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on Risk Management |
| By: | Roberto Baviera |
| Abstract: | Credit capital requirements in Internal Rating Based approaches require the calibration of two key parameters: the probability of default and the loss-given-default. This letter considers the uncertainty about these two parameters and models this uncertainty in an elementary way: it shows how this estimation risk can be computed and properly taken into account in regulatory capital. We analyse two standard real datasets: one composed by all corporates rated by Moody's and one limited only to the speculative grade ones. We statistically test model hypotheses on both marginal distributions and parameter dependency. We compute the estimation risk impact and observe that parameter dependency raises substantially the tail risk in capital requirements. The results are striking with a required increase in regulatory capital in the range $38\%$-$66\%$. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.08028&r=all |
| By: | Emese Lazar. Shuyuan Qi; Radu Tunaru |
| Abstract: | Measuring model risk is required by regulators on financial and insurance markets. We separate model risk into parameter estimation risk and model specification risk, and we propose expected shortfall type model risk measures applied to Levy jump models and affine jump-diffusion models. We investigate the impact of parameter estimation risk and model specification risk on the models'ability to capture the joint dynamics of stock and option prices. We estimate the parameters using Markov chain Monte Carlo techniques, under the risk-neutral probability measure and the real-world probability measure jointly. We find strong evidence supporting modeling of price jumps. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.08113&r=all |
| By: | Bent Jesper Christensen (Aarhus University, CREATES and the Dale T. Mortensen Center); Juan Carlos Parra-Alvarez (Aarhus University and CREATES); Rafael Serrano (Universidad del Rosario) |
| Abstract: | A risk-averse insurance company controls its reserve, modelled as a perturbed Cramér-Lundberg process, by choice of both the premium p and the deductible K offered to potential customers. The surplus is allocated to financial investment in a riskless and a basket of risky assets potentially correlating with the insurance risks and thus serving as a partial hedge against these. Assuming customers differ in riskiness, increasing p or K reduces the number of customers n(p,K) and increases the arrival rate of claims per customer ?(p,K) through adverse selection, with a combined negative effect on the aggregate arrival rate n(p,K)?(p,K). We derive the optimal premium rate, deductible, investment strategy, and dividend payout rate (consumption by the owner-manager) maximizing expected discounted life-time utility of intermediate consumption under the assumption of constant absolute risk aversion. Closed-form solutions are provided under specific assumptions on the distributions of size and frequency claims. |
| Keywords: | Stochastic optimal control, Hamilton-Jacobi-Bellman equation, Jump-diffusion, Adverse selection, Premium control, Deductible control, Optimal investment strategy. |
| JEL: | G11 G22 C60 D82 |
| Date: | 2020–10–12 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2020-11&r=all |
| By: | Edoardo Vittori; Michele Trapletti; Marcello Restelli |
| Abstract: | In this paper we show how risk-averse reinforcement learning can be used to hedge options. We apply a state-of-the-art risk-averse algorithm: Trust Region Volatility Optimization (TRVO) to a vanilla option hedging environment, considering realistic factors such as discrete time and transaction costs. Realism makes the problem twofold: the agent must both minimize volatility and contain transaction costs, these tasks usually being in competition. We use the algorithm to train a sheaf of agents each characterized by a different risk aversion, so to be able to span an efficient frontier on the volatility-p\&l space. The results show that the derived hedging strategy not only outperforms the Black \& Scholes delta hedge, but is also extremely robust and flexible, as it can efficiently hedge options with different characteristics and work on markets with different behaviors than what was used in training. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.12245&r=all |
| By: | Xing Yan; Weizhong Zhang; Lin Ma; Wei Liu; Qi Wu |
| Abstract: | We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns. Our model captures well both the time-varying characteristic and the asymmetrical heavy-tail property of financial time series. It combines the merits of a popular sequential neural network model, i.e., LSTM, with a novel parametric quantile function that we construct to represent the conditional distribution of asset returns. Our model also captures individually the serial dependences of higher moments, rather than just the volatility. Across a wide range of asset classes, the out-of-sample forecasts of conditional quantiles or VaR of our model outperform the GARCH family. Further, the proposed approach does not suffer from the issue of quantile crossing, nor does it expose to the ill-posedness comparing to the parametric probability density function approach. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.08263&r=all |
| By: | Xia Han; Zhibin Liang |
| Abstract: | In this paper, we study an optimal reinsurance-investment problem in a risk model with two dependent classes of insurance business, where the two claim number processes are correlated through a common shock component. We assume that the insurer can purchase per-loss reinsurance for each line of business and invest its surplus in a financial market consisting of a risk-free asset and a risky asset. Under the criterion of minimizing the probability of drawdown, the closed-form expressions of the optimal reinsurance-investment strategy and the corresponding value function are obtained. We show that the optimal reinsurance strategy is in the form of pure excess-of-loss reinsurance strategy under the expected value principle, and under the variance premium principle, the optimal reinsurance strategy is in the form of pure quota-share reinsurance. Furthermore, we extend our model to the case where the insurance company involves $n$ $(n\geq3)$ dependent classes of insurance business and the optimal results are derived explicitly as well. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.12158&r=all |
| By: | Mike Ludkovski; Yuri Saporito |
| Abstract: | We investigate a machine learning approach to option Greeks approximation based on Gaussian process (GP) surrogates. The method takes in noisily observed option prices, fits a nonparametric input-output map and then analytically differentiates the latter to obtain the various price sensitivities. Our motivation is to compute Greeks in cases where direct computation is expensive, such as in local volatility models, or can only ever be done approximately. We provide a detailed analysis of numerous aspects of GP surrogates, including choice of kernel family, simulation design, choice of trend function and impact of noise. We further discuss the application to Delta hedging, including a new Lemma that relates quality of the Delta approximation to discrete-time hedging loss. Results are illustrated with two extensive case studies that consider estimation of Delta, Theta and Gamma and benchmark approximation quality and uncertainty quantification using a variety of statistical metrics. Among our key take-aways are the recommendation to use Matern kernels, the benefit of including virtual training points to capture boundary conditions, and the significant loss of fidelity when training on stock-path-based datasets. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.08407&r=all |
| By: | Mohammad Bitar (Nottingham University Business School, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, United Kingdom.); Amine Tarazi (LAPE - Laboratoire d'Analyse et de Prospective Economique - GIO - Gouvernance des Institutions et des Organisations - UNILIM - Université de Limoges) |
| Abstract: | We see spikes in unemployment rates and turbulence in the securities markets during the COVID-19 pandemic. Governments are responding with aggressive monetary expansions and large-scale economic relief plans. We discuss the implications on banks and the economy of prudential regulatory intervention to soften the treatment of non-performing loans and ease bank capital buffers. We apply these easing measures on a sample of Globally Systemically Important Banks (G-SIBs) and show that these banks can play a constructive role in sustaining economic growth during the COVID-19 pandemic. However, softening the treatment of non-performing loans along with easing capital buffers should not undermine banks' solvency in the recovery period. Banks should maintain usable buffer in the medium-term horizon to absorb future losses, as the effect of COVID-19 on the economy might take time to fully materialise. |
| Keywords: | COVID-19,non-performing loans,capital buffers,solvency,G-SIBs |
| Date: | 2020–10–12 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-02964598&r=all |
| By: | Xiaosheng Mu; Luciano Pomatto; Philipp Strack; Omer Tamuz |
| Abstract: | We show that under plausible levels of background risk, no theory of choice under risk---such as expected utility theory, prospect theory, or rank dependent utility---can simultaneously satisfy the following three economic postulates: (i) Decision makers are risk-averse over small gambles, (ii) they respect stochastic dominance, and (iii) they account for background risk. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.08033&r=all |
| By: | Vo, Duc |
| Abstract: | This article is conducted to examine risk, return, and portfolio optimization at the industry level in China over the period 2007–2016. On the ground of the classical Markowitz framework for portfolio optimization, the mean-semivariance optimization framework is established for China’s stock market at the industry level. Findings from this study indicate that healthcare sector plays a significant role among 10 industries in China on a stand-alone basis. In addition, a significant change of rankings among the sectors in term of risk is found when the mean-semivariance optimization framework is used. We also find that utilizing this new framework helps improve the optimal portfolios in relation to performance, measured by Sortino ratio, and diversification. A simulation technique, generally known as resampling method, is also utilized to check the robustness of the estimates. While the use of this resampling method appears not to improve the performance of optimal portfolios compared with the mean-semivariance framework for China, there is a remarkable advance in diversification of the optimal portfolios. Implications for investors and the governments in Vietnam and other emerging markets have emerged from the study. |
| Keywords: | China, mean-semivariance, portfolio optimization, resample |
| JEL: | G2 G21 G28 |
| Date: | 2019–09–19 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:103276&r=all |
| By: | Nurlan Turdaliev (Department of Economics, University of Windsor); Yahong Zhang (Department of Economics, University of Windsor) |
| Abstract: | The collapse of housing prices in the aftermath of the U.S. subprime mortgage crisis of 2008 not only worsened the balance sheet positions of the banking sector but also led to a “bank run” in some cases such as the collapse of Lehman Brothers in September 2008. We develop a theoretical model featuring household debt (mortgages) and banking sector frictions. We show that mortgage risks can potentially lead to a bank run equilibrium. Such an equilibrium exists since mortgage risks reduce the liquidation prices of bank assets. We further show that mortgage market regulations such as loan-to-value requirements reduce the likelihood of bank runs. |
| Keywords: | bank run, mortgage risk, loan-to-value ratio |
| JEL: | E32 E44 G01 G21 G33 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:wis:wpaper:2007&r=all |
| By: | Ryan Niladri Banerjee; Giulio Cornelli; Egon Zakrajšek |
| Abstract: | Economic growth and forward-looking indicators of default risk inferred from equity markets, two variables that together predict business bankruptcies in advanced economies, show bankruptcies rising significantly by the end of 2021. Projections of real GDP growth embedded in the consensus forecast account for the bulk of this projected increase. Unlike in previous downturns, the stock market-based default indicators contribute very little. As these findings underscore, the pandemic and unprecedented government support for the business sector have driven a sizeable wedge between financial market perceptions of default risk and projections for economic activity. |
| Date: | 2020–10–12 |
| URL: | http://d.repec.org/n?u=RePEc:bis:bisblt:30&r=all |