|
on Risk Management |
| By: | H. Rangika Iroshani Peiris; Chao Wang; Richard Gerlach; Minh-Ngoc Tran |
| Abstract: | A semi-parametric joint Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting framework employing multiple realized measures is developed. The proposed framework extends the quantile regression using multiple realized measures as exogenous variables to model the VaR. Then, the information from realized measures is used to model the time-varying relationship between VaR and ES. Finally, a measurement equation that models the contemporaneous dependence between the quantile and realized measures is used to complete the model. A quasi-likelihood, built on the asymmetric Laplace distribution, enables the Bayesian inference for the proposed model. An adaptive Markov Chain Monte Carlo method is used for the model estimation. The empirical section evaluates the performance of the proposed framework with six stock markets from January 2000 to June 2022, covering the period of COVID-19. Three realized measures, including 5-minute realized variance, bi-power variation, and realized kernel, are incorporated and evaluated in the proposed framework. One-step ahead VaR and ES forecasting results of the proposed model are compared to a range of parametric and semi-parametric models, lending support to the effectiveness of the proposed framework. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.09985&r=rmg |
| By: | Sahab Zandi; Kamesh Korangi; Mar\'ia \'Oskarsd\'ottir; Christophe Mues; Cristi\'an Bravo |
| Abstract: | Whereas traditional credit scoring tends to employ only individual borrower- or loan-level predictors, it has been acknowledged for some time that connections between borrowers may result in default risk propagating over a network. In this paper, we present a model for credit risk assessment leveraging a dynamic multilayer network built from a Graph Neural Network and a Recurrent Neural Network, each layer reflecting a different source of network connection. We test our methodology in a behavioural credit scoring context using a dataset provided by U.S. mortgage financier Freddie Mac, in which different types of connections arise from the geographical location of the borrower and their choice of mortgage provider. The proposed model considers both types of connections and the evolution of these connections over time. We enhance the model by using a custom attention mechanism that weights the different time snapshots according to their importance. After testing multiple configurations, a model with GAT, LSTM, and the attention mechanism provides the best results. Empirical results demonstrate that, when it comes to predicting probability of default for the borrowers, our proposed model brings both better results and novel insights for the analysis of the importance of connections and timestamps, compared to traditional methods. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.00299&r=rmg |
| By: | Marah-Lisanne Thormann; Phan Tu Vuong; Alain B. Zemkoho |
| Abstract: | A highly relevant problem of modern finance is the design of Value-at-Risk (VaR) optimal portfolios. Due to contemporary financial regulations, banks and other financial institutions are tied to use the risk measure to control their credit, market and operational risks. For a portfolio with a discrete return distribution and finitely many scenarios, a Difference of Convex (DC) functions representation of the VaR can be derived. Wozabal (2012) showed that this yields a solution to a VaR constrained Markowitz style portfolio selection problem using the Difference of Convex Functions Algorithm (DCA). A recent algorithmic extension is the so-called Boosted Difference of Convex Functions Algorithm (BDCA) which accelerates the convergence due to an additional line search step. It has been shown that the BDCA converges linearly for solving non-smooth quadratic problems with linear inequality constraints. In this paper, we prove that the linear rate of convergence is also guaranteed for a piecewise linear objective function with linear equality and inequality constraints using the Kurdyka-{\L}ojasiewicz property. An extended case study under consideration of best practices for comparing optimization algorithms demonstrates the superiority of the BDCA over the DCA for real-world financial market data. We are able to show that the results of the BDCA are significantly closer to the efficient frontier compared to the DCA. Due to the open availability of all data sets and code, this paper further provides a practical guide for transparent and easily reproducible comparisons of VaR constrained portfolio selection problems in Python. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.09194&r=rmg |
| By: | Shoka Hayaki (Faculty of Economics, Kagawa University and Research Institute for Economics and Business Administration, Kobe University, JAPAN) |
| Abstract: | Traditional frameworks often fail to adequately explain the observed procyclical nature of the risk-return trade-off associated with aggregate risk aversion in recent years. This study introduces a simple model incorporating the concepts of loss aversion and state-dependent preferences. The model suggests an initial positive adjustment to the risk-return trade-off when the shock occurs, followed by a negative adjustment once the shock fully manifests. Essentially, the risk-return trade-off temporarily becomes procyclical as the shock spreads. In this study, the nonlinear structure of the risk-return trade-off is approximated using natural cubic splines with several constraints. Estimation results based on market excess returns in the United States indicate that a nonlinear risk-return trade-off, consistent with the model, offers valuable insights for pricing. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:kob:dpaper:dp2024-05&r=rmg |
| By: | Grill, Michael; Popescu, Alexandra; Rancoita, Elena |
| Abstract: | Climate-related risks are due to increase in coming years and can pose serious threats to financial stability. This paper, by means of a DSGE model including heterogeneous firms and banks, financial frictions and prudential regulation, first shows the need of climate-related capital requirements in the existing prudential framework. Indeed, we find that without specific climate prudential policies, transition risk can generate excessive risk-taking by banks, which in turn increases the volatility of lending and output. We further show that relying on microprudential regulation alone would not be enough to account for the systemic dimension of transition risk. Implementing macroprudential policies in addition to microprudential regulation, leads to a Pareto improvement. JEL Classification: D58, E58, E61, Q54 |
| Keywords: | prudential regulation, transition risk, financial frictions |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20242910&r=rmg |
| By: | Chotipong Charoensom |
| Abstract: | This paper proposes an approach to develop regime switching models where latent process determining the switching is endogenously controlled by the model shocks with free functional forms. The linear endogeneity assumption in the conventional endogenous regime switching models can therefore be relaxed. A recursive filter technique is applied to proceed maximum likelihood estimation in order to estimate the model parameters. A nonlinear endogenous two-regime switching mean-volatility model is conducted in numerical examples to investigate the model performance. In the examples, the endogeneity in switching allows heterogeneous effects of the shock signs (asymmetric endogeneity) and of the states being before the switching determination (state-dependent endogeneity). Monte Carlo simulations show that the conventional switching model ignoring the nonlinear endogeneity leads to the volatility biases. The estimates tend to be over or under their true value depending on how the endogeneity characteristics are. In particular, the true model that accounts the nonlinear endogeneity effectively provides the more precise estimates. The same model is also applied to real data of excess returns on US stock market, and the estimation results informatively describe the effects influencing the regime shifts. |
| Keywords: | Nonlinear endogeneity; Regime switching; Maximum likelihood estimation; Asymmetric endogeneity; State-dependent endogeneity |
| JEL: | C13 C32 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:pui:dpaper:217&r=rmg |
| By: | Narayan Tondapu |
| Abstract: | In this study, we examine the fluctuation in the value of the Great Britain Pound (GBP). We focus particularly on its relationship with the United States Dollar (USD) and the Euro (EUR) currency pairs. Utilizing data from June 15, 2018, to June 15, 2023, we apply various mathematical models to assess their effectiveness in predicting the 20-day variation in the pairs' daily returns. Our analysis involves the implementation of Exponentially Weighted Moving Average (EWMA), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, and Implied Volatility (IV) models. To evaluate their performance, we compare the accuracy of their predictions using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. We delve into the intricacies of GARCH models, examining their statistical characteristics when applied to the provided dataset. Our findings suggest the existence of asymmetric returns in the EUR/GBP pair, while such evidence is inconclusive for the GBP/USD pair. Additionally, we observe that GARCH-type models better fit the data when assuming residuals follow a standard t-distribution rather than a standard normal distribution. Furthermore, we investigate the efficacy of different forecasting techniques within GARCH-type models. Comparing rolling window forecasts to expanding window forecasts, we find no definitive superiority in either approach across the tested scenarios. Our experiments reveal that for the GBP/USD pair, the most accurate volatility forecasts stem from the utilization of GARCH models employing a rolling window methodology. Conversely, for the EUR/GBP pair, optimal forecasts are derived from GARCH models and Ordinary Least Squares (OLS) models incorporating the annualized implied volatility of the exchange rate as an independent variable. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.07435&r=rmg |
| By: | Kroll, Yoram; Marchioni, Andrea; Ben-Horin, Moshe |
| Abstract: | A portfolio’s Sortino ratio is strongly affected by the risk-free vs. risky assets mix, except for the case where the threshold, T is equal to the risk-free rate. Therefore, if T differs from the risk-free rate, the portfolio’s Sortino ratio could potentially be increased by merely changing the mix of the risk-free and the risky components. The widely used Sharpe ratio, on the other hand, does not share this caveat. We introduce a modified Sortino ratio, Sortino(γ), which is invariant with respect to the portfolio’s risk-free vs. risky assets mix, and hence eliminates the above deficiency. The selected threshold T(γ), mimics the portfolio composition in the sense that it equals to the risk-free rate plus γ times the portfolio’s equity risk premium. Higher selected γ reflects higher risk/loss aversion. We propose a procedure for optimizing the composition of the risky portion of the portfolio to maximize the Sortino(γ) ratio. In addition, we show that Sortino(γ) is consistent with first and second order stochastic dominance with riskless asset rules. |
| Keywords: | Performance ratios; Sortino ratio; Risk aversion; Loss aversion; FSDR rule; SSDR rule |
| JEL: | C0 G0 |
| Date: | 2024–01–02 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:120203&r=rmg |
| By: | Einmahl, John (Tilburg University, School of Economics and Management); Zhou, C. (Tilburg University, School of Economics and Management) |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiutis:6bcb09c5-8b19-48b8-9320-b80e0d9db36b&r=rmg |
| By: | Eduardo Abi Jaber; Louis-Amand G\'erard |
| Abstract: | We consider a stochastic volatility model where the dynamics of the volatility are given by a possibly infinite linear combination of the elements of the time extended signature of a Brownian motion. First, we show that the model is remarkably universal, as it includes, but is not limited to, the celebrated Stein-Stein, Bergomi, and Heston models, together with some path-dependent variants. Second, we derive the joint characteristic functional of the log-price and integrated variance provided that some infinite dimensional extended tensor algebra valued Riccati equation admits a solution. This allows us to price and (quadratically) hedge certain European and path-dependent options using Fourier inversion techniques. We highlight the efficiency and accuracy of these Fourier techniques in a comprehensive numerical study. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.01820&r=rmg |
| By: | Changqing Teng; Guanglian Li |
| Abstract: | The rough Bergomi (rBergomi) model can accurately describe the historical and implied volatilities, and has gained much attention in the past few years. However, there are many hidden unknown parameters or even functions in the model. In this work, we investigate the potential of learning the forward variance curve in the rBergomi model using a neural SDE. To construct an efficient solver for the neural SDE, we propose a novel numerical scheme for simulating the volatility process using the modified summation of exponentials. Using the Wasserstein 1-distance to define the loss function, we show that the learned forward variance curve is capable of calibrating the price process of the underlying asset and the price of the European-style options simultaneously. Several numerical tests are provided to demonstrate its performance. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.02714&r=rmg |
| By: | Schmidt, James |
| Abstract: | Because of increasing wildfire risk and associated losses, fire insurance has become more difficult to obtain in Northern California. The only insurance alternative for homeowners who are unable to find conventional home insurance is limited and costly coverage available through the California FAIR Plan. Counties located in the Central Sierras have been particularly hard hit with insurance cancellations. FAIR Plan policies in several of those counties exceeded 20% of all policies in 2021. Results from three recent assessments, based on wildfire simulation models, agree that counties in the Central Sierras are among the most at-risk for wildfire-caused structure loss. Most housing losses in the 2013-2022 decade, however, were the result of wind-driven fires in the Northern Sierras and in the Northern San Francisco Bay Area. 85% of all losses occurred in fires where a Red Flag Warning (RFW) for high winds had been issued by the National Weather Service. The Northern Sierras and the North Bay Area averaged 60% more RFW days during the fall fire season compared to the Central Sierras. Strong downslope “Diablo” winds from the Great Basin deserts were involved in seven of the most destructive fires, accounting for 65% of the total housing losses. Based on records from 109 weather stations throughout the Sierras and the Bay Area, these wind events occur primarily in the Northern Sierras and the Bay Area. Climate models have predicted that Diablo-type winds should decrease as the interior deserts warm, but weather stations in both the Bay Area and the Sierras recorded a large increase in the number of strong DiabIo wind days in the 2017 through 2021 years. All seven of the Diablo wind fires occurred during that time span. Fires driven by strong Diablo winds fit into a category of disasters referred to as “black swan” events – rare occurrences that have very large effects. Because these fires occur so infrequently, they have minimal effect on risk estimates produced by averaging together the outcomes of thousands of simulations. Exceedance probability analysis (Ager et al., 2021) can help to identify the communities most at risk from such high-loss, low-probability events. Combining exceedance probability analysis with simulation models that capture the frequency and location of extreme wind events should cause county risk rankings to more closely match actual losses. As a result, the relative risk ratings (and FAIR Plan policies) assigned to the Central Sierras should be reduced. |
| Keywords: | Wildfire; Fire Insurance; FAIR Plan; Diablo Wind; Red Flag Warnings; Exceedance Probability; Black Swan; Simulation; FSIM; ELMFIRE; Exposure; Ignition Density; Risk; California; Downslope Winds; Climate models; RAWS; weather stations; Wildland Urban Interface; WUI; Camp Fire; Tubbs Fire; Central Sierras; San Francisco Bay Area; Northern Sierras; |
| JEL: | G22 Q0 Q54 Y1 Y91 |
| Date: | 2024–02–15 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:120195&r=rmg |
| By: | Schult, Christoph |
| Abstract: | I estimate a dynamic stochastic general equilibrium (DSGE) model for the United States that incorporates oil market shocks and risk shocks working through credit market frictions. The findings of this analysis indicate that risk shocks play a crucial role during the Great Recession and the Dot-Com bubble but not during other economic downturns. Credit market frictions do not amplify persistent oil market shocks. This result holds as long as entry and exit rates of entrepreneurs are independent of the business cycle. |
| Keywords: | financial frictions, NK-DSGE models, oil price, recessions, risk |
| JEL: | E32 E37 E44 Q43 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwhdps:283617&r=rmg |
| By: | Favero, Carlo A. (Bocconi U); Melone, Alessandro (Ohio State U); Tamoni, Andrea (Rutgers U) |
| Abstract: | According to a no-arbitrage condition, risk-adjusted returns should be unpredictable. Using several prominent factor models and a large cross-section of anomalies, we find that past cumulative risk-adjusted returns predict future anomaly returns. Cumulative returns can be interpreted as deviations of an anomaly price from the price of the mean-variance efficient portfolio. Price deviations constitute a novel anomaly-specific predictor, endogenous to the given heuristic mean-variance portfolio, thus providing direct evidence for conditional misspecification. A zero-cost investment strategy using price deviations generates positive alphas. Our findings suggest that incorporating price information into cross-sectional models improves their ability to capture time-series return dynamics. |
| JEL: | C38 G12 G17 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:ohidic:2023-20&r=rmg |
| By: | Turner, Dylan; Tsiboe, Francis; Baldwin, Katherine; Williams, Brian; Dohlman, Erik; Astill, Gregory; Skorbiansky, Sharon Raszap; Abadam, Vidalina; Yeh, D. Adeline; Knight, Russell |
| Abstract: | This report provides a broad overview of the Federal programs that are designed to help agricultural producers manage risks to income or profitability caused by natural and economic forces. This report refers to these programs as “risk management programs.” Focus is given to risk management programs that are available under the Agriculture Improvement Act of 2018 (i.e., 2018 Farm Bill). Thus, this publication serves as an update to previous work (Motamed et al., 2018), which focused on programs available under the Agricultural Act of 2014 (i.e., 2014 Farm Bill). Although each title of the Farm Bill contains programs that attenuate risk indirectly, most current targeted risk management programs are authorized under Title I: Commodity Programs or Title XI: Crop Insurance. Accordingly, this report primarily focuses on programs for crop and livestock producers that are available under these two titles. Available policies for managing production and price risk are discussed with recent trends in program enrollment and outlays provided. |
| Keywords: | Agricultural and Food Policy, Agricultural Finance, Crop Production/Industries, Livestock Production/Industries, Risk and Uncertainty |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:ags:uersib:340216&r=rmg |
| By: | Federico C. Nucera (Bank of Italy); Lucio Sarno (University of Cambridge - Judge Business School); Gabriele Zinna (Bank of Italy) |
| Abstract: | We study a large currency cross section using asset pricing methods which account for omitted-variable and measurement-error biases. First, we show that the pricing kernel includes at least three latent factors which resemble (but are not identical to) a strong U.S. "Dollar" factor, and two weak, high Sharpe ratio "Carry" and "Momentum" slope factors. Evidence for an additional "Value" factor is weaker. Second, using this pricing kernel, we find that only a small fraction of the over 100 nontradable candidate factors considered have a statistically significant risk premium – mostly relating to volatility, uncertainty and liquidity conditions, rather than macro variables. |
| Keywords: | : currency risk premiums, asset pricing, omitted factors, measurement error, weak factors |
| JEL: | F31 G12 G15 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:wptemi:td_1415_23&r=rmg |
| By: | Simona Sanfelici; Giacomo Toscano |
| Abstract: | This paper presents the Fourier-Malliavin Volatility (FMVol) estimation library for MATLAB. This library includes functions that implement Fourier- Malliavin estimators (see Malliavin and Mancino (2002, 2009)) of the volatility and co-volatility of continuous stochastic volatility processes and second-order quantities, like the quarticity (the squared volatility), the volatility of volatility and the leverage (the covariance between changes in the process and changes in its volatility). The Fourier-Malliavin method is fully non-parametric, does not require equally-spaced observations and is robust to measurement errors, or noise, without any preliminary bias correction or pre-treatment of the observations. Further, in its multivariate version, it is intrinsically robust to irregular and asynchronous sampling. Although originally introduced for a specific application in financial econometrics, namely the estimation of asset volatilities, the Fourier-Malliavin method is a general method that can be applied whenever one is interested in reconstructing the latent volatility and second-order quantities of a continuous stochastic volatility process from discrete observations. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.00172&r=rmg |
| By: | Alessandro Borin (Bank of Italy); Gianmarco Cariola (Bank of Italy); Elena Gentili (Bank of Italy); Andrea Linarello (Bank of Italy); Michele Mancini (Bank of Italy); Tullia Padellini (Bank of Italy); Ludovic Panon (Bank of Italy); Enrico Sette (Bank of Italy) |
| Abstract: | Using customs and balance sheet data for Italy, we identify foreign-dependent products (FDPs) and quantify the effect of any disruptions to those products. Our framework allows us to assess how geoeconomic fragmentation affects value added at different levels of aggregation. Our baseline calibration suggests that a reduction in the imports of FDPs from high geopolitical risk countries would result in a 2 per cent drop in GDP, with sizable heterogeneity across firms, regions, and sectors. Our findings highlight that the short-term costs of supply disruptions for critical inputs can be substantial, especially when firms cannot easily substitute away from those products. |
| Keywords: | Geoeconomic fragmentation, international trade, imported inputs, global value chain |
| JEL: | F10 F14 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:opques:qef_819_23&r=rmg |
| By: | Vittoria La Serra (Bank of Italy); Emiliano Svezia (Bank of Italy) |
| Abstract: | Since 2016, insurance corporations have been reporting granular asset data in Solvency II templates on a quarterly basis. Assets are uniquely identified by codes that must be kept stable and consistent over time; nevertheless, due to reporting errors, unexpected changes in these codes may occur, leading to inconsistencies when compiling insurance statistics. The paper addresses this issue as a statistical matching problem and proposes a supervised classification approach to detect such anomalies. Test results show the potential benefits of machine learning techniques to data quality management processes, specifically of a selected random forest model for supervised binary classification, and the efficiency gains arising from automation. |
| Keywords: | insurance data, data quality management, record linkage, statistical matching, machine learning |
| JEL: | C18 C81 G22 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:opques:qef_821_23&r=rmg |
| By: | Loretta J. Mester |
| Abstract: | To conclude, a resilient financial system plays an important role in ensuring a strong economy. After the global financial crisis, steps were taken to shore up the resilience of the banking system. Systemically important banking institutions have higher capital and liquidity buffers and better risk-management systems than they did. The sound banking system was able to lend important support to households and businesses throughout the pandemic. But the financial system is dynamic, with new products, business models, and technologies being introduced, and the economic environment can change rapidly. Last year’s bank stress underscores the importance of not becoming complacent. We need to look holistically at the regulations, our methods of supervision, and our lender of last resort function to address the vulnerabilities that were revealed. This holistic approach should consider the interactions among various regulations, leaning toward simplification when possible. Recalibration should be informed by careful cost-benefit analyses. Identifying and addressing weaknesses will improve the underlying resilience of the financial system, so that we can continue to rely on it to provide its important services across the business and financial cycles and limit the need for government interventions. |
| Keywords: | Financial Resilience; Monetary policy; Financial supervision and regulation; Banking system; Silicon Valley Bank (SVB) |
| Date: | 2024–02–29 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcsp:97883&r=rmg |
| By: | Jarrod Burgh; Emerson Melo |
| Abstract: | We present a model elucidating wishful thinking, which comprehensively incorporates both the costs and benefits associated with biased beliefs. Our findings reveal that wishful thinking behavior can be accurately characterized as equivalent to superquantile-utility maximization within the domain of threshold beliefs distortion cost functions. By leveraging this equivalence, we establish conditions that elucidate when an optimistic decision-maker exhibits a preference for choices characterized by positive skewness and increased risk. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.01892&r=rmg |
| By: | Tao Ren; Ruihan Zhou; Jinyang Jiang; Jiafeng Liang; Qinghao Wang; Yijie Peng |
| Abstract: | The formulaic alphas are mathematical formulas that transform raw stock data into indicated signals. In the industry, a collection of formulaic alphas is combined to enhance modeling accuracy. Existing alpha mining only employs the neural network agent, unable to utilize the structural information of the solution space. Moreover, they didn't consider the correlation between alphas in the collection, which limits the synergistic performance. To address these problems, we propose a novel alpha mining framework, which formulates the alpha mining problems as a reward-dense Markov Decision Process (MDP) and solves the MDP by the risk-seeking Monte Carlo Tree Search (MCTS). The MCTS-based agent fully exploits the structural information of discrete solution space and the risk-seeking policy explicitly optimizes the best-case performance rather than average outcomes. Comprehensive experiments are conducted to demonstrate the efficiency of our framework. Our method outperforms all state-of-the-art benchmarks on two real-world stock sets under various metrics. Backtest experiments show that our alphas achieve the most profitable results under a realistic trading setting. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.07080&r=rmg |
| By: | Taiyo Yoshimi; Uraku Yoshimoto; Kiyotaka Sato; Takatoshi Ito; Junko Shimizu; Yushi Yoshida |
| Abstract: | This study empirically investigates how the invoice currency choice differs between intra-firm and arm’s-length exports. We also examine whether other firm- and product-level characteristics affect the choice of invoice currency. This study is the first to be granted access to highly disaggregated transaction-level trade data for Japan. Focusing on Japanese automobile exports to France, we demonstrate that the importer’s currency tends to be chosen in intra-firm export invoicing based on a panel logit estimation. Our empirical findings remain robust when different types of intra-firm export variables and other conventional explanatory variables are introduced, such as firm and product market share, exchange rate volatility, euro-invoiced imports, labor productivity, and research and development intensity. Given growing intra-firm trade and expanding global value chains, Japanese parent firms tend to invoice in the importers’ currency, assuming the foreign exchange risk that arises from intra-firm trade. Thus, exchange rate risk management is a significant consideration for Japanese parent firms. |
| JEL: | F14 F30 F31 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32142&r=rmg |
| By: | Hüser, Anne-Caroline; Lepore, Caterina; Veraart, Luitgard A. M. |
| Abstract: | We examine how the repo market operates during liquidity stress by applying network analysis to novel transaction-level data of the overnight gilt repo market including the COVID-19 crisis. We find that during this crisis the repo network becomes more connected, with most institutions relying on previously used counterparties. There are however important changes in the repo volumes and spreads during the stress relative to normal times. There is a significant increase in volumes traded with the central counterparties (CCPs) sector. At the same time non-banks, except hedge funds, decrease borrowing and face higher spreads in the bilateral segment. Overall, this evidence reflects a preference for dealers and banks to transact in the centrally cleared rather than the bilateral segment. Our results can inform the policy debate around the behaviour of banks and non-banks in recent liquidity stress and on widening participation in CCPs by non-banks. |
| Keywords: | repo market; liquidity risk; financial networks; non-banks; Covid-19; coronavirus |
| JEL: | G10 G33 |
| Date: | 2024–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:121347&r=rmg |