|
on Risk Management |
| By: | Suman Thapa; Yiqiang Q. Zhao |
| Abstract: | In this paper, we investigate risk measures such as value at risk (VaR) and the conditional tail expectation (CTE) of the extreme (maximum and minimum) and the aggregate (total) of two dependent risks. In finance, insurance and the other fields, when people invest their money in two or more dependent or independent markets, it is very important to know the extreme and total risk before the investment. To find these risk measures for dependent cases is quite challenging, which has not been reported in the literature to the best of our knowledge. We use the FGM copula for modelling the dependence as it is relatively simple for computational purposes and has empirical successes. The marginal of the risks are considered as exponential and pareto, separately, for the case of extreme risk and as exponential for the case of the total risk. The effect of the degree of dependency on the VaR and CTE of the extreme and total risks is analyzed. We also make comparisons for the dependent and independent risks. Moreover, we propose a new risk measure called median of tail (MoT) and investigate MoT for the extreme and aggregate dependent risks. |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2101.12402&r=all |
| By: | Petar Jevtic; Nicolas Lanchier |
| Abstract: | Smart contract risk can be defined as a financial risk of loss due to cyber attacks on or contagious failures of smart contracts. Its quantification is of paramount importance to technology platform providers as well as companies and individuals when considering the deployment of this new technology. That is why, as our primary contribution, we propose a structural framework of aggregate loss distribution for smart contract risk under the assumption of a tree-stars graph topology representing the network of interactions among smart contracts and their users. Up to our knowledge, there exist no theoretical frameworks or models of an aggregate loss distribution for smart contracts in this setting. To achieve our goal, we contextualize the problem in the probabilistic graph-theoretical framework using bond percolation models. We assume that the smart contract network topology is represented by a random tree graph of finite size, and that each smart contract is the center of a {random} star graph whose leaves represent the users of the smart contract. We allow for heterogeneous loss topology superimposed on this smart contract and user topology and provide analytical results and instructive numerical examples. |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2101.08964&r=all |
| By: | Daniel Felix Ahelegbey (University of Pavia); |
| Abstract: | We construct a new index of global equity market risk (EMR) using market interconnectedness and volatilities. We study the relationship between our EMR and the VIX over the last two decades. The EMR is shown to be a novel approach to measuring global market risk, and an alternative to the VIX. Using data of 20 major stock markets, including G10 economies, we find spikes in our EMR index during the dotcom bubble, the global financial crisis, the European sovereign debt crisis, and the novel coronavirus pandemic. The result shows that the global financial crisis and the Covid-19 induced crisis record the historic highest spikes in financial market risk, suggesting stronger evidence of contagion in both periods. |
| Keywords: | COVID-19, Financial Crises, Financial Markets, Market Risk, Mahalanobis Distance, Volatility Index. |
| JEL: | C11 C15 C51 C52 C55 C58 G01 G12 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:pav:demwpp:demwp0194&r=all |
| By: | Yener Altunbas; David Marques‐Ibanez; Michiel van Leuvensteijn; Tianshu Zhao |
| Abstract: | We examine how bank competition in the run-up to the 2007–2009 crisis affects banks’ systemic risk during the crisis. We then investigate whether this effect is influenced by two key bank characteristics: securitization and bank capital. Using a sample of the largest listed banks from 15 countries, we find that greater market power at the bank level and higher competition at the industry level lead to higher realized systemic risk. The results suggest that the use of securitization exacerbates the effects of market power on the systemic dimension of bank risk, while capitalization partially mitigates its impact. |
| Keywords: | Banking;Systemic risk;Securitization;Financial crises;Competition;WP,bank risk,capital ratio,bank competition variable |
| Date: | 2019–07–02 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2019/140&r=all |
| By: | Aref Mahdavi Ardekani (Centre d'Economie de la Sorbonne) |
| Abstract: | By applying the interbank network simulation, this paper examines whether the causal relationship between capital and liquidity is influenced by bank positions in the interbank network. While existing literature highlights the causal relationship that moves from liquidity to capital, the question of how interbank network characteristics affect this relationship remains unclear. Using a sample of commercial banks from 28 European countries, this paper suggests that bank's interconnectedness within interbank loan and deposit networks affects their decisions to set higher or lower regulatory capital ratios when facing higher iliquidity. This study provides support for the need to implement minimum liquidity ratios to complement capital ratios, as stressed by the Basel Committee on Banking Regulation and Supervision. This paper also highlights the need for regulatory authorities to consider the network characteristics of banks |
| Keywords: | Interbank network topology; Bank regulatory capital; Liquidity risk; Basel III |
| JEL: | G21 G28 L14 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:mse:cesdoc:20022r&r=all |
| By: | Stefan Mittnik; Willi Semmler; Alexander Haider |
| Abstract: | Recent research in financial economics has shown that rare large disasters have the potential to disrupt financial sectors via the destruction of capital stocks and jumps in risk premia. These disruptions often entail negative feedback e?ects on the macroecon-omy. Research on disaster risks has also actively been pursued in the macroeconomic models of climate change. Our paper uses insights from the former work to study disaster risks in the macroeconomics of climate change and to spell out policy needs. Empirically the link between carbon dioxide emission and the frequency of climate re-lated disaster is investigated using cross-sectional and panel data. The modeling part then uses a multi-phase dynamic macro model to explore this causal nexus and the e?ects of rare large disasters resulting in capital losses and rising risk premia. Our proposed multi-phase dynamic model, incorporating climate-related disaster shocks and their aftermath as one phase, is suitable for studying mitigation and adaptation policies. |
| Keywords: | Bonds;Greenhouse gas emissions;Climate change;Climate policy;Stocks;WP,monetary policy,capital stock,low income,risk pooling |
| Date: | 2019–07–11 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2019/145&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. |
| Keywords: | Early warning systems;Financial crises;Hedging;Global financial crisis of 2008-2009;Banking crises;WP,sentiment index,crisis sentiment,seed words,sentiment indices,term cluster,word vector representation,word-vector models |
| Date: | 2019–12–06 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2019/273&r=all |
| By: | Michael Kogler |
| Abstract: | How can tax policy improve financial stability? Recent studies suggest large stability gains from eliminating the debt bias in corporate taxation. It is well known that this reform reduces bank leverage. This paper analyzes a novel, complementary channel: risk taking. We model banks’ portfolio choice under moral hazard and emphasize the ‘incentive function’ of equity. We find that (i) an allowance for corporate equity (ACE) and a lower tax rate discourage risk taking and offer stability and welfare gains, (ii) a revenue-neutral ACE unambiguously improves financial stability, and (iii) capital regulation and deposit insurance influence the risk-taking effects of taxation. |
| Keywords: | corporate taxation, tax reform, banking, risk taking, financial stability |
| JEL: | G21 G28 H25 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8830&r=all |
| By: | Mark Kiermayer |
| Abstract: | Surrender poses one of the major risks to life insurance and a sound modeling of its true probability has direct implication on the risk capital demanded by the Solvency II directive. We add to the existing literature by performing extensive experiments that present highly practical results for various modeling approaches, including XGBoost and neural networks. Further, we detect shortcomings of prevalent model assessments, which are in essence based on a confusion matrix. Our results indicate that accurate label predictions and a sound modeling of the true probability can be opposing objectives. We illustrate this with the example of resampling. While resampling is capable of improving label prediction in rare event settings, such as surrender, and thus is commonly applied, we show theoretically and numerically that models trained on resampled data predict significantly biased event probabilities. Following a probabilistic perspective on surrender, we further propose time-dependent confidence bands on predicted mean surrender rates as a complementary assessment and demonstrate its benefit. This evaluation takes a very practical, going concern perspective, which respects that the composition of a portfolio might change over time. |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2101.11590&r=all |
| By: | Otto Konstandatos (Finance Discipline Group, UTS Business School, University of Technology Sydney) |
| Abstract: | Executive stock options (ESOs) are widely used to reward employees and represent major items of corporate liability. The International Accounting Standards Board IFRS9 financial reporting standard which came into full effect on 1-Jan 2018, along with its Australian implementation AASB9, requires public corporations to report their fair-value cost in financial statements. Reset ESOs are typically issued to re-incentivise employees by allowing the option to be cancelled and re-issued with a lower exercise price or later maturity. We produce a novel analytical reset ESO valuation consistent with the IFRS9 financial reporting standard incorporating the simultaneous resetting of vesting period, exercise window, reset level and maturity. We allow for voluntary and involuntary exercise. Our analytical result is expressed solely in terms of standardised European binary power option instruments. Using the multi-state mortality model of Hariyanto (2014) we estimate longitudinal disability and death transition probabilities from cross-sectional data. We determine survival functions for pre-vesting forfeiture or post-vesting involuntary exercise for use with weighted portfolios of our formulae to illustrate the effect of survival on the fair-value. We examine the IFRS9 method of valuation using expected time to option exercise and demonstrate a consistent over-estimation of fair-value of up to 27% for senior executives. |
| Keywords: | Executive compensation; Exotic options; Resetting; Non-life insurance liabilities; IFRS9 |
| JEL: | M40 G30 G32 J33 |
| Date: | 2020–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:uts:rpaper:418&r=all |
| By: | Michal Andrle; Benjamin L Hunt |
| Abstract: | This paper outlines an approach to assess uncertainty around a forecast baseline as well as the impact of alternative policy rules on macro variability. The approach allows for non-Gaussian shock distributions and non-linear underlying macroeconomic models. Consequently, the resulting distributions for macroeconomic variables can exhibit skewness and fat tails. Several applications are presented that illustrate the practical implementation of the technique including confidence bands around a baseline forecast, the probabilities of global growth falling below a specified threshold, and the impact of alternative fiscal policy reactions functions on macro variability. |
| Keywords: | Interest rate floor;Production growth;Zero lower bound;Fiscal policy;WP,distribution,shock distribution,monetary policy,normal distribution |
| Date: | 2020–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2020/064&r=all |
| By: | Majid Bazarbash |
| Abstract: | Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating. |
| Keywords: | Credit risk;Credit;Credit ratings;Loans;Machine learning;WP,ML model,bears risk,machine learning technique,ML analysis,ML evaluation |
| Date: | 2019–05–17 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2019/109&r=all |