Search

This paper investigates the impact of regional factors on Islamic and conventional stock returns in the member countries of the Gulf Cooperation Council (GCC) from April 2011 to April 2021. This paper employs the quantile regression method to determine the effect of regional factors on GCC markets during different market states, enabling a more detailed investigation of the structure and degree of dependence. Regional stock market returns and regional political risk are used to study the effect of regional factors. The findings of this study reveal that the reaction of market returns to regional factors is heterogeneous across the conditional distribution of the GCC\"s stock returns. More specifically, the results demonstrate that changes in regional factors, with respect to Islamic and conventional markets, have asymmetric effects on stock returns in the majority of the GCC markets. Except in Qatar, the regional geopolitical risk negatively affects the GCC\"s Islamic stock returns during bearish markets. Results for conventional stock returns have the same negative effect, yet only in extreme market states. As for the impact of the regional stock market, we find that the regional Islamic index has a positive impact across almost all quantiles with a stronger effect during bullish markets, except in Bahrain and Oman. As for conventional markets, we observe the same impact throughout the GCC except for in Saudi Arabia. Islamic and conventional markets\" responses to changes in regional factors have similar behavior. Therefore, we conclude that, despite fundamental differences, Islamic and conventional stock markets in the GCC perform comparably, implying that a potential portfolio diversification benefit is not achieved except in limited cases.

This article demonstrates that low bank capital carries a negative externality because it amplifies local shock spillovers. We exploit a natural disaster that is transmitted to firms in nondisaster areas via their banks. Firms connected to a strongly disaster-exposed bank with lowest-quartile capitalization significantly reduce their total borrowing by 6.6% and tangible assets by 6.9% compared to similar firms connected to a well-capitalized bank. These findings translate to negative regional effects on GDP and unemployment. Additionally, following a disaster event, banks reduce their exposure to currently unaffected but generally disaster-prone areas.

The report’s authors have offered necessary details on the latestBaby Toys markettrends and the crucial parameters impacting both short-term and long-term market growth. Its panoramic view of the Baby Toys market entails useful insights into the estimated Baby Toys market size, revenue share, and sales & distribution networks. Such helpful market insights are bound to help readers outline this industry’s key outcomes in the near future. The global baby toys market size is expected to reach USD 18.87 Billion at a revenue CAGR of 4.6% in 2030, according to latest analysis by Emergen Research. The market revenue growth can be attributed to the fact that toys manufactured for the use of infants and toddlers play an important role in the development of their thinking and decision-making skills at a very small age. As baby toys consist of various educational toys such as jigsaw puzzles, building sets, and others, which involve brainstorming activities making them capable enough to take initial decisions, especially for toddlers and preschoolers who are in their days to school and are away from parents. For this, manufacturers are indulging themselves in manufacturing innovative toys that are designed to stimulate learning in toddlers and preschoolers. Request Free Sample Copy (To Understand the Complete Structure of this Report [Summary + TOC]) @https://www.emergenresearch.com/request-sample/1318 Market Drivers: The constant efforts of prominent players to develop newer and modern technologies and product advancements are expected to drive the growth of the industry over the coming years. The report studies the extensive business expansion plans and advancements in R&D activities and product portfolio. The report offers a clear understanding of the alliances in the market, such as mergers and acquisitions, joint ventures, collaborations, partnerships, agreements, product launches and brand promotions, and corporate deals. Market Scope:The report explains the scope of various commercial possibilities in the global Baby Toys market over the upcoming years. The estimated revenue build-up over the forecast years has been included in the report. The report analyzes the key market segments and sub-segments and provides deep insights into the market to assist readers with the formulation of lucrative strategies for business expansion. Competitive Terrain: The global Baby Toys industry is highly consolidated owing to the presence of renowned companies operating across several international and local segments of the market. These players dominate the industry in terms of their strong geographical reach and a large number of production facilities. The companies are intensely competitive against one another and excel in their individual technological capabilities, as well as product development, innovation, and product pricing strategies. The leading market contenders listed in the report are: LEGO System A/S, Hasbro., Mattel, Spin Master, Bandai Namco Holdings Inc., Nintendo., Kids2 Inc., Tomy, Horst Brandstätter Group, BASIC FUN! INC., and FUNSKOOL The research study examines historic data from 2018 and 2020 to draw forecasts until 2028. The timeline makes the report an invaluable resource for readers, investors, and stakeholders looking for key insights in readily accessible documents with the information presented in the form of tables, charts, and graphs. Emergen Research is Offering Limited Time Discount (Grab a Copy at Discounted Price Now)@https://www.emergenresearch.com/request-discount/1318 Market Segmentations of the Baby Toys Market This market is segmented based on Types, Applications, and Regions. The growth of each segment provides accurate forecasts related to production and sales by Types and Applications, in terms of volume and value for the period between 2020 and 2028. This analysis can help readers looking to expand their business by targeting emerging and niche markets. Market share data is given on both global and regional levels. Regions covered in the report are North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. Research analysts assess the market positions of the leading competitors and provide competitive analysis for each company. For this study, this report segments the global Baby Toys market on the basis of product, application, and region: Segments Covered in this report are: Product Type Outlook (Revenue, USD Billion; 2019-2030) Soft Toys & Dolls Board Games Construction Toys Vehicles Musical Toys & Rattles Educational Toys Others Sales Channel Outlook (Revenue, USD Billion; 2019-2030) Hypermarkets/Supermarkets Convenience Stores Specialty Stores Online Retailing Regional Outlook (Revenue, USD Billion; 2019–2030) North America U.S. Canada Mexico Europe Germany France U.K. Italy Spain Benelux Rest of Europe Asia Pacific China India Japan South Korea Rest of APAC Latin America Brazil Rest of LATAM Middle East & Africa Saudi Arabia UAE South Africa Turkey Rest of MEA Browse Full Report Description + Research Methodology + Table of Content + Infographics@https://www.emergenresearch.com/industry-report/baby-toys-market Regional Outlook of the Baby Toys Market The global Baby Toys market has been categorized into several important geographical regions, including North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. In this section, authors of the report have studied the presence of the global Baby Toys market across major geographies. Moreover, the estimated market share, market size, revenue contribution, sales network and distribution channel, and other crucial elements of each regional segment have been detailed in the report. Key Points of the Geographical Analysis: Data and information related to the consumption rate in each region Estimated increase in the consumption rate Proposed growth of the market share of each region Geographical contribution to market revenue Expected growth rate of the regional markets Objectives of the Report: Industrial structure analysis of the Baby Toys market by identification of various sub-segments Extensive analysis of key market players along with their SWOT analysis Competitive landscape benchmarking Analysis of Baby Toys market based on growth trends, futuristic outlook, and contribution to the total growth of the market Analysis of drivers, constraints, opportunities, challenges, and risks in the global Baby Toys market Comprehensive analysis of competitive developments such as expansions, agreements, Table of Contents Executive Summary Research Methodology Market Overview Regional Landscape Global Market Analysis and Forecast by Types Global Market Analysis and Forecast by Applications Global Market Analysis and Forecast by Regions North America Market Analysis and Forecast Europe Market Analysis and Forecast Asia Pacific Market Analysis and Forecast Latin America Market Analysis and Forecast Middle East & Africa Market Analysis and Forecast Competitive Landscape In conclusion, the Baby Toys Market report is an exhaustive database that will help readers formulate lucrative strategies. The Baby Toys Market report studies the latest economic scenario with value, drivers, constraints, growth opportunities, challenges, demand and supply ratio, production capacity, import/export status, growth rate, and others. Additionally, the report also undertakes SWOT Analysis and Porter’s Five Forces Analysis to study the leading companies. Request Cumtomization as per your specific requirement@https://www.emergenresearch.com/request-history/1318 Latest Published Reports by Emergen Research: Maritime Autonomous Surface Ships Market https://www.emergenresearch.com/industry-report/maritime-autonomous-surface-ships-market Data Visualization Market https://www.emergenresearch.com/industry-report/data-visualization-market Dating Services Market https://www.emergenresearch.com/industry-report/dating-services-market Online Reading Platform Market https://www.emergenresearch.com/industry-report/online-reading-platform-market Aerogel Market https://www.emergenresearch.com/industry-report/aerogel-market Network Automation Market https://www.emergenresearch.com/industry-report/network-automation-market Drone Inspection And Monitoring Market https://www.emergenresearch.com/industry-report/drone-inspection-and-monitoring-market About Us: At Emergen Research, we believe in advancing with technology. We are growing market research and strategy consulting company with an exhaustive knowledge base of cutting-edge and potentially market-disrupting technologies that are predicted to become more prevalent in the coming decade. Contact Us: Eric Lee Corporate Sales Specialist Emergen Research | Web:www.emergenresearch.com Direct Line: +1 (604) 757-9756 E-mail:sales@emergenresearch.com Visit for More Insights:https://www.emergenresearch.com/insights Explore OurCustom Intelligence services|Growth Consulting Services Latest Reports:c5isr systems market|peritoneal dialysis market Trending Titles:ai in banking market|5g fixed wireless access market &nbsp

We study statistical aspects of state-dependent Hawkes processes, which are an extension of Hawkes processes where a self- and cross-exciting counting process and a state process are fully coupled, interacting with each other. The excitation kernel of the counting process depends on the state process that, reciprocally, switches state when there is an event in the counting process. We first establish the existence and uniqueness of state-dependent Hawkes processes and explain how they can be simulated. Then we develop maximum likelihood estimation methodology for parametric specifications of the process. We apply state-dependent Hawkes processes to high-frequency limit order book data, allowing us to build a novel model that captures the feedback loop between the order flow and the shape of the limit order book. We estimate two specifications of the model, using the bid-ask spread and the queue imbalance as state variables, and find that excitation effects in the order flow are strongly state-dependent. Additionally, we find that the endogeneity of the order flow, measured by the magnitude of excitation, is also state-dependent, being more pronounced in disequilibrium states of the limit order book., Comment: 33 pages, 13 figures, v3: added a comparison with other related models plus some other minor amendments, to appear in Quantitative Finance

We study whether carbon emissions affect the cross-section of US stock returns. We find that stocks of firms with higher total carbon dioxide emissions (and changes in emissions) earn higher returns, controlling for size, book-to-market, and other return predictors. We cannot explain this carbon premium through differences in unexpected profitability or other known risk factors. We also find that institutional investors implement exclusionary screening based on direct emission intensity (the ratio of total emissions to sales) in a few salient industries. Overall, our results are consistent with an interpretation that investors are already demanding compensation for their exposure to carbon emission risk.

We find dealer-level evidence that recent regulation on the leverage ratio requirement causes deviations from covered interest parity. Our analysis uses a unique data set of currency derivatives with disclosed counterparty identities together with exogenous variation introduced by the U.K. leverage ratio framework. Dealers who are affected by the regulatory shock charge an additional premium of about 20 basis points per annum for synthetic dollar funding relative to unaffected dealers. This finding holds even after controlling for changes in clients' demand. Also, some clients increase their trading activity with unaffected dealers with whom they already had a preexisting relationship.

On October 26, 2008, Porsche announced a largely unexpected domination plan for Volkswagen. The resulting short squeeze in Volkswagen’s stock briefly made it the most valuable listed company in the world. We argue that this was a manipulation designed to save Porsche from insolvency and the German laws against this kind of abuse were not effectively enforced. Using hand-collected data we provide the first rigorous academic study of the Porsche-VW squeeze and show that it significantly impeded market efficiency. Preventing manipulation is important because without efficient securities markets, the EU’s major project of the Capital Markets Union cannot be successful.

We show that lenders charge higher interest rates for mortgages on properties exposed to a greater risk of sea level rise (SLR). This SLR premium is not evident in short-term loans and is not related to borrowers’ short-term realized default or creditworthiness. Further, the SLR premium is smaller when the consequences of climate change are less salient and in areas with more climate change deniers. Overall, our results suggest that mortgage lenders view the risk of SLR as a long-term risk and that attention and beliefs are potential barriers through which SLR risk is priced in residential mortgage markets.

We calibrate several advanced stock price models to a time series of real market data of European options on the DAX. Via a Monte Carlo simulation, we price barrier down-and-out call options for all models and compare the modeled prices to given real market data of the barrier options. The Bates model reproduces barrier option prices very well. The BNS model overvalues and Levy models with stochastic time-change and leverage undervalue the exotic options. The Heston model and a local volatility model undervalue the barrier option prices by about 5–6%. A heuristic analysis suggests that the different degree of fluctuation of the random paths of the models are responsible of producing different prices for the barrier options. Higher margins or additional risks like liquidity, calibration or model risk might economically explain why many advanced models undervalue barrier options.

ispartof: JOURNAL OF MONETARY ECONOMICS vol:121 pages:15-18 status: published

In this paper, we examine the impact of including environmental, social and governance (ESG) criteria in the allocation of equity portfolios. We focus on the risk and return characteristics of the resulting ESG portfolios and investment strategies. Two specific measures are considered to quantify the ESG performance of a company; the ESG rating and the greenhouse gas (GHG) emission intensity. For both measures, we carry out empirical portfolio analyses with assets in either the STOXX Europe 600 or the Russell 1000 index. The ESG rating data analysis does not provide clear-cut evidence for enhanced performance of portfolios with either high or low ESG scores. We moreover illustrate that the choice of rating agency has a significant impact on the performance of the resulting ESG-constrained portfolios. Secondly, we study the impact of GHG emission reductions and increases. We show that emission reductions do not necessarily lead to increased risk or diminished returns, which gives confidence in a smooth transition towards the green economy pursued by the European Green Deal.

Economic theories posit conflicting hypotheses on how wealth inequality affects entrepre-neurial dynamism. We investigate the impact of wealth inequality on business dynamics by constructing local measures of household wealth inequality based on financial rents, home equity, and 1880 farmland. We then identify the effect of wealth inequality on entrepre-neurship by instrumenting it with land distribution under the 1862 Homestead Act. Wealth inequality decreases firm entry and exit, and the proportion of high-tech businesses across metropolitan statistical areas. Wealth inequality also lowers the supply of public goods, such as education. Growth in income per capita consequently lags.

Do credit ratings help enforce market discipline on banks? Analyzing a uniquely comprehensive data set consisting of 1,081 rating change announcements for 154 international financial institutions between January 2004 and December 2015, we find that rating downgrades for internal reasons, such as adverse changes in the operating performance or capital structure of banks, are associated with a significant credit default swap spread widening. However, this widening only occurs for banks that are not perceived as to be Too-Big-to-Fail (TBTF). Our findings question the reliability of credit ratings as a tool to discipline TBTF banks and suggest that regulatory monitoring should remain the main mechanism for disciplining these banks.

Small and micro-enterprises are usually majority-owned by entrepreneurs. Using a unique sample of loan applications from such firms, we study the role of owners’ gender in bank credit decisions and post-credit-decision firm outcomes. We find that, ceteris paribus, female entrepreneurs are more prudent loan applicants than are males because they are less likely to apply for credit or to default after loan origination. The relatively more aggressive behavior of male applicants pays off, however, in terms of higher average firm performance after loan origination.

The promise of contingent convertible capital securities (CoCos) as a ”bail-in” solution has been the subject of considerable theoretical analysis and debate, but little is known about their effects in practice. We undertake the first comprehensive empirical analysis of bank CoCo issues, a market segment that comprises over 730 instruments totaling $521 billion. Four main findings emerge: (1) the propensity to issue a CoCo is higher for larger and better capitalized banks; (2) CoCo issues result in a statistically significant decline in issuers’ CDS spread, indicating that they generate risk-reduction benefits and lower costs of debt (this is especially true for CoCos that convert into equity, have mechanical triggers, and are classified as Additional Tier 1 instruments); (3) CoCos with only discretionary triggers do not have a significant impact on CDS spreads; and (4) CoCo issues have no statistically significant impact on stock prices, except for principal write-down CoCos with a high trigger level, which have a positive effect.

We define the concept of good trade execution and we construct explicit adapted good trade execution strategies in the framework of linear temporary market impact. Good trade execution strategies are dynamic, in the sense that they react to the actual realisation of the traded asset price path over the trading period; this is paramount in volatile regimes, where price trajectories can considerably deviate from their expected value. Remarkably however, the implementation of our strategies does not require the full specification of an SDE evolution for the traded asset price, making them robust across different models. Moreover, rather than minimising the expected trading cost, good trade execution strategies minimise trading costs in a pathwise sense, a point of view not yet considered in the literature. The mathematical apparatus for such a pathwise minimisation hinges on certain random Young differential equations that correspond to the Euler-Lagrange equations of the classical Calculus of Variations. These Young differential equations characterise our good trade execution strategies in terms of an initial value problem that allows for easy implementations., 31 pages, 3 figures

We study the impact ofQuickpay, a federal reform that indefinitely acceleratedpayments to small business contractors of the U.S. government. We find a strong di-rect effect of the reform on employment growth at the firm-level. Importantly, how-ever, we also document substantial crowding out of non-treated firms’ employmentwithin local labor markets. While the overall net employment effect was positive,it was close to zero in tight labor markets – where crowding out was stronger. Ourresults highlight an important channel for alleviating financing constraints in smallfirms, but also emphasize the general-equilibrium effects of large-scale interventions,which can lead to lower aggregate outcomes depending on labor market conditions.

Asset returns incorporate new information via the effects of independent and possibly identically distributed random shocks. They may also incorporate long memory effects related to the concept of self‐similarity. The two approaches are here combined. In addition, methods are proposed for estimating the contribution of each component and evidence supporting the presence of both components in both the physical and risk‐neutral distributions is presented. Furthermore, it is shown that long‐horizon returns may be nonnormal when there is a self‐similar component. The presence of a self‐similar component also questions positive equity biases over the longer term.

We explore how large and small banks make funding decisions when system-wide bailouts are possible. We show that bank size, purely on strategic grounds, is a key determinant of banks’ leverage choices, even when bailout policies treat large and small banks symmetrically. Large banks leverage more than small banks because they internalize that their decisions directly affect bailout policies. In equilibrium, this effect is amplified by strategic spillovers to small banks since banks’ leverage choices are strategic complements. Overall, the presence of large banks makes bailouts more likely. The optimal regulation features size-dependent policies that disproportionately restrict large banks’ leverage.

Heterogeneity in the taxation of asset returns can create ownership clienteles. Using a simple model, we demonstrate that an important consequence of tax-induced ownership segmentation is to limit risk-sharing, creating regions of the aggregate demand curve for the asset that are "downward-sloping.'' As a result, the constraints of the ownership clientele impact the asset price response to variations in asset supply, and make the asset's price more sensitive to movements in idiosyncratic risk. We test these predictions on U.S. municipal bonds, where cross-state variation in state tax privilege results in different levels of in-state ownership. In states with high tax-induced ownership segmentation, we find greater susceptibility of municipal bond yields to supply variation and heightened sensitivity of muni yields to local political uncertainty.

Default risk significantly affects the corporate policies of a firm. We develop a model in which a limited liability entity subject to default at an exponential random time jointly sets its dividend policy and capital structure to maximize the expected lifetime utility from consumption of risk‐averse equity investors. We give a complete characterization of the solution to the singular stochastic control problem. The optimal policy involves paying dividends to keep the ratio of firm's equity value to investors' wealth below a critical threshold. Dividend payout acts as a precautionary channel to transfer wealth from the firm to investors for mitigation of losses in the event of default. Higher the default risk, more aggressively the firm leverages and pays dividends.

A classic approach to financial risk management is the use of scenario analysis to stress test portfolios. In the case of an S&P 500 options portfolio, for example, a scenario analysis might report a P&L of −$1m in the event the S&P 500 falls 5% and its implied volatility surface increases by 3 percentage points. But how accurate is this reported value of −$1m? Such a number is typically computed under the (implicit) assumption that all other risk factors are set to zero. But this assumption is generally not justified as it ignores the often substantial statistical dependence among the risk factors. In particular, the expected values of the non-stressed factors conditional on the values of the stressed factors are generally non-zero. Moreover, even if the non-stressed factors were set to their conditional expected values rather than zero, the reported P&L might still be inaccurate due to convexity effects, particularly in the case of derivatives portfolios. A further weakness of this standard approach to scenario analysis is that the reported P&L numbers are generally not back-tested so their accuracy is not subjected to any statistical tests. There are many reasons for this but perhaps the main one is that scenario analysis for derivatives portfolios is typically conducted without having a probabilistic model for the underlying dynamics of the risk factors under the physical measure P. In this paper we address these weaknesses by embedding the scenario analysis within a dynamic factor model for the underlying risk factors. Such an approach typically requires multivariate state-space models that can model the real-world behavior of financial markets where risk factors are often latent, and that are sufficiently tractable so that we can compute (or simulate from) the conditional distribution of unstressed risk factors. We demonstrate how this can be done for observable as well as latent risk factors in examples drawn from options and fixed income markets. We show how the two forms of scenario analysis can lead to dramatically different results particularly in the case of portfolios that have been designed to be neutral to a subset of the risk factors.

Supplemental material file: online appendix We show that negative interest rate policy (NIRP) has expansionary effects on credit supply through a portfolio rebalancing channel. By shifting down and flattening the yield curve, NIRP differs from rate cuts just above the zero-lower-bound and has effects similar to QE. For identification, we exploit ECB’s NIRP and the Italian credit register and, for external validity, European and U.S. datasets. NIRP affects more banks with higher ex-ante liquid assets, including net interbank positions. More exposed banks reduce liquid assets, expand credit supply, especially to financially-constrained firms, and cut loan rates, inducing firms to increase investment and the wage bill. This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 648398). Peydró also acknowledges financial support from the ECO2015-68182-P (MINECO/FEDER, UE) grant and the Spanish Ministry of Economy and Competitiveness, through the Severo Ochoa Programme for Centres of Excellence in R&D (SEV-2015-0563).

Supplemental material files: supporting information: online appendix; replication file Do financial crises radicalize voters? We study Germany's 1931 banking crisis, collecting new data on bank branches and firm-bank connections. Exploiting cross-sectional variation in precrisis exposure to the bank at the center of the crisis, we show that Nazi votes surged in locations more affected by its failure. Radicalization in response to the shock was exacerbated in cities with a history of anti-Semitism. After the Nazis seized power, both pogroms and deportations were more frequent in places affected by the banking crisis. Our results suggest an important synergy between financial distress and cultural predispositions, with far-reaching consequences. This project has received funding from the ERC (648398). Peydró also acknowledges financial support from the ECO2015-68182-P (MINECO/FEDER, EUE) grant and the Spanish Ministry of Economics and Competitiveness (SEV-2015-0563).

In this paper we carry out a cross-country analysis of the correlation risk premium. We examine the statistical properties of the implied and realized correlation in European equity markets and relate the resulting premium to US equity market correlation risk and a global correlation risk premium. We find evidence of strong co-movement of correlation risk premiums in European and US equity markets. Our results support the existence of a strong empirical relationship between the global correlation risk premium and international equity market option returns. We document the dependence of the correlation risk premium on macroeconomic uncertainty and related variables.

Nat Cat risks are not insurable by traditional insurance mainly because of producing highly correlated losses. The source of such correlation among buildings of a region subject to a natural hazard is discussed. A decomposition method is proposed to split Nat Cat risk into idiosyncratic (and hence insurable) risk and systematic risk (carrying the correlated part). It is explained that the systematic risk can be transferred to capital markets using a set of parametric CAT bonds. Premium calculation is presented for insuring the decomposed risk. Portfolio risk-return trade-off measures for investing on the parametric CAT bond are derived. Multi-regional and multi-hazard parametric CAT bonds are introduced to reduce the risk of the investment. The methodology is applied on a region with about 3000 residential buildings subject to flood hazards.

Governments around the world are tackling the COVID-19 pandemic with a mix of public health, fiscal, macroprudential, monetary, and/or market-based policies. We assess the impact of the pandemic in Europe on sovereign CDS spreads using an event study methodology. We find that a higher number of cases and deaths and public health containment responses significantly increase the uncertainty among investors in European government bonds. Other governmental policies magnify the effect in the short run as supply chains are disrupted. Moreover, an increased debt-to-GDP ratio significantly boosts the cumulative abnormal change of CDS spreads, which indicates that investors are concerned about countries that are too indebted and thus have a limited capacity to intervene and provide fiscal stimuli and emergency fiscal packages to businesses and households.

The concordance probability, also called the C-index, is a popular measure to capture the discriminatory ability of a predictive model. In this article, the definition of this measure is adapted to the specific needs of the frequency and severity model, typically used during the technical pricing of a non-life insurance product. For the frequency model, the need of two different groups is tackled by defining three new types of the concordance probability. Secondly, these adapted definitions deal with the concept of exposure, which is the duration of a policy or insurance contract. Frequency data typically have a large sample size and therefore we present two fast and accurate estimation procedures for big data. Their good performance is illustrated on two real-life datasets. Upon these examples, we also estimate the concordance probability developed for severity models.

The forecasting literature has presented overwhelming evidence that the aggregation of heterogeneous expectations leads to improvements in forecast accuracy; however, outperforming a simple equal weight- ing scheme has proved challenging. This paper proposes an aggregation scheme of subjective bond return expectations based on the historical accuracy of professional interest rate forecasters. Our aggregate belief proxy outperforms equal weight and median weight combinations and is comparable to statistical projections even if its dynamics are quite different. With this measure at hand, we study the relationship between quantities of risk and compensation for risk and demonstrate a strong link to subjective expectations even if this is difficult to detect using realised returns.

We study local loan conditions when, under external pressure, banks close branches. After the closure of nearby branches of their credit granting banks, firms that locally and hurriedly transfer to other banks receive an equivalent interest rate. However, and in stark contrast, where branch closures do not take place firms that purposely switch banks receive a 63 basis points discount. At the same time, the loan default rate for the (more expensive) transfer loans is on average a full percentage point lower than that for the (cheaper) switching loans. This suggests that firms that establish new relationships after their bank branch closes are “better” than regular switchers in terms of unobservable characteristics. Taken together, these findings provide evidence of losses for firms when banks close branches, even if local markets remain competitive.

Financial literacy education is being integrated into school curricula at an increasing frequency. However, the majority of teachers lack the required competencies and teacher self-efficacy to effectively teach financial topics. In this study, we evaluated whether participation in teacher design teams (TDTs) results in high-quality educational materials, encouragement of professional learning, and ultimately, enhanced teacher self-efficacy in the face of pending curriculum reform. We conducted an exploratory multiple-case study in Flanders, Belgium. Data were collected from two TDTs that developed materials aligning with the financial literacy learning standards. We observed the team meetings and conducted interviews with the participating teachers and the team coach. Our results suggest that participation in TDTs supports the three outcome variables that we examined. However, they also revealed that each outcome shows room for improvement. Furthermore, the data provided additional evidence for the importance of meeting several input and process factors that had been previously shown to be essential for effective TDT function.

We provide a unifying treatment of pathwise moderate deviations for models commonly used in financial applications, and for related integrated functionals. Suitable scaling enables us to transfer these results into small‐time, large‐time, and tail asymptotics for diffusions, as well as for option prices and realized variances. In passing, we highlight some intuitive relationships between moderate deviations rate functions and their large deviations counterparts; these turn out to be useful for numerical purposes, as large deviations rate functions are often difficult to compute.

A well-defined agent-based asset pricing model able to match the widely observed properties of financial time series is valuable for testing the implications of various biases associated with investors' behaviour. Extending one of the most successful models in capturing traders behaviour, we present a new behavioural agent-based asset pricing model. Specifically, we introduce a well-known behavioural bias in the model, loss aversion, and evaluate its implications. First, measuring how close the simulated time series are to its empirical counterparts, we show that the model with loss aversion better matches and explains the properties of real-world financial data, compared with the base model without the behavioural bias. Secondly, we assess the impact of different levels of loss aversion not only on the agents' switching mechanisms, but also on the properties of the time series generated by the model. We demonstrate how for different levels of the loss aversion parameter, the biased agents tend to be driven out of the market at different points in time. Since even the simplest strategies have been shown to survive the competition in an agent-based setting, we can link our findings with the behavioural finance literature, which states that investors' systematic biases lead to unexpected market behaviour, instabilities and systematic errors. Finally, we provide an in-depth analysis of the simulated time series and show the resulting dynamics replicate a rich set of the stylized facts including: absence of autocorrelation, heavy tails, volatility clustering and conditional heavy tails of returns, long memory of absolute returns, as well as volume–volatility relations, gain–loss asymmetry, power-law behaviour and long memory of volume.

We propose a methodology for estimating and testing beta-pricing models when a large number of assets is available for investment but the number of time-series observations is fixed. We first consider the case of correctly specified models with constant risk premia, and then extend our framework to deal with time-varying risk premia, potentially misspecified models, firm characteristics, and unbalanced panels. We show that our large cross-sectional framework poses a serious challenge to common empirical findings regarding the validity of beta-pricing models. In the context of pricing models with Fama-French factors, firm characteristics are found to explain a much larger proportion of variation in estimated expected returns than betas. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

We consider the pricing of American-type basket derivatives by numerically solving a partial differential equation (PDE). The curse of dimensionality inherent in basket derivative pricing is circumvented by using the theory of comonotonicity. We start with deriving a PDE for the European-type comonotonic basket derivative price, together with a unique self-financing hedging strategy. We show how to use the results for the comonotonic market to approximate American-type basket derivative prices for a basket with correlated stocks. Our methodology generates American basket option prices which are in line with the prices obtained via the standard Least-Square Monte-Carlo approach. Moreover, the numerical tests illustrate the performance of the proposed method in terms of computation time, and highlight some deficiencies of the standard LSM method. ispartof: Quantitative Finance vol:19 issue:10 pages:1689-1704 status: published

© 2019, EAJ Association. We investigate the construction of medical inflation indexes. Such indexes can be used to update premiums of lifelong health insurance contracts. We compare the accuracy of the medical indexes currently applied in Belgium for private health insurance contracts with product-specific experience-based indexes. The latter enable to better capture product-specific systematic deviations due to medical inflation, but their application might raise some practical problems. Therefore, we propose an alternative way to construct medical inflation indexes. Several numerical examples are used to compare the performance of the newly proposed indexes with the experience-based and the current Belgian approaches. The conclusion drawn from these numerical examples is that the newly proposed indexes provide good approximations for the product-specific experience-based indexes without having their practical limitations. ispartof: EUROPEAN ACTUARIAL JOURNAL vol:9 issue:1 pages:139-153 status: published

Identifying the effects of “flights to safety” on asset prices using pure time-series methods is difficult because crises are infrequent. We develop a new cross-sectional identification approach, motivated by the insight that investors may differ in their “preferred habitats” within a broad asset class. We apply the method to the question of whether foreign capital is responsible for residential real estate price movements in global cities such as London and New York, especially during crises. Using large data sets of housing transactions, we find that foreign risk strongly affects London house prices. The effects are long-lasting, and are associated with both safe-haven effects and immigration.

We establish a rigorous duality theory, under No Unbounded Profit with Bounded Risk, for an infinite horizon problem of optimal consumption in the presence of an income stream that can terminate randomly at an exponentially distributed time, independent of the asset prices. We thus close a duality gap encountered by Vellekoop and Davis in a version of this problem in a Black-Scholes market. Many of the classical tenets of duality theory hold, with the notable exception that marginal utility at zero initial wealth is finite. We use as dual variables a class of supermartingale deflators such that deflated wealth plus cumulative deflated consumption in excess of income is a supermartingale. We show that the space of discounted local martingale deflators is dense in our dual domain, so that the dual problem can also be expressed as an infimum over the discounted local martingale deflators. We characterise the optimal wealth process, showing that optimal deflated wealth is a potential decaying to zero, while deflated wealth plus cumulative deflated consumption over income is a uniformly integrable martingale at the optimum. We apply the analysis to the Vellekoop and Davis example and give a numerical solution., Major revision of earlier paper entitled "Duality and deep learning for optimal consumption with randomly terminating income". arXiv admin note: text overlap with arXiv:2006.04687, arXiv:2009.00972

Over recent years stricter EU capital requirements have resulted in constraining bank lending to SMEs. Alternative finance is expected to ease such constraints, but what would it be its impact on bank fragility? This paper examines whether alternative finance for Small and Medium Enterprises (SMEs) in the euro area would moderate bank fragility. We employ a bank profit model from which we derive a novel measure of bank fragility that is based on micro-foundations and is estimated in a single stage with Bayesian techniques. Controlling for many bank and firm specific variables, including bank capital adequacy ratios and volatility, we find that alternative finance overall strengthens bank stability, but that there is some variability in this impact over time and across countries. Interestingly, while higher bank capital adequacy ratios at times may even increase fragility, their interactions with alternative finance could help reduce it.

Building parable economies embedding econometricians, we view alternative estimators (Instrumental variables, fuzzy regression discontinuity, natural experiments, OLS, event studies) from the perspective of privately informed decision-makers, e.g., CFOs. Instrumental variable estimates can be misleading since randomization through observable instruments eliminates signal content arising from discretion. If the goal is informing discretionary decisions, rather than predicting outcomes after forced/mistaken actions, instrumentation is problematic, whereas OLS or event studies can be sufficient. The analysis shows that the utility of alternative estimators hinges upon often neglected assumptions about agent/econometrician information sets, as distinct from exclusion restrictions. We recommend parable economy estimation before real-world IV estimation.

Using household surveys and bank penetration data at the district-level in 2006 and 2009, this paper examines the impact of Equity Bank -- a leading private commercial bank focusing on microfinance -- on access to banking in Kenya. Unlike other commercial banks in Kenya, Equity Bank pursues distinct branching strategies that target underserved areas and less-privileged households. Equity Bank presence has a positive and significant impact on households'use of bank accounts and bank credit, especially for Kenyans with low income, no salaried job, and less education and those who do not own their own home. The findings are robust to using the district-level proportion of people speaking a minority language as an instrument for Equity Bank presence. It appears that Equity Bank's business model -- providing financial services to population segments typically ignored by traditional commercial banks and generating sustainable profits in the process -- can be a solution to the financial access problem that has hindered the development of inclusive financial sectors in many African countries.

We introduce a first theory of price impact in presence of an interest-rates term structure. We explain how one can formulate instantaneous and transient price impact on bonds with different maturities, including a cross price impact that is endogenous to the term structure. We connect the introduced impact to classic no-arbitrage theory for interest rate markets, showing that impact can be embedded in the pricing measure and that no-arbitrage can be preserved. We present pricing examples in presence of price impact and numerical examples of how impact changes the shape of the term structure. Finally, to show that our approach is applicable we solve an optimal execution problem in interest rate markets with the type of price impact we developed in the paper., 49 pages, 5 figures

Ouverture à la concurrence, développement des énergies renouvelables, volatilité des prix du pétrole, réchauffement climatique… Avec la généralisation de la prise de conscience des défis à relever, les questions énergétiques passionnent. Ces défis ne sont pas uniquement économiques mais sont aussi de vrais sujets de société. C’est sur les spécificités « du modèle énergétique français » et du rôle de l’État que se sont penchés, régulièrement durant trois ans, historiens, économistes et acteurs lors d’un séminaire organisé par le Comité pour l’histoire économique et financière de la France. En effet, s’il est bien un domaine industriel dans lequel l’État a été, depuis le début du XIXe siècle, tout à la fois législateur, opérateur, régulateur et financier, c’est celui de l’énergie. Véritable secteur moteur des différentes phases d’industrialisation, l’énergie (charbon, gaz, électricité, pétrole) s’est révélée stratégique tout en étant le champ privilégié de l’émergence de la notion de service public. Quels sont les différents rôles de l’État dans ce domaine ? Comment se sont élaborés, dans la longue durée, ces rapports complexes entre l’État et les opérateurs ? Quelle est la généalogie du modèle de la grande entreprise publique qui mettait son monopole au service de la nation ? Pourquoi l’énergie est-elle un secteur privilégié de l’action publique ? La trentaine de textes et de témoignages rassemblés dans ces actes retrace, analyse et permet de mieux comprendre les principaux mécanismes de cette histoire.

This paper explores the optimal consumption and investment behavior of an individual who derives utility from the ratio between his consumption and an endogenous habit. We obtain closed-form policies under general utility functionals and stochastic investment opportunities by developing a nontrivial linearization to the budget constraint. This enables us to explicitly characterize how habit formation affects the marginal propensity to consume and optimal stock–bond investments. We also show that in a setting that combines habit formation with Epstein–Zin utility, consumption no longer grows at unrealistically high rates at high ages and investments in risky assets decrease.

We estimate the export and import pass-through rates using product-level data from Turkey. We find that the Turkish lira (TRY) exchange rate changes are mostly passed on to TRY prices of exports and imports—and, therefore, modestly to their prices in trading partners' currencies. The rate of average pass-through to TRY prices is estimated at 89% for imported goods and 82% for exported goods, with no apparent lags in the impact. Pass-through estimates by sector show variation and are relatively low for food and agricultural products. We argue that the fine-grained product-level data enable us to estimate the pass-through rates with better reliability and precision than we could by using only aggregated time-series data. We also introduce a pooled equation to estimate the difference between the export and import pass-through rates—a potentially useful statistic—in a way that allows for statistical inference.

We use investment-level data to study performance persistence in venture capital (VC). Consistent with prior studies, we find that each additional initial public offering (IPO) among a VC firm’s first ten investments predicts as much as an 8% higher IPO rate on its subsequent investments, though this effect erodes with time. In exploring its sources, we document several additional facts: successful outcomes stem in large part from investing in the right places at the right times; VC firms do not persist in their ability to choose the right places and times to invest; but early success does lead to investing in later rounds and in larger syndicates. This pattern of results seems most consistent with the idea that initial success improves access to deal flow. That preferential access raises the quality of subsequent investments, perpetuating performance differences in initial investments.

The coronavirus pandemic has had huge impacts on the National Health Service (NHS). Patients suffering from the illness have placed unprecedented demands on acute care, particularly on Intensive Care Units (ICUs). This has led to an effort to dramatically increase the resources available to NHS hospitals in treating these patients, involving re‐organisation of hospital facilities, redeployment of existing staff and a drive to bring in recently retired and newly graduated staff to fight the pandemic. These increases in demand and changes to supply have had large knock‐on effects on the care provided to the wider population. This paper discusses likely implications for healthcare delivery in the short and medium term of the responses to the coronavirus pandemic, focusing primarily on the implications for non‐coronavirus patients. Patterns of past care suggest those most likely to be affected by these disruptions will be older individuals and those living in more deprived areas, potentially exacerbating pre‐existing health inequalities. Effects are likely to persist into the longer‐run, with particular challenges around recruitment and ongoing staff shortages. This article is protected by copyright. All rights reserved

We develop a modelling framework for estimating and predicting weighted network data. The edge weights in weighted networks often arise from aggregating some individual relationships between the nodes. Motivated by this, we introduce a modelling framework for weighted networks based on the compound Poisson distribution. To allow for heterogeneity between the nodes, we use a regression approach for the model parameters. We test the new modelling framework on two types of financial networks: a network of financial institutions in which the edge weights represent exposures from trading Credit Default Swaps and a network of countries in which the edge weights represent cross-border lending. The compound Poisson Gamma distributions with regression fit the data well in both situations. We illustrate how this modelling framework can be used for predicting unobserved edges and their weights in an only partially observed network. This is for example relevant for assessing systemic risk in financial networks.

Does democratization reduce the cost of credit? Using global syndicated loan data from 1984 to 2014, we find that democratization has a sizable negative effect on loan spreads: a 1-point increase in the zero-to-ten Polity IV index of democracy shaves at least 19 basis points off spreads, but likely more. Reversals to autocracy hike spreads more strongly. Our findings are robust to the comprehensive inclusion of relevant controls, to the instrumentation with regional waves of democratization, and to a battery of other sensitivity tests. We thus highlight the lower cost of loans as one relevant mechanism through which democratization can affect economic development.

We consider portfolio optimization problems with expected loss constraints under the physical measure (Formula presented.) and the risk neutral measure (Formula presented.), respectively. Using Merton's portfolio as a benchmark portfolio, the optimal terminal wealth of the (Formula presented.) -risk constraint problem can be easily replicated with the standard delta hedging strategy. Motivated by this, we consider the (Formula presented.) -strategy fulfilling the (Formula presented.) -risk constraint and compare its solution with the true optimal solution of the (Formula presented.) -risk constraint problem. We show the existence and uniqueness of the optimal solution to the (Formula presented.) -strategy fulfilling the (Formula presented.) -risk constraint, and provide a tractable evaluation method. The (Formula presented.) -strategy fulfilling the (Formula presented.) -risk constraint is not only easier to implement with standard forwards and puts on a benchmark portfolio than the (Formula presented.) -risk constraint problem, but also easier to solve than either of the (Formula presented.) - or (Formula presented.) -risk constraint problem. The numerical test shows that the difference of the values of the two strategies (the (Formula presented.) -strategy fulfilling the (Formula presented.) -risk constraint and the optimal strategy solving the (Formula presented.) -risk constraint problem) is reasonably small.