Risk Management
http://lists.repec.org/mailman/listinfo/nep-rmg
Risk Management
2022-11-21
Cyber-Risk Forecasting using Machine Learning Models and Generalized Extreme Value Distributions
http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03814979&r=rmg
In this paper, we estimate the cost of a data breach using the number of compromised records. The number of such records is predicted by means of a machine learning model, particularly the Random Forest. We further analyse the fat tail phenomena which capture the underlying dynamics in the number of affected records. The objective is to calculate the maximum loss in order to answer the question of the insurability of cyber risk. Our results show that the total number of affected records follow a Frechet distribution, and we then estimate the Generalized Extreme Value (GEV) parameters to calculate the value at risk (VaR). This analysis is critical because it gives an idea of the maximum loss that can be generated by an enterprise data breach. These results are usable in anticipating the premiums for cyber risk coverage in the insurance markets.
Jules Sadefo Kamdem
Danielle Selambi
Cyber insurance,Cyber risk,Machine Learning,Regression Trees,Random Forest,Generalized Extreme Value
2022-10-13
Back-testing, risk estimation models: a simulation study for two-asset portfolios
http://d.repec.org/n?u=RePEc:pec:wpaper:2021_2&r=rmg
The aim of the study is to check the validity of five different risk-estimation models for two-asset portfolios, a topic which is relevant in model selection both for determining the minimum capital requirements for trading book portfolios and for the regulatory monitoring of the performance of internal risk models. Simulations based on a real data set containing the FTSE 100 constituents were carried out, and the risk was gauged by Expected Shortfall, a measure which also captures tail risk. Given that the period studied includes that of the subprime crisis, there is an inherent opportunity to compare and contrast the results produced under disaster conditions with others from less stressful periods. Our empirical analysis has confirmed that using Expected Shortfall instead of Value-at-Risk alone is not enough, and that the risk model has to be carefully selected and back-tested. The general Pareto distribution proved to be a prudent choice for risk models. In fact, among the five models considered, the model when general Pareto marginals were coupled with Clayton copula showed the best performance. It was, however, also revealed that this model is susceptible to being “over-cautious” in estimating loss.
Gyöngyi Bugár
Máté Uzsoki
Risk estimation models, Portfolio, Back-testing, Expected Shortfall, Copula
2021-08
Things That Have Never Happened Before Happen All the Time
http://d.repec.org/n?u=RePEc:fip:fednsp:94974&r=rmg
Remarks at the Central Bank of Nigeria’s Second National Risk Management Conference (delivered via videoconference).
Joshua V. Rosenberg
risk management
2022-10-27
W-shaped implied volatility curves in a variance-gamma mixture model
http://d.repec.org/n?u=RePEc:arx:papers:2209.14726&r=rmg
In liquid option markets, W-shaped implied volatility curves have occasionally be observed. We show that such shapes can be reproduced in a mixture of two variance-gamma models. This is in contrast to lognormal models, where at least three different distributions have to be mixed in order to produce a W-shape, as recently shown by Glasserman and Pirjol.
Martin Keller-Ressel
2022-09
Risk Management in Border Inspection
http://d.repec.org/n?u=RePEc:wbk:wbrwps:9438&r=rmg
As part of their commitments under the World Trade Organization's Agreement on Trade Facilitation, many developing countries are set to adopt risk management, a strategy for selecting import shipments for inspection. This paper formalizes key enforcement issues related to risk management. It argues that the complexities of international trade oversight mean that inspecting agencies lack certainty about the conditional probability that a given shipment will not comply with import regulations. Ambiguity of this sort is likely to be especially important in developing countries that lack the sophisticated information technology used in advanced risk management systems. This paper formalizes a role for ambiguity in a theoretical model of border inspection. It provides evidence suggesting that ambiguity affects inspection rates. Finally, the paper calibrates the model and shock the ambiguity parameters to illustrate the consequences of an information technology-driven improvement in risk management capabilities for equilibrium rates of search and compliance.
Hillberry,Russell Henry
Karabay,Bilgehan
Tan,Shawn Weiming
International Trade and Trade Rules,Information Technology,Trade Facilitation,Financial Sector Policy,Human Rights
2020-10-14
Basel III and SME bank finance in Germany
http://d.repec.org/n?u=RePEc:zbw:bubdps:372022&r=rmg
This paper examines how Basel III capital reforms affected bank lending in Ger- many. We focus on the increase of minimum risk-based capital requirements and the introduction of the leverage ratio. The announcement of stricter risk-based capital regulation significantly affected low capitalized banks. The impact depends on a bank's credit risk model, i.e. whether a bank applies the standardized approach (SA) or an internal ratings-based approach (IRBA) to determine risk weights. Low capitalized SA banks significantly cut lending whereas IRBA banks did not ad- just lending volumes. By contrast, low capitalized IRBA banks significantly in- creased collateralization while low capitalized SA banks adjusted collateralization only marginally. Moreover, the impact on SMEs and large companies also differs. In terms of lending, SMEs were affected more strongly, whilst in terms of collateralization the impact on large companies was bigger. The announcement of the leverage ratio had, however, a rather limited impact. We find some evidence that low capitalized banks reduced lending. Furthermore, low capitalized banks somewhat tightened collateral requirements, especially for large companies.
Marek, Philipp
Stein, Ingrid
Basel III,bank lending,nancial regulation,small and medium-sizedenterprises (SMEs)
2022
The rough Hawkes Heston stochastic volatility model
http://d.repec.org/n?u=RePEc:arx:papers:2210.12393&r=rmg
We study an extension of the Heston stochastic volatility model that incorporates rough volatility and jump clustering phenomena. In our model, named the rough Hawkes Heston stochastic volatility model, the spot variance is a rough Hawkes-type process proportional to the intensity process of the jump component appearing in the dynamics of the spot variance itself and the log returns. The model belongs to the class of affine Volterra models. In particular, the Fourier-Laplace transform of the log returns and the square of the volatility index can be computed explicitly in terms of solutions of deterministic Riccati-Volterra equations, which can be efficiently approximated using a multi-factor approximation technique. We calibrate a parsimonious specification of our model characterized by a power kernel and an exponential law for the jumps. We show that our parsimonious setup is able to simultaneously capture, with a high precision, the behavior of the implied volatility smile for both S&P 500 and VIX options. In particular, we observe that in our setting the usual shift in the implied volatility of VIX options is explained by a very low value of the power in the kernel. Our findings demonstrate the relevance, under an affine framework, of rough volatility and self-exciting jumps in order to capture the joint evolution of the S&P 500 and VIX.
Alessandro Bondi
Sergio Pulido
Simone Scotti
2022-10
The Most Expected Things Often Come as a Surprise: Analysis of the Impact of Monetary Surprises on the Bank's Risk and Activity
http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03807034&r=rmg
In this paper, we analyse the link between monetary and banks' activity and risk-taking. Some theoretical and empirical studies show that monetary easing increases banks' appetite for risk, affect credit allocation and bank's profitability. Our study adds to analyses of the monetary risk-taking channel considering monetary surprise, i.e. the impact of unexpected changes in monetary policy on bank's risk and activity. Using a dataset of US banks, we find that higher increase or lower decrease of interest rates than expected (negative surprise) leads banks to take more risk, to grant more corporate loans than consumption loans, and to be more profitable. We complement the literature on the risk-taking channel and provide arguments that Central Banks can manage financial stability.
Melchisedek Joslem Ngambou Djatche
2021
Solomon Islands: Technical Assistance Report-Central Bank Risk Management
http://d.repec.org/n?u=RePEc:imf:imfscr:2022/327&r=rmg
At the request of the Central Bank of Solomon Islands (CBSI), a Monetary and Capital Markets Department (MCM) mission provided technical assistance on central bank risk management during the period August–September 2021. The mission comprised Mr. Paul Woods (Central Bank of Ireland) and Mr. Chris Aylmer (formerly with the Reserve Bank of Australia), under supervision of Mr. Ashraf Khan (MCM, Central Bank Operations Division) The purpose of the mission was to guide the CBSI on how to establish an Enterprise Risk Management (ERM) framework. The mission focused in particular on establishing a strengthened risk culture throughout the organization, and strengthening risk governance - including the role of the CBSI’s risk management unit.
International Monetary Fund
2022-10-21
EXPLORING THE IMPACT OF LOAN RESTRUCTURING IN INDONESIAN BANKING
http://d.repec.org/n?u=RePEc:idn:wpaper:wp062021&r=rmg
This paper investigates the impact of loan restructuring on risk and performance in Indonesian banking. We find that higher restructured loans increase non-performing loans. Concomittantly, higher restructured loans are associated with higher capital ratio and lower insolvency risk. In this regard, higher capital ratio is sufficient to offset an increase in credit risk, which in turn enhances bank solvency. A deeper analysis suggests that such findings are driven by banks with lower capitalization and private-owned banks. For banks with higher capitalization and government-owned banks, higher restructured loans may deteriorate bank solvency. Moreover, the role of loan restructuring in strengthening financial stability is more pronounced during economic downturns in general. Although loan restructuring matters for financial stability regardless of the degree of economic growth, the effectiveness of loan restructuring policy is conditional.
Wahyoe Soedarmono
Iman Gunadi
Fiskara Indawan
Carla Sheila Wulandari
Bank loan restructuring, risk, capital ratio, performance, Indonesia
2021
Identifying Structural Shocks to Volatility through a Proxy-MGARCH Model
http://d.repec.org/n?u=RePEc:zbw:vfsc22:264010&r=rmg
Fengler, Matthias
Polivka, Jeanine
2022
Ambiguity, value of information and forest rotation decision under storm risk
http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03796414&r=rmg
Storm is a major risk in forestry. However, due to the more or less pessimistic scenarios of future climate change, storm frequency is now ambiguous and only partially known (i.e., scenario ambiguity). Furthermore, within each scenario, the quantification of storm frequency is also ambiguous due to the differences in risk quantification by experts, creating a second level of ambiguity (i.e., frequency ambiguity). In such an ambiguous context, knowledge of the future climate through accurate information about this risk is fundamental and can be of significant value. In this paper, we question how ambiguity and ambiguity aversion affect forest management, in particular, optimal cutting age. Using a classical Faustmann framework of forest rotation decisions, we compare three different situations: risk, scenario ambiguity and frequency ambiguity. We show that risk and risk aversion significantly reduce the optimal cutting age. We also show that both scenario and frequency ambiguities reinforce the effect of risk. Inversely, ambiguity aversion has no effect. The value of information that resolves scenario ambiguity is high, whereas it is null for frequency ambiguity.
Patrice Loisel
Marielle Brunette
Stéphane Couture
Rotation decision,Risk,Ambiguity,Ambiguity Aversion,Risk Aversion,Value of Information,Forests,Faustmann criterion
2022-10-04
Can Time-Varying Currency Risk Hedging Explain Exchange Rates?
http://d.repec.org/n?u=RePEc:chf:rpseri:rp2277&r=rmg
Over the last decade foreign bond portfolio positions in US dollar assets have risen above the reciprocal US investor positions in foreign currencies. In periods of increased economic uncertainty, institutional investors hedge their international bond positions, which creates a net hedging demand for dollar assets that depreciates USD rates in both the forward and spot markets. We document the time-varying nature of this net hedging demand and show how it relates to economic uncertainty and the US net foreign bond position in various currencies. Based on a parsimonious VAR model, we find that changes in FX hedging pressure can account for approximately 30% of all monthly variation in the seven most important dollar exchange rates from 2012 to 2022.
Leonie Bräuer
Harald Hau
Exchange Rate, Hedging Channel, Institutional Investors
2022-10
Considerations for the allocation of non-default losses by financial market infrastructures
http://d.repec.org/n?u=RePEc:bca:bocsan:22-16&r=rmg
Non-default losses of financial market infrastructures (FMIs) have gained attention due to their potential impacts on FMIs and FMI participants, and the lack of a common approach to address them. A key question is, who should absorb these losses?
Daniele Costanzo
Radoslav Raykov
Financial markets; Financial system regulation and policies
2022-11
Shannon entropy: an econophysical approach to cryptocurrency portfolios
http://d.repec.org/n?u=RePEc:arx:papers:2210.02633&r=rmg
Cryptocurrency markets have attracted many interest for global investors because of their novelty, wide online availability, increasing capitalization and potential profits. In the econophysics tradition we show that many of the most available cryptocurrencies have return statistics that do not follow Gaussian distributions but heavy--tailed distributions instead. Entropy measures are also applied showing that portfolio diversification is a reasonable practice for decreasing return uncertainty.
Noe Rodriguez-Rodriguez
Octavio Miramontes
2022-10
Эконометрический анализ факторов банкротств российских компаний в обрабатывающем секторе
http://d.repec.org/n?u=RePEc:pra:mprapa:114969&r=rmg
This work is devoted to the analysis of the factors influencing the bankruptcy of the Russian manufacturing industry companies for the period from 2012 to 2020. Logistic regression was used as an econometric tool for the modelling the probability of companies’ default. According to the results, financial indicators of profitability, liquidity and business activity play a significant role in explaining the probability of default of Russian manufacturing companies. Special attention was paid to the impact on the probability of bankruptcy of corporate governance and ownership structure factors. First, including these indicators into the model led to an increase in its predictive power. Secondly, CEO-duality increases the stability of the company, and too high maximum share of ownership increases the likelihood of bankruptcy.
Bekirova, Olga
Zubarev, Andrey
probability of default, logistic regression, corporate governance.
2022-06
A la Recherche du Temps Perdu : Legal and Quantitative analysis of the First Documented Option Market - Paris 1844-1939
http://d.repec.org/n?u=RePEc:hal:cesptp:halshs-03815575&r=rmg
We provide the first ever quantitative analysis of pricing and profitability of option trading in Paris from 1843 to 1939 based on a data source featuring more than 75,000 option prices. Using a special case of the Black (1976) option pricing model, we show that, albeit options were consistently undervalued, the market was still profitable for all the parties. We prove that the exceptional longevity of the Paris options market was based on a 4-pillars market microstructure: (1.) systematic underpricing of cheap options to attract gamblers, (2.) administration of settlement price by the brokers' syndicate, (3.) parimutuel-like betting operation and safety thanks to (4.) a sophisticated risk management in the position-taking style which minimized actual clearing price manipulation.
Antoine Parent
Pierre-Charles Pradier
Option pricing,financial risk management,betting markets,alternative investments
2022-10
Factor Investing with a Deep Multi-Factor Model
http://d.repec.org/n?u=RePEc:arx:papers:2210.12462&r=rmg
Modeling and characterizing multiple factors is perhaps the most important step in achieving excess returns over market benchmarks. Both academia and industry are striving to find new factors that have good explanatory power for future stock returns and good stability of their predictive power. In practice, factor investing is still largely based on linear multi-factor models, although many deep learning methods show promising results compared to traditional methods in stock trend prediction and portfolio risk management. However, the existing non-linear methods have two drawbacks: 1) there is a lack of interpretation of the newly discovered factors, 2) the financial insights behind the mining process are unclear, making practitioners reluctant to apply the existing methods to factor investing. To address these two shortcomings, we develop a novel deep multi-factor model that adopts industry neutralization and market neutralization modules with clear financial insights, which help us easily build a dynamic and multi-relational stock graph in a hierarchical structure to learn the graph representation of stock relationships at different levels, e.g., industry level and universal level. Subsequently, graph attention modules are adopted to estimate a series of deep factors that maximize the cumulative factor returns. And a factor-attention module is developed to approximately compose the estimated deep factors from the input factors, as a way to interpret the deep factors explicitly. Extensive experiments on real-world stock market data demonstrate the effectiveness of our deep multi-factor model in the task of factor investing.
Zikai Wei
Bo Dai
Dahua Lin
2022-10
Have the risk policy shifts related to Seveso Upper Tier establishments in France led to an improvement in risk prevention? A focus on three risk prevention tools
http://d.repec.org/n?u=RePEc:hal:journl:hal-03812138&r=rmg
Scarlett Tannous
Myriam Merad
2022-10-10
Forecasting Oil Prices: Can Large BVARs Help?
http://d.repec.org/n?u=RePEc:een:camaaa:2022-65&r=rmg
Large Bayesian Vector Autoregressions (BVARs) have been a successful tool in the forecasting literature and most of this work has focused on macroeconomic variables. In this paper, we examine the ability of large BVARs to forecast the real price of crude oil using a large dataset with over 100 variables. We find consistent results that the large BVARs do not beat the BVARs with small and medium sizes for short forecast horizons but offer better forecasts at long horizons. In line with the forecasting macroeconomic literature, we also find that the forecast ability of the large models further improves upon the competing standard BVARs once endowed with flexible error structures.
Bao H. Nguyen
Bo Zhang
forecasting, non-Gaussian, stochastic volatility, oil prices, big data
2022-10
What drives most jumps in global crude oil prices? Fundamental shortage conditions, Cartel, geopolitics or the behavior of market financial participants
http://d.repec.org/n?u=RePEc:hal:journl:hal-03793866&r=rmg
Several studies have emphasized the potential role of oil price volatility as a leading macroeconomic indicator, since it provides prominent information to energy traders, market participants and policymakers. In an effort to shed fresh insights on the underlying factors of wide oil price changes, the objective of this paper is twofold. First to capture large oil price changes caused by the arrival of surprising news (i.e., jumps); second to distinguish between short-, medium-and long-term determinants of jumps in oil prices due to changes in oil supply and demand fundamentals, factors associated with the market power of oil producers, speculation, geopolitical risks and OPEC's spare capacity. Using an Empirical Mode Decomposition (EMD), we find that oil supply and demand forces are the most prevalent in determining oil price changes in the long run, which is quite predictable. OPEC gains increasing importance in the medium-and long-terms. Our method also allows us to show that OPEC's use of spare capacity moderately reduces oil price volatility in the short-term, thus suggesting a limited stabilizing influence on the oil market. We consider broader policy implications for our results.
Refk Selmi
Shawkat Hammoudeh
Mark Wohar
Oil price jumps,oil price determinants,Empirical Mode Decomposition,Empirical Mode Decomposition JEL classification: G15,C11,C58,Q30,Q31
2022-08-01
Uncertainty, Skewness, and the Business Cycle Through the MIDAS Lens
http://d.repec.org/n?u=RePEc:pad:wpaper:0291&r=rmg
We employ a mixed-frequency quantile regression approach to model the time-varying conditional distribution of the US real GDP growth rate. We show that monthly information on the US financial cycle improves the predictive power of an otherwise quarterly-only model. We combine selected quantiles of the estimated conditional distribution to produce measures of uncertainty and skewness. Embedding these measures in a VAR framework, we show that unexpected changes in uncertainty are associated with an increase in (left) skewness and a downturn in real activity. Empirical findings related to VAR impulse responses and forecast error variance decomposition are shown to depend on the inclusion/omission of monthly-level information on financial conditions when estimating real GDP growthâ€™s conditional density. Effects are significantly downplayed if we consider a quarterly-only quantile regression model. A counterfactual simulation conducted by shutting down the endogenous response of skewness to uncertainty shocks shows that skewness substantially amplifies the recessionary effects of uncertainty.
Efrem Castelnuovo
Lorenzo Mori
Uncertainty, skewness, quantile regressions, vector autoregressions, MIDAS
2022-10
Social Distancing and Risk Taking: Evidence from a Team Game Show *
http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03792423&r=rmg
We examine the risky choices of pairs of contestants in a popular radio game show in France. At the onset of the COVID-19 pandemic, the show, held in person, had to switch to an all-remote format. We find that such an exogenous change in social context affected risk-taking behavior. Remotely, pairs take far fewer risks when the stakes are high than in the flesh. This behavioral difference is consistent with prosocial behavior theories, which argue that the nature of social interactions influences risky choices. Our results suggest that working from home may reduce participation in profitable but risky team projects.
Jean-Marc Bourgeon
José de Sousa
Alexis Noir-Luhalwe
COVID-19,Social Distancing,Social Pressure,Decision Making,Risk
2022-09-30
Microfounding GARCH Models and Beyond: A Kyle-inspired Model with Adaptive Agents
http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03797251&r=rmg
We relax the strong rationality assumption for the agents in the paradigmatic Kyle model of price formation, thereby reconciling the framework of asymmetrically informed traders with the Adaptive Market Hypothesis, where agents use inductive rather than deductive reasoning. Building on these ideas, we propose a stylised model able to account parsimoniously for a rich phenomenology, ranging from excess volatility to volatility clustering. While characterising the excess-volatility dynamics, we provide a microfoundation for GARCH models. Volatility clustering is shown to be related to the self-excited dynamics induced by traders' behaviour, and does not rely on clustered fundamental innovations. Finally, we propose an extension to account for the fragile dynamics exhibited by real markets during flash crashes.
Michele Vodret
Iacopo Mastromatteo
Bence Tóth
Michael Benzaquen
adaptive agents,volatility clustering,excess volatility,price impact
2022-10-04
American options in the Volterra Heston model
http://d.repec.org/n?u=RePEc:hal:journl:hal-03178306&r=rmg
We price American options using kernel-based approximations of the Volterra Heston model. We choose these approximations because they allow simulation-based techniques for pricing. We prove the convergence of American option prices in the approximating sequence of models towards the prices in the Volterra Heston model. A crucial step in the proof is to exploit the affine structure of the model in order to establish explicit formulas and convergence results for the conditional Fourier--Laplace transform of the log price and an adjusted version of the forward variance. We illustrate with numerical examples our convergence result and the behavior of American option prices with respect to certain parameters of the model.
Etienne Chevalier
Sergio Pulido
Elizabeth Zúñiga
2022-04-27
Fractal landscape dynamics in dense emulsions and stock prices
http://d.repec.org/n?u=RePEc:arx:papers:2210.13667&r=rmg
Many soft and biological materials display so-called 'soft glassy' dynamics; their constituents undergo anomalous random motion and intermittent cooperative rearrangements. Stock prices show qualitatively similar dynamics, whose origins also remain poorly understood. Recent simulations of a foam have revealed that such motion is due to the system evolving in a high-dimensional configuration space via energy minimization on a slowly changing, fractal energy landscape. Here we show that the salient geometrical features of such energy landscapes can be explored and quantified not only in simulation but empirically using real-world, high-dimensional data. In a mayonnaise-like dense emulsion, the experimentally observed motion of oil droplets shows that the fractal geometry of the configuration space paths and energy landscape gives rise to the anomalous random motion and cooperative rearrangements, confirming corresponding simulations in detail. Our empirical approach allows the same analyses to be applied to the component stock prices of the Standard and Poor's 500 Index. This analysis yields remarkably similar results, revealing that stock return dynamics also appear due to prices moving on a similar, slowly evolving, high-dimensional fractal landscape.
Clary Rodriguez-Cruz
Mehdi Molaei
Amruthesh Thirumalaiswamy
Klebert Feitosa
Vinothan N. Manoharan
Shankar Sivarajan
Daniel H. Reich
Robert A. Riggleman
John C. Crocker
2022-10