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on Risk Management |
| By: | Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, USA); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, Hamburg, Germany) |
| Abstract: | We study the incremental in- and out-of-sample predictive value of time-varying risk aversion for realized volatility of gold-price returns via extended heterogeneous autoregressive realized volatility (HAR-RV) models. Our findings suggest that time varying risk aversion possesses predictive value for gold volatility both in- and out-of-sample. The predictive power of risk aversion is robust to the inclusion of realized higher-moments, jumps, gold returns, leverage effect as well as the aggregate market volatility in the forecasting model. Interestingly, risk aversion is found to absorb in sample the predictive power of stock-market volatility at a short forecasting horizon, while out-of-sample results show that risk aversion adds to predictive value at a medium and long forecast horizon. Additional tests suggest that the short-run (long-run) out-of-sample predictive value of risk aversion is beneficial for investors who are more concerned about over-predicting (under-predicting) gold market volatility. Overall, our findings show that time-varying risk aversion captures information useful for predicting (bad, good) realized volatility not already contained in the other predictors, and allows more accurate out-of-sample forecasts to be computed at a medium and long forecast horizon. |
| Keywords: | Gold-price returns, Realized volatility, Forecasting |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201881&r=rmg |
| By: | Eickmeier, Sandra; Kolb, Benedikt; Prieto, Esteban |
| Abstract: | Bank capital regulations are intended to enhance financial stability in the long run, but may, in the meanwhile, involve costs for the real economy. To examine these costs we propose a narrative index of aggregate tightenings in regulatory US bank capital requirements from 1979 to 2008. Anticipation effects are explicitly taken into account and found to matter. In response to a tightening in capital requirements, banks temporarily reduce business and real estate lending, which temporarily lowers investment, consumption, housing activity and production. A decline in financial and macroeconomic risk helps sustain spending in the medium run. Monetary policy also cushions negative effects of capital requirement tightenings on the economy. |
| Keywords: | Narrative Approach,Bank Capital Requirements,Local Projections |
| JEL: | G28 G18 C32 E44 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:442018&r=rmg |
| By: | Xuan Lu; Li Huang; Kangjuan Lyu |
| Abstract: | The insufficient understanding of the credit network structure was recognized as a key factor for regulators' underestimation of the destructive systematic risk during the financial crisis that started in 2007. The existing credit network research either took a macro perspective to clarify the topological properties of financial systems at a descriptive level or analyzed the risk transmission path and characteristics of individual entities with much pre-assumptions of the network. Here, we used the theory of complex network to model China's credit system from 2000 to 2014 based on actual financial data. A bipartite financial institution-firm network and its projected sub-networks were constructed for an integrated analysis from both macro and micro perspectives, and the relationship between typological properties and systematic credit risk control was also explored. The typological analysis of the networks suggested that the financial institutions and firms were highly but asymmetrically connected, and the credit network structure made local idiosyncratic shocks possible to proliferate through the whole economy. In addition, the Chinese credit market was still dominated by state-owned financial institutions with firms competing fiercely for financial resources in the past fifteen years. Furthermore, the credit risk score (CRS) was introduced by simulation to identify the systematically important vertices in terms of systematic risk control. The results indicated that the vertices with more access to the credit market or less likelihood to be a bridge in the network were the ones with higher systematically importance. The empirical results from this study would provide specific policy suggestions to financial regulators on supervisory approaches and optimizing the allocation of regulatory resources to enhance the robustness of credit systems in China and in other countries. |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1812.01341&r=rmg |
| By: | Adrian, Tobias; Grinberg, Federico; Liang, Nellie; Malik, Sheherya |
| Abstract: | Using panel quantile regressions, we show that the conditional distribution of GDP growth depends on financial conditions, with growth-at-risk (GaR)-defined as conditional growth at the lower 5th percentile-more responsive than the median or upper percentiles. The term structure of GaR features an intertemporal tradeoff: GaR is higher in the short run but lower in the medium run when initial financial conditions are loose relative to typical levels. This shift in the growth distribution generally is not incorporated when solving dynamic stochastic general equilibrium models with macrofinancial linkages, which suggests downside risks to GDP growth are systematically underestimated. |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13349&r=rmg |
| By: | David Turner; Thomas Chalaux; Hermes Morgavi |
| Abstract: | This paper describes a method for parameterising fan charts around GDP growth forecasts of the major OECD economies as well as the aggregate OECD. The degree of uncertainty – reflecting the overall spread of the fan chart – is based on past forecast errors, but the skew – reflecting whether risks are tilted to the downside – is derived from a probit model-based assessment of the probability of a future downturn. This approach is applied to each of the G7 countries separately, with combinations of variables found to be useful in predicting future downturns at different horizons up to 8 quarters: at short horizons of 2-4 quarters, a flattening or inverted yield curve slope, recent sharp falls in house prices, share prices or credit; at longer horizons of 6-8 quarters, sustained strong growth in house prices, share prices and credit; and at all horizons, a tight labour market and rapid growth in OECD-wide (or in some cases euro-wide) house prices, share prices or credit. The in-sample fit of the probit models appears reasonably good for all G7 countries. The predicted probabilities from the probit models provide a graduated assessment of downturn risk, which is reflected in the degree of skew in the fan chart. Fan charts computed on an out-of-sample basis around pre-crisis OECD forecasts published in June 2008 encompass the extreme outturns associated with the Global Financial Crisis for five of the G7 countries. A weakness of the approach is that, although it predicts a clear majority of past downturns, it will not predict atypical downturns. For example, in the current conjuncture, it is unlikely that current concerns about risks associated with Brexit, an escalation of trade tensions or spillovers from emerging markets would be picked up by the models. At the same time, a severe downturn triggered by such atypical events might be more severe if more typical risk factors are also high. |
| Keywords: | downturn, economic forecasts, fan charts, recession, risk, uncertainty |
| JEL: | E01 E17 E58 E65 E66 |
| Date: | 2018–12–11 |
| URL: | http://d.repec.org/n?u=RePEc:oec:ecoaaa:1521-en&r=rmg |
| By: | Catalina Bolancé (Department of Econometrics, Riskcenter-IREA, University of Barcelona, Avinguda Diagonal 690, 08034 Barcelona, Spain.); Ricardo Cao (Research Group MODES, Department of Mathematics, CITIC, Universidade da Coruña and ITMATI Campus de Elviña, s/n 15071 A Coruña, Spain.); Montserrat Guillen (Department of Econometrics, Riskcenter-IREA, University of Barcelona, Avinguda Diagonal 690, 08034 Barcelona, Spain.) |
| Abstract: | Estimation in single-index models for risk assessment is developed. Statistical properties are given and an application to estimate the cost of traffic accidents in an innovative insurance data set that has information on driving style is presented. A new kernel approach for the estimator covariance matrix is provided. Both, the simulation study and the real case show that the method provides the best results when data are highly skewed and when the conditional distribution is of interest. Supplementary materials containing appendices are available online. |
| Keywords: | Insurance loss data, heavy tailed distributions, quantiles, non-parametric conditional distribution. JEL classification:C51, C14, G22 |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:ira:wpaper:201829&r=rmg |
| By: | Olessia CAILLÉ; Daria ONORI |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:leo:wpaper:2629&r=rmg |
| By: | Moshe A. Milevsky |
| Abstract: | Who {\em values} life annuities more? Is it the healthy retiree who expects to live long and might become a centenarian, or is the unhealthy retiree with a short life expectancy more likely to appreciate the pooling of longevity risk? What if the unhealthy retiree is pooled with someone who is much healthier and thus forced to pay an implicit loading? To answer these and related questions this paper examines the empirical conditions under which retirees benefit (or may not) from longevity risk pooling by linking the {\em economics} of annuity equivalent wealth (AEW) to {\em actuarially} models of aging. I focus attention on the {\em Compensation Law of Mortality} which implies that individuals with higher relative mortality (e.g. lower income) age more slowly and experience greater longevity uncertainty. Ergo, they place higher utility value on the annuity. The impetus for this research today is the increasing evidence on the growing disparity in longevity expectations between rich and poor. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.11326&r=rmg |
| By: | Riccardo Colacito; Mariano Max Croce; Yang Liu; Ivan Shaliastovich |
| Abstract: | We develop a novel measure of volatility pass-through to assess international propagation of output volatility shocks to macroeconomic aggregates, equity prices, and currencies. An increase in country's output volatility is associated with a decrease in its output, consumption, and net exports. The average consumption pass-through is 50% (a 1% increase in output volatility increases consumption volatility by 0.5%) and it increases to 70% for shocks originating in smaller countries. The equity volatility pass-through is 90%, whereas the link between volatility of currency and fundamentals is weak. A novel channel of risk sharing of volatility risks can explain our empirical findings. |
| JEL: | F3 G12 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:25276&r=rmg |
| By: | Alexander Heinemann; Sean Telg |
| Abstract: | This paper studies a fixed-design residual bootstrap method for the two-step estimator of Francq and Zako\"ian (2015) associated with the conditional Expected Shortfall. For a general class of volatility models the bootstrap is shown to be asymptotically valid under the conditions imposed by Beutner et al. (2018). A simulation study is conducted revealing that the average coverage rates are satisfactory for most settings considered. There is no clear evidence to have a preference for any of the three proposed bootstrap intervals. This contrasts results in Beutner et al. (2018) for the VaR, for which the reversed-tails interval has a superior performance. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.11557&r=rmg |
| By: | João F. Gomes; Marco Grotteria; Jessica Wachter |
| Abstract: | Financial crises tend to follow rapid credit expansions. Causality, however, is far from obvious. We show how this pattern arises naturally when financial intermediaries optimally exploit economic rents that drive their franchise value. As this franchise value fluctuates over the business cycle, so too do the incentives to engage in risky lending. The model leads to novel insights on the effects of recent unconventional monetary policies in developed economies. We argue that bank lending might have responded less than expected to these interventions because they enhanced franchise value, inadvertently encouraging banks to pursue safer investments in low-risk government securities. |
| JEL: | G01 G18 G21 G32 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:25277&r=rmg |
| By: | Suproteem K. Sarkar; Kojin Oshiba; Daniel Giebisch; Yaron Singer |
| Abstract: | Algorithms are increasingly common components of high-impact decision-making, and a growing body of literature on adversarial examples in laboratory settings indicates that standard machine learning models are not robust. This suggests that real-world systems are also susceptible to manipulation or misclassification, which especially poses a challenge to machine learning models used in financial services. We use the loan grade classification problem to explore how machine learning models are sensitive to small changes in user-reported data, using adversarial attacks documented in the literature and an original, domain-specific attack. Our work shows that a robust optimization algorithm can build models for financial services that are resistant to misclassification on perturbations. To the best of our knowledge, this is the first study of adversarial attacks and defenses for deep learning in financial services. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.11079&r=rmg |