Risk Management
http://lists.repec.org/mailman/listinfo/nep-rmg
Risk Management
2019-05-06
Tail models and the statistical limit of accuracy in risk assessment
http://d.repec.org/n?u=RePEc:arx:papers:1904.12113&r=rmg
In risk management, tail risks are of crucial importance. The assessment of risks should be carried out in accordance with the regulatory authority's requirement at high quantiles. In general, the underlying distribution function is unknown, the database is sparse, and therefore special tail models are used. Very often, the generalized Pareto distribution is employed as a basic model, and its parameters are determined with data from the tail area. With the determined tail model, statisticians then calculate the required high quantiles. In this context, we consider the possible accuracy of the calculation of the quantiles and determine the finite sample distribution function of the quantile estimator, depending on the confidence level and the parameters of the tail model, and then calculate the finite sample bias and the finite sample variance of the quantile estimator. Finally, we present an impact analysis on the quantiles of an unknown distribution function.
Ingo Hoffmann
Christoph J. B\"orner
2019-04
Risk measures and progressive enlargement of filtration: a BSDE approach
http://d.repec.org/n?u=RePEc:arx:papers:1904.13257&r=rmg
We consider dynamic risk measures induced by Backward Stochastic Differential Equations (BSDE) in enlargement of filtration setting. On a fixed probability space, we are given a standard Brownian motion and a pair of random variables $(\tau, \zeta) \in (0,+\infty) \times E$, with $E \subset \mathbb{R}^m$, that enlarge the reference filtration, i.e., the one generated by the Brownian motion. These random variables can be interpreted financially as a default time and an associated mark. After introducing a BSDE driven by the Brownian motion and the random measure associated to $(\tau, \zeta)$, we define the dynamic risk measure $(\rho_t)_{t \in [0,T]}$, for a fixed time $T > 0$, induced by its solution. We prove that $(\rho_t)_{t \in [0,T]}$ can be decomposed in a pair of risk measures, acting before and after $\tau$ and we characterize its properties giving suitable assumptions on the driver of the BSDE. Furthermore, we prove an inequality satisfied by the penalty term associated to the robust representation of $(\rho_t)_{t \in [0,T]}$ and we provide an explicit example of such kind of dynamic risk measures, along with its decomposition.
Alessandro Calvia
Emanuela Rosazza Gianin
2019-04
Mortgage Risk Since 1990
http://d.repec.org/n?u=RePEc:hfa:wpaper:19-02&r=rmg
This paper provides a comprehensive account of the evolution of default risk for newly originated home purchase loans since 1990. We bring together several data sources to produce this history, including loan-level data for the entire Enterprise (Fannie Mae and Freddie Mac) book. We use these data to track a large number of loan characteristics and a summary measure of risk, the stressed default rate. Among the many results in the paper, we show that mortgage risk had already risen in the 1990s, planting the seeds of the financial crisis well before the actual event. Our results also cast doubt on explanations of the crisis that focus on low-credit-score borrowers.
Morris A. Davis
William D. Larson
Stephen D. Oliner
Benjamin Smith
mortgage risk, housing boom, default, foreclosure, house price, leverage
2019-02
Taking regulation seriously: fire sales under solvency and liquidity constraints
http://d.repec.org/n?u=RePEc:boe:boeewp:0793&r=rmg
We build a framework for modelling fire sales where banks face both liquidity and solvency constraints and choose which assets to sell in order to minimise liquidation losses. Banks constrained by the leverage ratio prefer to first sell assets that are liquid and held in small amounts, while banks constrained by the risk-weighted capital ratio and the liquidity coverage ratio need to trade off assets’ liquidity with their regulatory weights. We calibrate the model to the UK banking system, and find that banks’ optimal liquidation strategies translate into moderate fire-sale losses even for extremely large solvency shocks. By contrast, severe funding shocks can generate significant losses. Thus models focusing exclusively on solvency risk may significantly underestimate the extent of contagion via fire sales. Moreover, when studying combined funding and solvency shocks, we find complementarities between the two shocks’ effects that cannot be reproduced by focusing on either shock in isolation.
Coen, Jamie
Lepore, Caterina
Schaanning, Eric
Banks; financial regulation; fire sales; stress testing; systemic risk
2019-04-26
Cash sub-additive risk statistics with scenario analysis
http://d.repec.org/n?u=RePEc:arx:papers:1905.00486&r=rmg
Since the money is of time value, we will study a new class of risk statistics, named cash sub-additive risk statistics in this paper. This new class of risk statistics can be considered as a kind of risk extension of risk statistics introduced by Kou, Peng and Heyde (2013), and also data-based versions of cash sub-additive risk measures introduced by El Karoui and Ravanelli (2009) and Sun and Hu (2019).
Fei Sun
2019-04
Asset Price Bubbles and Systemic Risk
http://d.repec.org/n?u=RePEc:nbr:nberwo:25775&r=rmg
We analyze the relationship between asset price bubbles and systemic risk, using bank-level data covering almost thirty years. Systemic risk of banks rises already during a bubble’s build-up phase, and even more so during its bust. The increase differs strongly across banks and bubble episodes. It depends on bank characteristics (especially bank size) and bubble characteristics, and it can become very large: In a median real estate bust, systemic risk increases by almost 70 percent of the median for banks with unfavorable characteristics. These results emphasize the importance of bank-level factors for the build-up of financial fragility during bubble episodes.
Markus K. Brunnermeier
Simon C. Rother
Isabel Schnabel
2019-04
The Insurance is the Lemon: Failing to Index Contracts
http://d.repec.org/n?u=RePEc:ecl:stabus:3569&r=rmg
We model the widespread failure of contracts to share risk using available indices. A borrower and lender can share risk by conditioning repayments on an index. The lender has private information about the ability of this index to measure the true state the borrower would like to hedge. The lender is risk averse, and thus requires a premium to insure the borrower. The borrower, however, might be paying something for nothing, if the index is a poor measure of the true state. We provide sufficient conditions for this effect to cause the borrower to choose a non-indexed contract instead.
Hartman-Glaser, Barney
Hebert, Benjamin
2019-01
Pricing and hedging of VIX options for Barndorff-Nielsen and Shephard models
http://d.repec.org/n?u=RePEc:arx:papers:1904.12260&r=rmg
The VIX call options for the Barndorff-Nielsen and Shephard models will be discussed. Derivatives written on the VIX, which is the most popular volatility measurement, have been traded actively very much. In this paper, we give representations of the VIX call option price for the Barndorff-Nielsen and Shephard models: non-Gaussian Ornstein--Uhlenbeck type stochastic volatility models. Moreover, we provide representations of the locally risk-minimizing strategy constructed by a combination of the underlying riskless and risky assets. Remark that the representations obtained in this paper are efficient to develop a numerical method using the fast Fourier transform. Thus, numerical experiments will be implemented in the last section of this paper.
Takuji Arai
2019-04
Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility
http://d.repec.org/n?u=RePEc:pra:mprapa:93544&r=rmg
This work proposes to forecast the Realized Volatility (RV) and the Value-at-Risk (VaR) of the most liquid Russian stocks using GARCH, ARFIMA and HAR models, including both the implied volatility computed from options prices and Google Trends data. The in-sample analysis showed that only the implied volatility had a significant effect on the realized volatility across most stocks and estimated models, whereas Google Trends did not have any significant effect. The out-of-sample analysis highlighted that models including the implied volatility improved their forecasting performances, whereas models including internet search activity worsened their performances in several cases. Moreover, simple HAR and ARFIMA models without additional regressors often reported the best forecasts for the daily realized volatility and for the daily Value-at-Risk at the 1% probability level, thus showing that efficiency gains more than compensate any possible model misspecifications and parameters biases. Our empirical evidence shows that, in the case of Russian stocks, Google Trends does not capture any additional information already included in the implied volatility.
Bazhenov, Timofey
Fantazzini, Dean
Forecasting; Realized Volatility; Value-at-Risk; Implied Volatility; Google Trends; GARCH; ARFIMA; HAR;
2019-04
Financial cycles as early warning indicators - Lessons from the Nordic region
http://d.repec.org/n?u=RePEc:ice:wpaper:wp80&r=rmg
Frameworks to handle cyclical systemic risk usually contain a wide selection of early warning indicators. Different indicators sometimes send diverging signals which can be hard to interpret. However, measures of aggregate financial cycles can serve as a way to synthesize information from many indicators. There are however many ways to construct a measure of such cycles. Many methods exist for cycle extraction, variable choice represents another dimension, and cycle aggregation the third. We tackle each step of the way by selecting the best out of six cycle extraction methods, then comparing variables from three groups: credit, house prices and bank funding, and lastly arguing for a simple method of cycle aggregation based on cycle correlation and frequency domain analysis. We then construct a trivariate financial cycle measure which outperforms the ’Basel gap’, all univariate cycles and all other multivariate combinations for the Nordic countries in terms of a noise-to-signal ratio. In addition, it peaks much closer to crisis onset and does relatively well at real-time turning point identification. The trivariate band-pass filtered measure contains the best variable from each group, and outperforms them all. This indicates that aggregate cycles can be more than the sum of their parts, as early warning indicators. Furthermore, we examine potential weaknesses of our analysis in terms of small-sample problems, spurious cycles and the timing of crisis onset. We conclude with 15 lessons from the Nordic countries.
Önundur Páll Ragnarsson
Jón Magnús Hannesson
Loftur Hreinsson
2019-03
CONSUMER LENDING EFFICIENCY:COMMERCIAL BANKS VERSUS A FINTECH LENDER
http://d.repec.org/n?u=RePEc:fip:fedpwp:19-22&r=rmg
We compare the performance of unsecured personal installment loans made by traditional bank lenders with that of LendingClub, using a stochastic frontier estimation technique to decompose the observed nonperforming loans into three components. The first is the best-practice minimum ratio that a lender could achieve if it were fully efficient at credit-risk evaluation and loan management. The second is a ratio that reflects the difference between the observed ratio (adjusted for noise) and the minimum ratio that gauges the lender’s relative proficiency at credit analysis and loan monitoring. The third is statistical noise. In 2013 and 2016, the largest bank lenders experienced the highest ratio of nonperformance, the highest inherent credit risk, and the highest lending efficiency, indicating that their high ratio of nonperformance is driven by inherent credit risk, rather than by lending inefficiency. LendingClub’s performance was similar to small bank lenders as of 2013. As of 2016, LendingClub’s performance resembled the largest bank lenders — the highest ratio of nonperforming loans, inherent credit risk, and lending efficiency — although its loan volume was smaller. Our findings are consistent with a previous study that suggests LendingClub became more effective in risk identification and pricing starting in 2015. Caveat: We note that this conclusion may not be applicable to fintech lenders in general, and the results may not hold under different economic conditions such as a downturn
Hughes, Joseph P.
Jagtiani, Julapa
Moon, Choon-Geol
marketplace lending; P2P lending; credit risk management; lending efficiency
2019-04-02
Has regulatory capital made banks safer? Skin in the game vs moral hazard
http://d.repec.org/n?u=RePEc:srk:srkwps:201991&r=rmg
The paper evaluates the impact of macroprudential capital regulation on bank capital, risk taking behaviour, and solvency. The identification relies on the policy change in bank-level capital requirements across systemically important banks in Europe. A one percentage point hike in capital requirements leads to an average CET1 capital increase of 13 percent and no evidence of reduction in assets. The increase in capital comes at a cost. The paper documents robust evidence on the existence of substitution effects toward riskier assets. The risk taking behavior is predominantly driven by large and less profitable banks: large wholesale funded banks show less risk taking, and large banks relying on internal ratings based approach successfully disguise their risk taking. In terms of overall impact on solvency, the higher risk taking crowds out the positive effect of increased capital. JEL Classification: E51, G21, O52
Dautovic, Ernest
capital requirements, macroprudential policy, moral hazard, risk-taking
2019-05
A Practical Guide to Harnessing the HAR Volatility Model
http://d.repec.org/n?u=RePEc:qut:auncer:2019_01&r=rmg
The standard heterogeneous autoregressive (HAR) model is perhaps the most popular benchmark model for forecasting return volatility. It is often estimated using raw realized variance (RV) and ordinary least squares (OLS). However, given the stylized facts of RV and wellknown properties of OLS, this combination should be far from ideal. One goal of this paper is to investigate how the predictive accuracy of the HAR model depends on the choice of estimator, transformation, and forecasting scheme made by the market practitioner. Another goal is to examine the effect of replacing its high-frequency data based volatility proxy (RV) with a proxy based on free and publicly available low-frequency data (logarithmic range). In an out-of-sample study, covering three major stock market indices over 16 years, it is found that simple remedies systematically outperform not only standard HAR but also state of the art HARQ forecasts, and that HAR models using logarithmic range can often produce forecasts of similar quality to those based on RV.
A Clements
D Preve
Volatility forecasting; Realized variance; HAR model; HARQ model; Robust regression; Box-Cox transformation; Forecast comparisons; QLIKE loss; Model confidence set
2019-04-12
Cyclical income risk in Great Britain
http://d.repec.org/n?u=RePEc:ces:ceswps:_7594&r=rmg
This paper establishes new evidence on the cyclical behaviour of household income risk in Great Britain and assesses the role of social insurance policy in mitigating against this risk. We address these issues using the British Household Panel Survey (1991-2008) by decomposing stochastic idiosyncratic income into its transitory, persistent and fixed components. We then estimate how income risk, measured by the variance and the skewness of the probability distribution of shocks to the persistent component, varies between expansions and contractions of the aggregate economy. We first find that the volatility and left-skewness of these shocks is a-cyclical and counter-cyclical respectively. The latter implies a higher probability of receiving large negative income shocks in contractions. We also find that while social insurance (tax-benefits) policy reduces the levels of both measures of risk as well as the counter-cyclicality of the asymmetry measure, the mitigation effects work mainly via benefits.
Konstantinos Angelopoulos
Spyridon Lazarakis
Jim Malley
household income risk, social insurance policy, aggregate fluctuations
2019
Nonparametric pricing and hedging of exotic derivatives
http://d.repec.org/n?u=RePEc:arx:papers:1905.00711&r=rmg
In the spirit of Arrow-Debreu, we introduce a family of financial derivatives that act as primitive securities in that exotic derivatives can be approximated by their linear combinations. We call these financial derivatives signature payoffs. We show that signature payoffs can be used to nonparametrically price and hedge exotic derivatives in the scenario where one has access to price data for other exotic payoffs. The methodology leads to a computationally tractable and accurate algorithm for pricing and hedging using market prices of a basket of exotic derivatives that has been tested on real and simulated market prices, obtaining good results.
Terry Lyons
Sina Nejad
Imanol Perez Arribas
2019-05