|
on Risk Management |
| By: | Muteba Mwamba, John Weirstrass; Tchuinkam Djemo, Charles Raoul |
| Abstract: | This paper examines the impact of foreign exchange rate risk on the expected return of a South African investor’s portfolio. A GJR-GARCH based Value at Risk (VaR) model was used to compute the upside and downside risk measures. Data sample of ten emerging stock markets were utilized: from 1 January 2000 to 6 March 2019. The tails of negative and positive asset returns were modelled with the help of the generalized Pareto distribution (GPD) method in order to separate left tail risk from right tail risk. Our findings reveal that international diversification substantially enhances the South African investor’s portfolio return, with a noticeable yield increase in China, Brazil, Argentina, Mexico, and Russia. Furthermore, the Singaporean dollar and Chinese Yuan are found to have a negative impact on the portfolio return, while the rest of the currencies have a positive impact on the portfolio return. Also, we found that exchange rate risk is underestimated when using the variance-covariance method as it fails to capture the swing movement of currency in the minimum- value at risk optimization. |
| Keywords: | International Diversification, Exchange Rate Risk, Portfolio Selection, Value at Risk |
| JEL: | F3 F31 F37 G1 G14 G17 |
| Date: | 2019–12–03 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:97338&r=all |
| By: | Andreas A. Jobst; Hiroko Oura |
| Abstract: | This paper explains the treatment of sovereign risk in macroprudential solvency stress testing, based on the experiences in the Financial Sector Assessment Program (FSAP). We discuss four essential steps in assessing the system-wide impact of sovereign risk: scope, loss estimation, shock calibration, and capital impact calculation. Most importantly, a market-consistent valuation approach lies at the heart of assessing the resilience of the financial sector in a tail risk scenario with sovereign distress. We present a flexible, closed-form approach to calibrating haircuts based on changes in expected sovereign defaults affecting bank solvency during adverse macroeconomic conditions. This paper demonstrates the effectiveness of using extreme value theory (EVT) in this context, with empirical examples from past FSAPs. |
| Date: | 2019–12–06 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:19/266&r=all |
| By: | Marcel Br\"autigam; Marie Kratz |
| Abstract: | Procyclicality of historical risk measure estimation means that one tends to over-estimate future risk when present realized volatility is high and vice versa under-estimate future risk when the realized volatility is low. Out of it different questions arise, relevant for applications and theory: What are the factors which affect the degree of procyclicality? More specifically, how does the choice of risk measure affect this? How does this behaviour vary with the choice of realized volatility estimator? How do different underlying model assumptions influence the pro-cyclical effect? In this paper we consider three different well-known risk measures (Value-at-Risk, Expected Shortfall, Expectile), the r-th absolute centred sample moment, for any integer $r>0$, as realized volatility estimator (this includes the sample variance and the sample mean absolute deviation around the sample mean) and two models (either an iid model or an augmented GARCH($p$,$q$) model). We show that the strength of procyclicality depends on these three factors, the choice of risk measure, the realized volatility estimator and the model considered. But, no matter the choices, the procyclicality will always be present. |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2001.00529&r=all |
| By: | Mirko Moscatelli (Bank of Italy); Simone Narizzano (Bank of Italy); Fabio Parlapiano (Bank of Italy); Gianluca Viggiano (Bank of Italy) |
| Abstract: | We analyze the performance of a set of machine learning (ML) models in predicting default risk, using standard statistical models, such as the logistic regression, as a benchmark. When only a limited information set is available, for example in the case of financial indicators, we find that ML models provide substantial gains in discriminatory power and precision compared with statistical models. This advantage diminishes when high quality information, such as credit behavioral indicators obtained from the Credit Register, is also available, and becomes negligible when the dataset is small. We also evaluate the consequences of using an ML-based rating system on the supply of credit and the number of borrowers gaining access to credit. ML models channel a larger share of credit towards safer and larger borrowers and result in lower credit losses for lenders. |
| Keywords: | Credit Scoring, Machine Learning, Random Forest, Gradient Boosting Machine |
| JEL: | G2 C52 C55 D83 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:wptemi:td_1256_19&r=all |
| By: | Abootaleb Shirvani; Frank J. Fabozzi; Stoyan V. Stoyanov |
| Abstract: | In this paper, we combine modern portfolio theory and option pricing theory so that a trader who takes a position in a European option contract and the underlying assets can construct an optimal portfolio such that at the moment of the contract's maturity the contract is perfectly hedged. We derive both the optimal holdings in the underlying assets for the trader's optimal mean-variance portfolio and the amount of unhedged risk prior to maturity. Solutions assuming the cases where the price dynamics in the underlying assets follow discrete binomial price dynamics, continuous diffusions, stochastic volatility, volatility-of-volatility, and Merton-jump diffusion are derived. |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2001.00737&r=all |
| By: | Chengyu Huang; Sean Simpson; Daria Ulybina; Agustin Roitman |
| Abstract: | We construct sentiment indices for 20 countries from 1980 to 2019. Relying on computational text analysis, we capture specific language like “fear”, “risk”, “hedging”, “opinion”, and, “crisis”, as well as “positive” and “negative” sentiments, in news articles from the Financial Times. We assess the performance of our sentiment indices as “news-based” early warning indicators (EWIs) for financial crises. We find that sentiment indices spike and/or trend up ahead of financial crises. |
| Date: | 2019–12–06 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:19/273&r=all |
| By: | Leonardo Massai; Giacomo Como; Fabio Fagnani |
| Abstract: | We undertake a fundamental study of network equilibria modeled as solutions of fixed point of monotone linear functions with saturation nonlinearities. The considered model extends one originally proposed to study systemic risk in networks of financial institutions interconnected by mutual obligations and is one of the simplest continuous models accounting for shock propagation phenomena and cascading failure effects. We first derive explicit expressions for network equilibria and prove necessary and sufficient conditions for their uniqueness encompassing and generalizing several results in the literature. Then, we study jump discontinuities of the network equilibria when the exogenous flows cross a certain critical region consisting of the union of finitely many linear submanifolds of co-dimension 1. This is of particular interest in the financial systems context, as it shows that even small shocks affecting the values of the assets of few nodes, can trigger catastrophic aggregated loss to the system and cause the default of several agents. |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1912.04815&r=all |
| By: | Nikil Chande; Dennis Yanchus |
| Abstract: | The Canadian financial system is vulnerable to cyber threats. But for many firms, cyber risk is difficult to quantify. We examine public information on past cyber incidents to better understand the current risk landscape and find that a holistic view is needed to fully grasp the nature of this risk. |
| Keywords: | Financial markets; Financial stability |
| JEL: | G28 M15 O33 O38 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocsan:19-32&r=all |
| By: | Aditya Maheshwari; Traian Pirvu |
| Abstract: | We consider the problem of portfolio optimization with a correlation constraint. The framework is the multiperiod stochastic financial market setting with one tradable stock, stochastic income and a non-tradable index. The correlation constraint is imposed on the portfolio and the non-tradable index at some benchmark time horizon. The goal is to maximize portofolio's expected exponential utility subject to the correlation constraint. Two types of optimal portfolio strategies are considered: the subgame perfect and the precommitment ones. We find analytical expressions for the constrained subgame perfect (CSGP) and the constrained precommitment (CPC) portfolio strategies. Both these portfolio strategies yield significantly lower risk when compared to the unconstrained setting, at the cost of a small utility loss. The performance of the CSGP and CPC portfolio strategies is similar. |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1912.12521&r=all |
| By: | Anand Deo; Sandeep Juneja |
| Abstract: | We consider discrete default intensity based and logit type reduced form models for conditional default probabilities for corporate loans where we develop simple closed form approximations to the maximum likelihood estimator (MLE) when the underlying covariates follow a stationary Gaussian process. In a practically reasonable asymptotic regime where the default probabilities are small, say 1-3% annually, the number of firms and the time period of data available is reasonably large, we rigorously show that the proposed estimator behaves similarly or slightly worse than the MLE when the underlying model is correctly specified. For more realistic case of model misspecification, both estimators are seen to be equally good, or equally bad. Further, beyond a point, both are more-or-less insensitive to increase in data. These conclusions are validated on empirical and simulated data. The proposed approximations should also have applications outside finance, where logit-type models are used and probabilities of interest are small. |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1912.12611&r=all |
| By: | Fries, Sébastien |
| Abstract: | Noncausal, or anticipative, alpha-stable processes generate trajectories featuring locally explosive episodes akin to speculative bubbles in financial time series data. For (X_t) a two-sided infinite alpha-stable moving average (MA), conditional moments up to integer order four are shown to exist provided (X_t) is anticipative enough. The functional forms of these moments at any forecast horizon under any admissible parameterisation are obtained by extending the literature on arbitrary bivariate alpha-stable random vectors. The dynamics of noncausal processes simplifies during explosive episodes and allows to express ex ante crash odds at any horizon in terms of the MA coefficients and of the tail index alpha. The results are illustrated in a synthetic portfolio allocation framework and an application to the Nasdaq and S&P500 series is provided. |
| Keywords: | Noncausal processes, Multivariate stable distributions, Conditional dependence, Extremal dependence, Explosive bubbles, Prediction, Crash odds, Portfolio allocation |
| JEL: | C22 C53 C58 |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:97353&r=all |
| By: | Muteba Mwamba, John Weirstrass; Mhlophe, Bongani |
| Abstract: | This paper examines the extraction of the empirical asset correlation for three datasets of monthly defaults on loans and credit cards obtained from the SARB from February 2006 to January 2017. The study makes use of the Beta and Vasicek distributions over a static period of time, as well as a rolling period of time. However two different calculation approaches (mode and percentile) are used for the Vasicek distribution assumption. We first use these three distinct calculation approaches to empirically estimate the asset correlation over a static period of time and compare them to the BCBS (Basel Committee for Bank Supervision) prescribed asset correlations. The computed empirical asset correlations are thereafter used to determine the economic capital and compare it to the economic capital determined using the BCBS prescribed asset correlations. Secondly, we use these three distinct calculation approaches to empirically estimate the asset correlation over a rolling five-year period and compare them to the BCBS’ prescribed asset correlations. For both the static and five-year rolling empirical asset correlations, we show that the BCBS’ prescribed asset correlations are much higher than the empirical asset correlations for the South African loans dataset. However, the opposite is found for both the credit card default and writeoff datasets which had higher empirical asset correlations. The economic capital charge calculated using the computed empirical asset correlations is lower than the economic capital calculated using the BCBS’ prescribed asset correlations for the South African loans dataset, while the opposite result is found for both the credit card default and write-off datasets. This result implies that the BCBS’ prescribed asset correlation is not as conservative as intended for South African bank specific credit cards and that the required capital charge stipulated by the BCBS is not sufficient to cover unexpected losses. This may have dire consequences to the South African banking system through systemic risk. Therefore, we recommend that the capital levels be raised to match the capital levels determined in this study. |
| Keywords: | asset correlation, Vasicek distribution, Beta distribution, BCBS, economic capital, credit card defaults |
| JEL: | C1 C15 G1 G12 G3 |
| Date: | 2019–08–14 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:97340&r=all |
| By: | Yang Lu (CEPN - Centre d'Economie de l'Université Paris Nord - UP13 - Université Paris 13 - USPC - Université Sorbonne Paris Cité - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We study count processes in insurance, in which the underlying risk factor is time varying and unobservable. The factor follows an autoregressive gamma process, and the resulting model generalizes the static Poisson-Gamma model and allows for closed form expression for the posterior Bayes (linear or nonlinear) premium. Moreover, the estimation and forecasting can be conducted within the same framework in a rather efficient way. An example of automobile insurance pricing illustrates the ability of the model to capture the duration dependent, nonlinear impact of past claims on future ones and the improvement of the Bayes pricing method compared to the linear credibility approach. Introduction We propose a time series model for count variables encountered in insurance, when the underlying risk factor is time varying and unobservable. We introduce the au-toregressive gamma process for the latent factor dynamics and show how it provides an integrated framework for the efficient estimation, pricing, and forecasting of the risk. One typical application of the model is automobile insurance, for which the insurer holds the history of individual yearly claim counts over several periods. This information should then be taken into account in order to update the future premium regularly, on an individual basis. |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-02418950&r=all |
| By: | Giuliana, Raffaele (Central Bank of Ireland) |
| Abstract: | Several countries that have introduced macroprudential limits in the mortgage market apply differential limits to first time buyers relative to second and subsequent buyers. From a financial stability perspective, a key reason for such differentiation stems from a systematic observed difference in the probability of default across these different groups of borrowers. Kelly et al. (2015) already show that, in Ireland, FTBs were significantly less likely to default to the end of 2013. In order to further investigate whether FTBs are inherently less exposed to default, and to confirm that a key rationale of the calibration of the LTV restrictions under the Irish mortgage measures continues to hold, this paper provides two main contributions using Irish loan-level data. First, I show that the evidence of lower default probability among FTBs is consistent over time from 2013 to 2017. Second, in order to address a potential persistency bias, I implement a “default flow analysis” confirming that FTBs default less than SSBs. |
| Date: | 2019–11 |
| URL: | http://d.repec.org/n?u=RePEc:cbi:fsnote:14/fs/19&r=all |
| By: | Bräuning, Michael; Malikkidou, Despo; Scricco, Giorgio; Scalone, Stefano |
| Abstract: | This paper describes a machine learning technique to timely identify cases of individual bank financial distress. Our work represents the first attempt in the literature to develop an early warning system specifically for small European banks. We employ a machine learning technique, and build a decision tree model using a dataset of official supervisory reporting, complemented with qualitative banking sector and macroeconomic variables. We propose a new and wider definition of financial distress, in order to capture bank distress cases at an earlier stage with respect to the existing literature on bank failures; by doing so, given the rarity of bank defaults in Europe we significantly increase the number of events on which to estimate the model, thus increasing the model precision; in this way we identify bank crises at an earlier stage with respect to the usual default definition, therefore leaving a time window for supervisory intervention. The Quinlan C5.0 algorithm we use to estimate the model also allows us to adopt a conservative approach to misclassification: as we deal with bank distress cases, we consider missing a distress event twice as costly as raising a false flag. Our final model comprises 12 variables in 19 nodes, and outperforms a logit model estimation, which we use to benchmark our analysis; validation and back testing also suggest that the good performance of our model is relatively stable and robust. JEL Classification: E58, C01, C50 |
| Keywords: | bank distress, decision tree, machine learning, Quinlan |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20192348&r=all |
| By: | Saki Bigio; Adrien d'Avernas |
| Abstract: | Financial crises are particularly severe and lengthy when banks fail to recapitalize after bearing large losses. We present a model that explains the slow recovery of bank capital and economic activity. Banks provide intermediation in markets with information asymmetries. Large equity losses force banks to tighten intermediation, which exacerbates adverse selection. Adverse selection lowers bank profit margins which slows both the internal growth of equity and equity injections. This mechanism generates financial crises characterized by persistent low growth. The lack of equity injections during crises is a coordination failure that is solved when the decision to recapitalize banks is centralized. |
| JEL: | E32 E44 G01 G21 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26561&r=all |
| By: | Luigi Guiso (EIEF); Luigi Pistaferri (Stanford University) |
| Abstract: | We review the recent literature on the risk sharing role of the firm. We provide a framework for studying risk sharing between workers and firm owners vis-à-vis firms specific shocks of different nature.We show how this framework can be taken to the data to provide estimates of the extent of insurance within the firm. Estimates from a large number of Western countries strongly support the view that in capitalist economies the firm is a large albeit far from complete wage insurance instrument. We quantify the welfare benefits of firm-provided wage insurance, show evidence on how workers react to firms shockspassed through wages, and discuss the future role of the firm as a wage insurance provider. |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:eie:wpaper:2001&r=all |