Risk Management
http://lists.repec.org/mailman/listinfo/nep-rmg
Risk Management
2022-01-10
Empirical analysis of collateral at central counterparties
http://d.repec.org/n?u=RePEc:srk:srkwps:2021131&r=&r=rmg
This paper studies the risk management of central counterparties (CCPs) using a granular transaction-level dataset. We test whether margining practices are sufﬁcient relative to portfolio risk and whether CCPs reduce margin requirements in a ‟race-to-the-bottom.” We ﬁnd that, for some CCPs, margin breaches are predictable ex ante, but the portfolios of more interconnected clearing members are associated with higher margin holdings. While margin requirements increased signiﬁcantly around the onset of the Covid-19 pandemic, controlling for portfolio and macro-ﬁnancial variables, margin breaches did not. Our results indicate that changes in margins should be analyzed alongside margin breaches. JEL Classification: G23, G21, G15
Grothe, Magdalena
Pancost, N. Aaron
Tompaidis, Stathis
CCP, initial margin, risk management, variation margin
2021-12
Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data
http://d.repec.org/n?u=RePEc:pre:wpaper:202201&r=&r=rmg
We use monthly data covering a century-long sample period (1915-2021) to study whether geopolitical risk helps to forecast subsequent gold returns and gold volatility. We account not only for geopolitical threats and acts, but also for 39 country-specific sources of geopolitical risk. The response of subsequent returns and volatility is heterogeneous across countries and nonlinear. We find that accounting for geopolitical risk at the country level improves forecast accuracy especially when we use random forests to estimate our forecasting models. As an extension, we report empirical evidence on the predictive value of the country-level sources of geopolitical risk for two other candidate safe-haven assets, oil and silver, over the sample periods 1900â€“2021 and 1915â€“2021, respectively. Our results have important implications for the portfolio decisions of investors who seek a safe haven in times of heightened geopolitical tensions.
Rangan Gupta
Sayar Karmakar
Christian Pierdzioch
Gold, Geopolitical Risk, Forecasting, Returns, Volatility, Random Forests
2022-01
Risk-Taking and Tail Events Across Trading Institutions
http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03468913&r=&r=rmg
We study the reaction of investors to tail events across trading institutions. We conduct experiments in which investors bid on a financial asset that delivers a small positive reward in more than 99% of the cases and a large loss otherwise. The baseline treatment uses a repeated BDM mechanism whereas the market treatment replaces the uniform draw of the BDM mechanism by a uniform draw over the bids of the other participants. Our design is such that bids should not differ across treatments in normal times while allowing for potential differences to emerge after tail events have occurred. We find that markets tend to exacerbate the reaction of investors to tail losses and we attribute this effect to emotions.
Brice Corgnet
Camille Cornand
Nobuyuki Hanaki
Tail events,trading institutions,experimental finance,emotions and risk
2021-12-07
A Theoretical Foundation for Prudential Authorities Decision Making
http://d.repec.org/n?u=RePEc:inf:wpaper:2021.11&r=&r=rmg
In the aftermath of the Global Financial Crisis, financial regulation uses micro- and macro-prudential rules, most of the time motivated by empirical studies. This article suggests a theoretical explanation for countercyclical and progressive capital requirements that incorporate micro- and macro-prudential stabilization objectives. The Capital Adequacy Ratio (CAR) imposed to individual banks by a Prudential Authority (PA) would thus represent an optimal regulation whose aim is to avoid individual and systemic risk accumulation by imposing minimal constraints to financial institutions. This corresponds to the implementation of optimal time-varying prudential capital requirements to banks, with non-linear structure, that allows PA to take progressive countercyclical actions in order to insure financial stability. We also test the mechanism in a DSGE model and show that it would be more suitable for the financial and real stability compared to the existing fixed prudential ratios.
Cristina Badarau
Corentin Roussel
prudential regulation model, optimal CAR, time-varying capital requirements, DSGE model
2021
The COVID-19 Impact on Corporate Leverage and Financial Fragility
http://d.repec.org/n?u=RePEc:imf:imfwpa:2021/265&r=&r=rmg
We study the impact of the COVID-19 recession on capital structure of publicly listed U.S. firms. Our estimates suggest leverage (Net Debt/Asset) decreased by 5.3 percentage points from the pre-shock mean of 19.6 percent, while debt maturity increased moderately. This de-leveraging effect is stronger for firms exposed to significant rollover risk, while firms whose businesses were most vulnerable to social distancing did not reduce leverage. We rationalize our evidence through a structural model of firm value that shows lower expected growth rate and higher volatility of cash flows following COVID-19 reduced optimal levels of corporate leverage. Model-implied optimal leverage indicates firms which did not de-lever became over-leveraged. We find default probability deteriorates most in large, over-leveraged firms and those that were stressed pre-COVID. Additional stress tests predict value of these firms will be less than one standard deviation away from default if cash flows decline by 20 percent.
Mr. Richard Varghese
Sharjil M. Haque
COVID-19; Corporate Debt; Optimal Capital Structure; Rollover Risk; Distance-To-Default; Default Risk; Stress Tests
2021-11-05
Life insurance convexity
http://d.repec.org/n?u=RePEc:zbw:icirwp:4221&r=&r=rmg
Life insurers massively sell savings contracts with surrender options which allow policyholders to withdraw a guaranteed amount before maturity. These options move toward the money when interest rates rise. Using data on German life insurers, we estimate that a 1 percentage point increase in interest rates raises surrender rates by 17 basis points. We quantify the resulting liquidity risk in a calibrated model of surrender decisions and insurance cash flows. Simulations predict that surrender options can force insurers to sell up to 3% of their assets, depressing asset prices by 90 basis points. The effect is amplified by the duration of insurers' investments, and its impact on the term structure of interest rates depends on life insurers' investment strategy.
Kubitza, Christian
Grochola, Nicolaus
Gründl, Helmut
Life Insurance,Liquidity Risk,Interest Rates,Fire Sales,Systemic Risk
2021
Life-cycle risk-taking with personal disaster risk
http://d.repec.org/n?u=RePEc:srk:srkwps:2021132&r=&r=rmg
This paper examines households’ self-insurance in financial markets when a rare personal disaster, such as disability or long-term unemployment, may occur during working years. Personal disaster risk alters lifetime ex-ante investment choices, even if most workers will not experience a disaster. Uncertainty about the size of human capital losses, which characterizes rare disasters, results in lower risk-taking at the beginning of working life, and is crucial in order to match the observed age profiles of US investors from 1992 to 2016. JEL Classification: D15, E21, G11
Bagliano, Fabio C.
Fugazza, Carolina
Nicodano, Giovanna
beta distribution, disability risk, disaster risk, non-linear income process, portfolio choice, unemployment risk
2021-12
Geopolitical Risk on Stock Returns: Evidence from Inter-Korea Geopolitics
http://d.repec.org/n?u=RePEc:imf:imfwpa:2021/251&r=&r=rmg
We investigate how corporate stock returns respond to geopolitical risk in the case of South Korea, which has experienced large and unpredictable geopolitical swings that originate from North Korea. To do so, a monthly index of geopolitical risk from North Korea (the GPRNK index) is constructed using automated keyword searches in South Korean media. The GPRNK index, designed to capture both upside and downside risk, corroborates that geopolitical risk sharply increases with the occurrence of nuclear tests, missile launches, or military confrontations, and decreases significantly around the times of summit meetings or multilateral talks. Using firm-level data, we find that heightened geopolitical risk reduces stock returns, and that the reductions in stock returns are greater especially for large firms, firms with a higher share of domestic investors, and for firms with a higher ratio of fixed assets to total assets. These results suggest that international portfolio diversification and investment irreversibility are important channels through which geopolitical risk affects stock returns.
Seungho Jung
Jongmin Lee
Seohyun Lee
Geopolitical risk, Textual analysis, Stock returns, Inter-Korean relations.; GPRNK index; stock return; firms' stock; GPRNK trend; search keyword; growth outlook; Stocks; Economic cooperation; Stock markets; Asset prices; Global
2021-10-22
Reinforcement learning for options on target volatility funds
http://d.repec.org/n?u=RePEc:arx:papers:2112.01841&r=&r=rmg
In this work we deal with the funding costs rising from hedging the risky securities underlying a target volatility strategy (TVS), a portfolio of risky assets and a risk-free one dynamically rebalanced in order to keep the realized volatility of the portfolio on a certain level. The uncertainty in the TVS risky portfolio composition along with the difference in hedging costs for each component requires to solve a control problem to evaluate the option prices. We derive an analytical solution of the problem in the Black and Scholes (BS) scenario. Then we use Reinforcement Learning (RL) techniques to determine the fund composition leading to the most conservative price under the local volatility (LV) model, for which an a priori solution is not available. We show how the performances of the RL agents are compatible with those obtained by applying path-wise the BS analytical strategy to the TVS dynamics, which therefore appears competitive also in the LV scenario.
Roberto Daluiso
Emanuele Nastasi
Andrea Pallavicini
Stefano Polo
2021-12
What should be taken into consideration when forecasting oil implied volatility index?
http://d.repec.org/n?u=RePEc:pra:mprapa:110831&r=&r=rmg
Crude oil is considered a key commodity in all the economies around the world. This study forecasts the oil volatility index (OVX), which is the market’s expectation of future oil volatility, by incorporating information from other asset classes. The literature does not extensively test the long memory of the targeted volatility. Thus, we estimate the Hurst exponent implementing a rolling window rescaled analysis. We provide evidence for a strong long memory in the implied volatility (IV) indices which justifies the use of the HAR model in obtaining multiple days ahead OVX forecasts. We also define a dynamic model averaging (DMA) structure in the HAR model in order to allow for IV indices from other asset classes to be applicable at different time periods. The implementation of the DMA-HAR models informs forecasters to focus on the major stock market IV indices, and more specifically on the DJIA Volatility Index. Our results lead us to the conclusion that accurate OVX forecasts are obtained for short- and mid-run forecasting horizons. The evaluation framework is not limited to statistical loss functions but also embodies an options straddle trading strategy.
Delis, Panagiotis
Degiannakis, Stavros
Giannopoulos, Kostantinos
crude oil, implied volatility, HAR modelling, trading strategies, dynamic model averaging, long memory
2021-11-26
RPS: Portfolio Asset Selection using Graph based Representation Learning
http://d.repec.org/n?u=RePEc:arx:papers:2111.15634&r=&r=rmg
Portfolio optimization is one of the essential fields of focus in finance. There has been an increasing demand for novel computational methods in this area to compute portfolios with better returns and lower risks in recent years. We present a novel computational method called Representation Portfolio Selection (RPS) by redefining the distance matrix of financial assets using Representation Learning and Clustering algorithms for portfolio selection to increase diversification. RPS proposes a heuristic for getting closer to the optimal subset of assets. Using empirical results in this paper, we demonstrate that widely used portfolio optimization algorithms, such as MVO, CLA, and HRP, can benefit from our asset subset selection.
MohammadAmin Fazli
Parsa Alian
Ali Owfi
Erfan Loghmani
2021-11
Adaptive calibration of Heston Model using PCRLB based switching Filter
http://d.repec.org/n?u=RePEc:arx:papers:2112.04576&r=&r=rmg
Stochastic volatility models have existed in Option pricing theory ever since the crash of 1987 which violated the Black-Scholes model assumption of constant volatility. Heston model is one such stochastic volatility model that is widely used for volatility estimation and option pricing. In this paper, we design a novel method to estimate parameters of Heston model under state-space representation using Bayesian filtering theory and Posterior Cramer-Rao Lower Bound (PCRLB), integrating it with Normal Maximum Likelihood Estimation (NMLE) proposed in [1]. Several Bayesian filters like Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF) are used for latent state and parameter estimation. We employ a switching strategy proposed in [2] for adaptive state estimation of the non-linear, discrete-time state-space model (SSM) like Heston model. We use a particle filter approximated PCRLB [3] based performance measure to judge the best filter at each time step. We test our proposed framework on pricing data from S&P 500 and NSE Index, estimating the underlying volatility and parameters from the index. Our proposed method is compared with the VIX measure and historical volatility for both the indexes. The results indicate an effective framework for estimating volatility adaptively with changing market dynamics.
Kumar Yashaswi
2021-12
The Geometry of Risk Adjustments
http://d.repec.org/n?u=RePEc:hhs:luwick:2021_002&r=&r=rmg
In this paper we present a geometric approach to portfolio theory, with the aim to explain the geometrical principles behind risk adjusted returns; in particular Jensen’s alpha. We find that while the alpha/beta approach has severe limitations (especially in higher dimensions), only minor conceptual modifications are needed to complete the picture. However, these modifications (e.g. using risk adjusted Sharpe ratios rather than returns) can only be appreciated once a full geometric approach to portfolio theory is developed. We further show that, in a complete market, the so called market price of risk vector is identical to the growth optimal Kelly vector, albeit expressed in coordinates of a different basis. For trading strategies collinear to the growth optimal Kelly vector, we formalise a notion of relative value trading based on the risk adjusted Sharpe ratio. As an application we show that a derivative having a risk adjusted Sharpe ratio of zero has a corresponding price given by the the minimal martingale measure.
Bermin, Hans-Peter
Holm, Magnus
Jensen’s alpha; Kelly criterion; market price of risk; option pricing; geometry
2021-12-01
Unintended Consequences of the Global Derivatives Market Reform
http://d.repec.org/n?u=RePEc:drm:wpaper:2021-36&r=&r=rmg
The G-20’s global over-the-counter (OTC) derivatives market reform has caused a dramatic shift in the geography of the global derivatives market. Following the early implementation of the reform in the US and associated increase in the cost of trading derivatives, US banks shifted up to 60 percent of their OTC derivatives activity abroad, particularly towards less regulated jurisdictions. This implies an increase in global risk as risk is shifted to jurisdictions that are less prepared to monitor it and deal with the consequences. Further, we find that foreign subsidiaries in more tightly regulated jurisdictions have increased risk-taking overall.
Pauline Gandré
Mike Mariathasan
Ouarda Merrouche
Steven Ongena
Bank regulation, Regulatory arbitrage, OTC Markets, Derivatives, Cross-border financial institutions, Financial risk.
2021
Corporate Liquidity During the Covid-19 Crisis: the Trade Credit Channel
http://d.repec.org/n?u=RePEc:bfr:banfra:851&r=&r=rmg
Using unique daily data on payment defaults to suppliers in France, we show how the trade credit channel amplified the Covid-19 shock, during the first months of the pandemic. It dramatically increased short-term liquidity needs in the most impacted downstream sectors: a one standard deviation increase in net trade credit position leads to a rise in the probability of default of up to a third. This effect is short-term and cyclical and is concentrated on financially constrained firms. We argue that taking into account the trade credit channel is critical to properly quantify liquidity shortfalls in crisis times.
Benjamin Bureau
Anne Duquerroy
Frédéric Vinas
Firm, Corporate Finance, Trade Credit, Liquidity, Payment Default, Covid-19, Lockdown, Pandemic
2021
The Impact of Islamic Portfolio on Risk and Return
http://d.repec.org/n?u=RePEc:pra:mprapa:111211&r=&r=rmg
The purpose of this study is to investigate the comparative impact of conventional and Islamic bonds over returns. It provides useful insights to investors to diversify investment by lowering the risk to the optimum level. This study examines the impact of the conventional and Islamic portfolios on returns through simple OLS regression, suggesting that Sukuk returns are positive and significant. Simultaneously, conventional bonds show a negative trend, but in the long run, the returns are significant. It indicates that the market is volatile due to macroeconomic factors that can reduce risks through portfolio diversification. Thus, this research suggests that investment can be secured by taking a rational portfolio decision that confirms robustness. Therefore, it is a good opportunity for the investors to get high margins over the investment tenure.
Alim, Wajid
Ali, Amjad
Farid, Maryiam
Financial Instruments, Portfolio Diversification, Islamic Finance, Sukuk, Conventional Bonds
2021-11-10
Climate and environmental risks: measuring the exposure of investments
http://d.repec.org/n?u=RePEc:bdi:wpmisp:mip_015_21&r=&r=rmg
This paper presents a number of methodologies for assessing the climate risk exposure of several financial asset classes. Regarding government bonds, the paper proposes using public information; in order to develop forward-looking measures of countriesâ€™ risk exposure, the paper uses historical trends combined with governmentsâ€™ climate commitments and the scenarios developed by the Network for Greening the Financial System. With regard to private sector issuers, the paper finds quite a high coverage and correlation amongst the carbon emissions data from different providers, while the divergences in the data for other environmental indicators are still significant. Finally, the paper shows that the application of sustainability criteria in the Bank of Italyâ€™s investment strategy delivered a non-negligible reduction in the exposure to the climate and environmental risks of the portfolios.
Enrico Bernardini
Johnny Di Giampaolo
Ivan Faiella
Marco Fruzzetti
Simone Letta
Raffaele Loffredo
Davide Nasti
sustainable finance, investments, climate risks, environmental risks
2021-12
Bank equity, interest payments, and credit creation under Basel III regulations
http://d.repec.org/n?u=RePEc:pra:mprapa:111269&r=&r=rmg
Both equity and regulation play key roles in determining the ability of credit creation of banks. The equity endogenously varies while the regulations are exogenously imposed. I propose a banking model to investigate how the changes in bank equity due to interest receipt and expenditure affect credit and money creation under the Basel III regulations. Three Basel III regulations are discussed: the capital adequacy ratio, liquidity coverage ratio, and net stable funding ratio. The effects on credit creation are demonstrated by the changes in the credit supply in response to the interest payments changing the equity. My results indicate that the changes in equity cause multiplier effects on the credit supply. The multipliers depend on the regulatory constraints. Similarly, I present the impacts on money creation, given by the multiplier effects on the money supply. This study sheds considerable light on how bank equity and Basel III regulations affect credit and money creation.
Li, Boyao
Credit creation; Basel III; Bank equity; Interest payments; Multiplier effect; Balance sheet
2021-12-28
Global risk and the dollar
http://d.repec.org/n?u=RePEc:ecb:ecbwps:20212628&r=&r=rmg
How does global risk impact the world economy? In taking up this question, we focus on the dollar’s role in the international adjustment mechanism. First, we rely on high-frequency surprises in the price of gold to identify the effects of global risk shocks in a Bayesian Proxy VAR model. They cause a synchronized contraction of global economic activity and appreciate the dollar. Other key financial indicators adjust in line with pre-dictions of recent theoretical work. Second, we illustrate through counterfactuals that the dollar appreciation amplifies the adverse impact of global risk shocks outside of the US via a financial channel. JEL Classification: F31, F42, F44
Georgiadis, Georgios
Müller, Gernot J.
Schumann, Ben
Bayesian proxy structural VAR, counterfactual, global risk shocks, minimum relative entropy, US dollar exchange rate
2021-12
Commercial Real Estate and Macrofinancial Stability During COVID-19
http://d.repec.org/n?u=RePEc:imf:imfwpa:2021/264&r=&r=rmg
The COVID-19 pandemic crisis has severely shocked the commercial real estate (CRE) sector, which could have important implications for macro-financial stability going forward because of the large size of the sector and its strong interconnectedness with the real economy. Using a novel methodology, this paper quantifies vulnerabilities in the CRE sector and analyzes policy tools available to mitigate related risks. The analysis shows that CRE prices were overvalued in several major advanced economies in 2020:Q1. It also shows that such price misalignments increase the likelihood of future price corrections and exacerbate downside risks to future GDP growth. While the path of recovery in the sector will depend inherently on the pace of overall economic recovery and the structural shifts induced by the pandemic, easy financial conditions may contribute to an increase in financial vulnerabilities and persistent price misalignment. Macroprudential policy can, however, be effective in curbing the financial stability risks posed by the CRE sector.
Mr. Junghwan Mok
Andrea Deghi
Tomohiro Tsuruga
Commercial Real Estate; Asset Prices; Growth-at-Risk; Panel Quantile Regression; Macroprudential Policy
2021-11-05
A General Approach for Lookback Option Pricing under Markov Models
http://d.repec.org/n?u=RePEc:arx:papers:2112.00439&r=&r=rmg
We propose a very efficient method for pricing various types of lookback options under Markov models. We utilize the model-free representations of lookback option prices as integrals of first passage probabilities. We combine efficient numerical quadrature with continuous-time Markov chain approximation for the first passage problem to price lookbacks. Our method is applicable to a variety of models, including one-dimensional time-homogeneous and time-inhomogeneous Markov processes, regime-switching models and stochastic local volatility models. We demonstrate the efficiency of our method through various numerical examples.
Gongqiu Zhang
Lingfei Li
2021-12
Forex Trading Volatility Prediction using NeuralNetwork Models
http://d.repec.org/n?u=RePEc:arx:papers:2112.01166&r=&r=rmg
In this paper, we investigate the problem of predicting the future volatility of Forex currency pairs using the deep learning techniques. We show step-by-step how to construct the deep-learning network by the guidance of the empirical patterns of the intra-day volatility. The numerical results show that the multiscale Long Short-Term Memory (LSTM) model with the input of multi-currency pairs consistently achieves the state-of-the-art accuracy compared with both the conventional baselines, i.e. autoregressive and GARCH model, and the other deep learning models.
Shujian Liao
Jian Chen
Hao Ni
2021-12