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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.

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

Many firms use forced rating systems in which supervisors must evaluate employees according to a predefined distribution. We develop new theory suggesting that forced ratings are less likely to enhance performance when supervisors assess subjective dimensions of employee performance (e.g., creativity), but can have some harmful side effects. In a laboratory experiment, employees work on a creative task, and supervisors rate their performance. We do not find any difference in the employees’ performance or effort in a creative task setting between forced and free ratings. We do, however, find that forced ratings create higher stress for employees (ex post stress scales and biomarkers). Higher stress in turn mitigates the positive effect of effort on creativity. Furthermore, we find that actual creativity explains less of supervisors’ ratings of employees’ performance under forced ratings. Instead, factors that are unrelated to actual creativity, such as eloquent writing and strategic gaming behavior, matter more. Results of an additional online experiment confirm that forced ratings work differently in tasks where performance needs to be evaluated subjectively compared to tasks where objective measures are available.

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

Sports contexts are increasingly used in management research to test and develop theory and explore managerially relevant phenomena. This growth in publications is likely driven by a series of advantages that sports data offers to management researchers. However, such positive features are not a panacea, as several drawbacks are also associated with leveraging sports data, which can limit their usefulness for management scholars. In this paper, we aim to provide management researchers guidance to leverage the advantages and avoid the drawbacks of leveraging sports contexts. To do so, we identify and review 249 papers published over the last 50 years that used sports data to advance management theories and shed light on managerial phenomena. After outlining how these works contributed to the growth of several key conversations in management research, we discuss the advantages of using sports data by outlining how they can advance management research both conceptually (e.g., theory building and radical theorizing) and empirically (e.g., triangulation and replication). We then discuss the potential drawbacks of research using sports data and suggest ways to compensate for them. We close by outlining several new directions in which scholars can leverage sports data to further advance management research.

First published online: 19 June 2021 The past decade has profoundly reshaped the fiscal governance system of the Eurozone. Supranational prerogatives vis-à-vis State budgets have been significantly expanded, thereby redefining the nature of Union action in the field of fiscal policy and transforming the dynamics between the Union and its Member States. In spite of its overhaul and the practical effects that Eurozone fiscal governance now produces on the ground, the paper shows that overall, this regulatory system still formally qualifies as soft law. This results in a deep disconnect between the form and substance of Eurozone fiscal surveillance in the Eurozone, which raises a number of constitutional challenges. The paper shows that the source of this disconnect is to be found in the strict apprehension of the hard law/soft law divide and the narrow understanding of bindingness attached to it, which currently prevails in the legal discipline, but no longer corresponds to the realities of the EU’s regulatory practice. From there on, the paper offers an alternative approach towards the distinction between hard and soft law, based on a renewed, more open and contextual, understanding of the concepts of bindingness and legal effects, which might reconcile the form and the reality of Eurozone fiscal governance nowadays. This article was published Open Access with the support from the EUI Library through the CRUI - Springer Transformative Agreement (2020-2024)

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.

As human resource development (HRD) research and practice has flourished, researchers have called for new theoretical perspectives that might be brought to bear on increasing the effectiveness of HRD initiatives (e.g. Nolan & Garavan, 2016). In this chapter, we suggest how optimal distinctiveness theory (Brewer, 1991) could be applied to HRD. Optimal distinctiveness theory is a social psychological theory with roots in social identity theory that addresses individuals’ propensity to identify with groups. We present the results of an empirical study involving members of organisational work groups and discuss how HRD interventions could benefit from considering how individuals’ identification with a work group can be affected by optimal distinctiveness and the composition of the work group (age and functional backgrounds represented in the group). Based on these findings, we impart practical advice about composing work groups, including ensuring that young group members and those with few identities are placed in groups in which they share commonalities with other members and also maintain some distinctiveness from members. In addition, we discuss how the tenets of optimal distinctiveness theory have both research and practical implications for HRD.

As human resource development (HRD) research and practice has flourished, researchers have called for new theoretical perspectives that might be brought to bear on increasing the effectiveness of HRD initiatives (e.g. Nolan & Garavan, 2016). In this chapter, we suggest how optimal distinctiveness theory (Brewer, 1991) could be applied to HRD. Optimal distinctiveness theory is a social psychological theory with roots in social identity theory that addresses individuals’ propensity to identify with groups. We present the results of an empirical study involving members of organisational work groups and discuss how HRD interventions could benefit from considering how individuals’ identification with a work group can be affected by optimal distinctiveness and the composition of the work group (age and functional backgrounds represented in the group). Based on these findings, we impart practical advice about composing work groups, including ensuring that young group members and those with few identities are placed in groups in which they share commonalities with other members and also maintain some distinctiveness from members. In addition, we discuss how the tenets of optimal distinctiveness theory have both research and practical implications for HRD.

PurposeThe purpose of this study is to investigate the association between certain audit committee characteristics like independence and financial expertise with financial reporting quality (FRQ) of the firms listed on the Tehran Stock Exchange (TSE).Design/methodology/approachThe sample includes the 558 firm-year observations from companies listed on the TSE during the years 2012–2017, and the study’s hypotheses were tested using multivariate regression model based on panel data.FindingsThe authors find that audit committee independence has no significant effect on corporate FRQ, whereas audit committee's financial expertise significantly improves firms' FRQ. In other words, higher financial expertise of an audit committee can lead to an increase in its FRQ. The findings of the study are robust to alternate measures of FRQ, individual analysis of the research hypotheses for each year and endogeneity problem.Originality/valueTo the best of the authors’ knowledge, this is the first study to analyze the association between audit committee characteristics and FRQ in emerging capital markets, and so, the findings of the study not only extend the extant theoretical literature concerning the audit committee in developing countries including emerging capital market of Iran but also help investors, managers, capital market regulators, policymakers and audit profession regulators to make informed decisions.

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.

We test the hypothesis that security analysts discipline credit rating agencies. After all, analyst reports about a firm’s equity would no doubt be informative about its debt default probability and calibrate its credit ratings. We follow Hong and Kacperczyk (2010) in using brokerage house mergers, which eliminate redundant analysts, to shock analyst following so as to identify the causal effect of coverage on credit ratings. We find that a drop in one analyst covering increases the subsequent ratings of a company by around a significant halfrating notch. This effect is coming largely from firms with low initial analyst coverage and hence where the loss of one analyst is a sizeable percentage drop in market discipline. Our effect is stronger for firms close to default and hence where firm debt trades more like equity. The higher ratings due to fewer analysts following also result in lower bond yields.

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.

We outline a simple and robust methodology to align portfolios with a science-based, carbon budget consistent with maintaining a temperature rise below 1.5 °C with 83% probability. We show how to keep the tracking error at a negligible level. This approach works for both passive and active managers. It also establishes an exit roadmap for carbon-intensive corporates, thereby generating a form of competition to decarbonize within each sector. We also discuss four sources of risks: uncertainty around a rapidly shrinking carbon budget, time impacts on decarbonization rates, implementation risk due to market-wide selling pressure, and uncertainty about taxes on polluting companies.

International audience; Between 2010 and 2012 and with bank stability as the ultimate target, five European countries implemented a tax levy on banks’ liabilities thereby decreasing the cost of equity relative to the cost of debt. Using a difference-in-differences approach we assess the impact of this tax levy on banks’ participation in the syndicated loan market. We further investigate the impact of the tax levy along bank size and capital structure. We find that banks located in countries where the tax levy was implemented supply more credit. This increase is more significant for larger lenders and banks that are more capital constrained.

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).

Scholars and practitioners acknowledge the role of design, and specifically design thinking, as a driver of innovation and change. Design thinking is gaining attention in the business community beyond the traditional product innovation realm and is increasingly promoted as an engine for the creation of novel user experiences, new businesses, strategic transformation, organizational and cultural change. Is it reasonable to assume that the same set of practices fits such a broad range of applications equally well? This study addresses how design thinking applications are differently framed when addressing diverse innovation purposes. Specifically, we compare two purposes: innovation of solutions, encompassing traditional product and service development projects, and innovation of direction, encompassing strategic and organizational renewal projects. Based on data collected from 146 design thinking projects conducted by European consulting firms we investigate the relationships between the design thinking practices adopted and the value generated by the projects. We then analyze how these relationships vary depending on the purpose of the innovation project, namely whether focused on innovating solutions or direction. The results show that different purposes indeed call for different practices. In projects aimed at innovating solutions, market value is positively related to capturing current user needs and envisioning future society. Conversely, in projects aimed at innovating direction, market value is positively related to challenging current assumptions.

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.

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 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.

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

Purpose This study aims to analyze the presence of a long-term relationship between precious metal prices, such as for gold, silver, platinum and palladium, and Islamic stock markets of 32 different countries – 21 developed and 11 developing. In this study, the long-term relationship between the precious metal prices and the Islamic stock markets of countries grouped by the Morgan Stanley Capital Index (MSCI) according to their level of development was examined. In the economies included in the study, it is necessary that the Islamic stock index be created by MSCI. It is not a constraint as a Muslim country. Design/methodology/approach This study used the Pedroni panel cointegration analysis and full modified ordinary least square method. All analyses in this study were performed using monthly data from 2002 to 2015. Findings According to the Pedroni panel cointegration analysis applied in this study, all four precious metals – gold, silver, platinum and palladium – are effective portfolio diversification tools for developed Islamic stock markets within the analyzed period. However, in developing countries, although gold and palladium are effective portfolio diversification tools, silver and platinum are not. Practical implications These results provide practical implications for academicians, practitioners as portfolio managers, policymakers. These implications are related in portfolio risk management, the diversification benefits and to propose new investment tools among developed and developing Islamic markets. Social implications This study is important for investors who assemble portfolios under the restriction of selecting investment tools suitable for Islamic rules. These investors are important in terms of using precious metals that they prefer as an alternative to stock markets to protect against the risks related to their suitable portfolio options. Governments, individuals and institutional investors that use capital stock according to Islamic rules in developed countries can effectively use gold, silver, platinum and palladium as hedging tools. However, this choice is limited to only gold and palladium in developing country markets. Gold continues to be an effective diversification tool in Islamic share markets, as in traditional stock markets. Originality/value The author would like to emphasize that this article is second to examine precious metals and Islamic stock markets in literature.

This paper uses a novel approach to examine motives for geographic segment disclosure. It is generally assumed that firms prefer not to disclose disaggregated segment information for proprietary reasons, although there is mixed support for this assertion in empirical work. We develop an approach that uses the location characteristics of geographic segments to empirically identify reasons for withholding or disclosing segments. Using hand- collected segment data around a switch in reporting standards that forced firms to reveal more disaggregated segment information, we find that proprietary costs are the main determinant of geographic segment disclosure. We find that segments in wealthier areas and ranked better for business tend to be hidden, while higher entry barriers to a segment's location are positively related to the likelihood of a segment being disclosed. We also find that among previously unrevealed segments, proprietary costs also explain the non-disclosure of segment earnings and other information. In contrast, the attractiveness of a segment and entry barriers do not explain the amount of disclosed information for segments that were already disclosed prior to the switch in reporting standards. The findings suggest that proprietary, rather than agency costs, are a more important determinant of geographic segment disclosure.

Building on the social network and strategic entrepreneurship literature, we investigate the overall relationship between returnee entrepreneurs’ networks in different periods and locations, domestic resource acquisitions and firm performance. While the labor mobility literature emphasizes the “gone but not forgotten” networks in the prior location of migrants, other studies argue that returnees suffer from a lack of local networks. Our findings show that returnee entrepreneurs are different in the extent of their home country embeddedness while they are overseas, which indicates different degrees of enduring networks in the home countries. The effect of home country embeddedness improves the performance of returnee entrepreneurship via domestic resource acquisition, and this effect could be substituted by pre-overseas local ties and the presence of local top management team (TMT) members. This study extends returnee research by shedding light on the importance of network maintenance in determining whether the home country’s network endures or decays and by highlighting the interactions of ties in the different periods of pre-overseas, during overseas, and after return.

We propose a randomised version of the Heston model-a widely used stochastic volatility model in mathematical finance-assuming that the starting point of the variance process is a random variable. In such a system, we study the small- and large-time behaviours of the implied volatility, and show that the proposed randomisation generates a short-maturity smile much steeper (`with explosion') than in the standard Heston model, thereby palliating the deficiency of classical stochastic volatility models in short time. We precisely quantify the speed of explosion of the smile for short maturities in terms of the right tail of the initial distribution, and in particular show that an explosion rate of~tγ (γ∈[0,1/2]) for the squared implied volatility-as observed on market data-can be obtained by a suitable choice of randomisation. The proofs are based on large deviations techniques and the theory of regular variations.

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.