Overnight Index Swap) spread, STLFSI (St. Louis Fed Financial Stress Index) and USD/CHF (USA Dollar/Swiss Franc) exchange rate. The information theory measures are extracted from a Gaussian Graphical Model constructed from daily stock time series of the top 74 listed US banks. The graphical model is calculated with a recently developed algorithm (LoGo) which provides very fast inference model that allows us to update the graphical model each market day. We therefore can generate daily time series of mutual information and transfer entropy for each bank of the network. The Granger causality between the bank related measures and the financial stress indexes is investigated with both standard Granger-causality and Partial Granger-causality conditioned on control measures representative of the general economy conditions. |
| Keywords: | financial stress; granger causality; graphical models |
| JEL: | F3 G3 |
| Date: | 2020–11–23 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:107563&r=all |
| By: | Olkhov, Victor |
| Abstract: | We suggest use continuous numerical risk grades [0,1] of R for a single risk or the unit cube in Rn for n risks as the economic domain. We consider risk ratings of economic agents as their coordinates in the economic domain. Economic activity of agents, economic or other factors change agents risk ratings and that cause motion of agents in the economic domain. Aggregations of variables and transactions of individual agents in small volume of economic domain establish the continuous economic media approximation that describes collective variables, transactions and their flows in the economic domain as functions of risk coordinates. Any economic variable A(t,x) defines mean risk XA(t) as risk weighted by economic variable A(t,x). Collective flows of economic variables in bounded economic domain fluctuate from secure to risky area and back. These fluctuations of flows cause time oscillations of macroeconomic variables A(t) and their mean risks XA(t) in economic domain and are the origin of any business and credit cycles. We derive equations that describe evolution of collective variables, transactions and their flows in the economic domain. As illustration we present simple self-consistent equations of supply-demand cycles that describe fluctuations of supply, demand and their mean risks. |
| Keywords: | business cycle; risk ratings; collective variables; economic flows; economic domain |
| JEL: | C53 E32 E37 F44 |
| Date: | 2020–12–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:104598&r=all |
| By: | Raphaël Douady (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Yao Kuang |
| Abstract: | Financial crises is an important research topic because of their impact on the economy, the businesses and the populations. However, prior research tend to show systemic risk measures which are reactive, in the sense that risk surges after the crisis starts. Few of them succeed in predicting financial crises in advance. In this paper, we first introduce a toy model based on a dynamic regime switching process producing normal mixture distributions. We observe that the relative concavity of various indices increases before a crisis. We use this stylized fact to introduce a measure of concavity from nonlinear Polymodel, as a crisis risk indicator, and test it against known crises. We validate the indicator by using it for a trading strategy that holds long or short positions on S&P 500, depending on the indicator value. |
| Keywords: | crisis risk,financial crisis,concavity,Polymodel,trading strategy |
| Date: | 2020–11–22 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:hal-03018481&r=all |
| By: | Van der Ghote, Alejandro |
| Abstract: | I study macro-prudential policy intervention in economies with secularly low interest rates. Intervention boosts risk-free real interest rates unintentionally, simply as a by-product of containing systemic risk in financial markets. Thus, intervention also boosts the natural rate of return in particular (i.e., the equilibrium risk-free rate that is consistent with inflation on target and production at full capacity). These results point to a novel complementarity between financial stability and macroeconomic stabilization. Complementary is sufficiently strong to generate a divine coincidence if the natural rate is secularly low, but not too low. JEL Classification: E31, E32, E44 |
| Keywords: | macro-prudential policy, natural rate of return, systemic risk |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20202498&r=all |
| By: | Avela, Aleksi; Lehmus, Markku |
| Abstract: | Abstract Uncertainty may affect economic behavior of individuals and firms in a wide variety of ways, with typically negative consequences for economic growth. It is due to this fact, combined with rising political uncertainty observed lately in many countries, that uncertainty has gained increasing attention in economic literature, too. In this paper, we construct a measure of economic uncertainty for Finland based on Finnish news titles, collected from the YLE’s (the Finnish broadcasting company) website. To construct the index, we utilize machine learning and natural language processing (NLP) techniques, and in this paper, specifically, a transformed naive Bayes text classifier. On basis of the model evaluation, the constructed uncertainty index seems helpful in giving a timely assessment of the current state of the Finnish economy. We find a strong negative correlation between our index and the consumer confidence index by Statistics Finland, and most remarkably, our index seems to lead the consumer confidence index by one month. |
| Keywords: | Economic uncertainty, Nowcasting, Machine learning, Natural language processing, Naive Bayes |
| JEL: | C45 C53 C61 E71 |
| Date: | 2020–12–08 |
| URL: | http://d.repec.org/n?u=RePEc:rif:wpaper:84&r=all |