# Research articles for the 2019-06-10

arXiv

In an economy which could not accommodate the full employment of its labor force, it employs some labor but does not employ others. The bipartition of the labor force is random, and we characterize it by an axiom of equal employment opportunity. We value each employed individual by his or her marginal contribution to the production function; we also value each unemployed individual by the potential marginal contribution the person would make if the market hired the individual. We then use the aggregate individual value to distribute the net production to the unemployment welfare and the employment benefits. Using real-time balanced-budget rule as a constraint and policy stability as an objective, we derive a scientific formula which describes a fair, debt-free, and asymptotic risk-free tax rate for any given unemployment rate and national spending level. The tax rate minimizes the asymptotic mean, variance, semi-variance, and mean absolute deviation of the underlying posterior unemployment rate. The allocation rule stimulates employment and boosts productivity. Under some symmetry assumptions, we even find that an unemployed person should enjoy equivalent employment benefits, and the tax rate goes with this welfare equality. The tool employed is the cooperative game theory in which we assume many players. The players are randomly bipartitioned, and the payoff varies with the partition. One could apply the fair distribution rule and valuation approach to other profit-sharing or cost-sharing situations with these characteristics. This framework is open to alternative identification strategies and other forms of equal opportunity axiom.

arXiv

We propose a new framework to value employee stock options (ESOs) that captures multiple exercises of different quantities over time. We also model the ESO holder's job termination risk and incorporate its impact on the payoffs of both vested and unvested ESOs. Numerical methods based on Fourier transform and finite differences are developed and implemented to solve the associated systems of PDEs. In addition, we introduce a new valuation method based on maturity randomization that yields analytic formulae for vested and unvested ESO costs. We examine the cost impact of job termination risk, exercise intensity, and various contractual features.

arXiv

This paper discusses the sensitivity of the long-term expected utility of optimal portfolios for an investor with constant relative risk aversion. Under an incomplete market given by a factor model, we consider the utility maximization problem with long-time horizon. The main purpose is to find the long-term sensitivity, that is, the extent how much the optimal expected utility is affected in the long run for small changes of the underlying factor model. The factor model induces a specific eigenpair of an operator, and this eigenpair does not only characterize the long-term behavior of the optimal expected utility but also provides an explicit representation of the expected utility on a finite time horizon. We conclude that this eigenpair therefore determines the long-term sensitivity. As examples, explicit results for several market models such as the Kim--Omberg model for stochastic excess returns and the Heston stochastic volatility model are presented.

SSRN

This paper investigates what we can learn from the financial crisis about the link between accounting and financial stability. The picture that emerges ten years after the crisis is substantially different from the picture that dominated the accounting debate during and shortly after the crisis. Widespread claims about the role of fair-value (or mark-to-market) accounting in the crisis have been debunked. However, we identify several other core issues for the link between accounting and financial stability. Our analysis suggests that, going into the financial crisis, banksâ€™ disclosures about relevant risk exposures were relatively sparse. Such disclosures came later after major concerns about banksâ€™ exposures had arisen in markets. Similarly, banks delayed the recognition of loan losses. Banksâ€™ incentives seem to drive this evidence, suggesting that reporting discretion and enforcement deserve careful consideration. In addition, bank regulation through its interlinkage with financial accounting may have dampened banksâ€™ incentives for corrective actions. Our analysis illustrates that a number of serious challenges remain if accounting and financial reporting are to contribute to financial stability.

SSRN

The widely cited empirical relation between equity misvaluation and the choice of merger currency has been recently called into question in a substantive manner. How can it be that the academic community was misled by this spurious correlation for more than a decade? We investigate this question and show that, most likely, failing to account for the abolishment of pooling in 2001 is the culprit. We start by arguing that pooling was the accounting method of choice for highly-valued acquirers, and that failing to control for this regulatory incentive (and the eventual disappearance of that incentive) leads to invalid inference. We finally confirm these arguments with new empirical results: (i) the relation between acquirer valuation and the choice of mode of payment disappears in analysis of U.S. mergers with fresh data that post-date 2001; (ii) this relation also fails in examination of data from Europe and Australia, environments where pooling was either not allowed or almost never used; (iii) this relation is absent even in analyses of pre-2001 subsamples of U.S. mergers that did not use pooling. We conclude that the abolishment of pooling played the role of an endogenous omitted variable, leading to the erroneous interpretation of empirical evidence

SSRN

In recent years, increasing Environmental, Social and Governance (ESG) consciousness has necessitated the need to hold companies accountable for social consequences resulting from their activities. This paper focuses on analyzing the impact of ESG screening on financial performance of the companies. The analysis of stock returns and beta of selected NIFTY 50 companies conducted using Shapiro-Wilk test, Wilcoxon test and Paired Sample t test indicate that there is no impact of ESG screening on financial performance and risk profile of the companies.

arXiv

We study a single-period optimal transport problem on $\mathbb{R}^2$ with a covariance-type cost function $c(x,y) = (x_1-y_1)(x_2-y_2)$ and a backward martingale constraint. We show that a transport plan $\gamma$ is optimal if and only if there is a maximal monotone set $G$ that supports the $x$-marginal of $\gamma$ and such that $c(x,y) = \min_{z\in G}c(z,y)$ for every $(x,y)$ in the support of $\gamma$. We obtain sharp regularity conditions for the uniqueness of an optimal plan and for its representation in terms of a map. Our study is motivated by a variant of the classical Kyle model of insider trading from Rochet and Vila (1994).

arXiv

The multilevel Monte Carlo path simulation method introduced by Giles ({\it Operations Research}, 56(3):607-617, 2008) exploits strong convergence properties to improve the computational complexity by combining simulations with different levels of resolution. In this paper we analyse its efficiency when using the Milstein discretisation; this has an improved order of strong convergence compared to the standard Euler-Maruyama method, and it is proved that this leads to an improved order of convergence of the variance of the multilevel estimator. Numerical results are also given for basket options to illustrate the relevance of the analysis.

arXiv

We study risk-sharing equilibria with general convex costs on the agents' trading rates. For an infinite-horizon model with linear state dynamics and exogenous volatilities, the equilibrium returns mean-revert around their frictionless counterparts -- the deviation has Ornstein-Uhlenbeck dynamics for quadratic costs whereas it follows a doubly-reflected Brownian motion if costs are proportional. More general models with arbitrary state dynamics and endogenous volatilities lead to multidimensional systems of nonlinear, fully-coupled forward-backward SDEs. These fall outside the scope of known wellposedness results, but can be solved numerically using the simulation-based deep-learning approach of \cite{han.al.17}. In a calibration to time series of returns, bid-ask spreads, and trading volume, transaction costs substantially affect equilibrium asset prices. In contrast, the effects of different cost specifications are rather similar, justifying the use of quadratic costs as a proxy for other less tractable specifications.

arXiv

Many existing jobs are prone to automation, but since new technologies also create new jobs it is crucial to understand job transitions. Based on empirical data we construct an occupational mobility network where nodes are occupations and edges represent the likelihood of job transitions. To study the effects of automation we develop a labour market model. At the macro level our model reproduces the Beveridge curve. At the micro level we analyze occupation-specific unemployment in response to an automation-related reallocation of labour demand. The network structure plays an important role: workers in occupations with a similar automation level often face different outcomes, both in the short term and in the long term, due to the fact that some occupations offer little opportunity for transition. Our work underscores the importance of directing retraining schemes towards workers in occupations with limited transition possibilities.

SSRN

Change in control clauses are pervasive in loan contracts, yet their terms are not boilerplate. Examining 14,940 contracts, we document significant heterogeneity in the use and size of ownership caps, which limit borrowers' block size. Lenders set lower caps to mitigate risks arising from power contests among blocks, takeover threats, and coordination costs within the syndicate. Setting caps below 50\% is associated with a drop in firm value, but not in the cost of debt, consistent with exacerbated firm-manager agency costs. Finally, largest block size increases when these restrictions expire, shedding new light on ways creditors may influence corporate governance.

arXiv

For the degree corrected stochastic block model in the presence of arbitrary or even adversarial outliers, we develop a convex-optimization-based clustering algorithm that includes a penalization term depending on the positive deviation of a node from the expected number of edges to other inliers. We prove that under mild conditions, this method achieves exact recovery of the underlying clusters. Our synthetic experiments show that our algorithm performs well on heterogeneous networks, and in particular those with Pareto degree distributions, for which outliers have a broad range of possible degrees that may enhance their adversarial power. We also demonstrate that our method allows for recovery with significantly lower error rates compared to existing algorithms.

SSRN

We study the role of co-jumps in the interest rate futures markets. To disentangle continuous part of quadratic covariation from co-jumps, we localize the co-jumps precisely through wavelet coefficients and identify statistically significant ones. Using high frequency data about U.S. and European yield curves we quantify the effect of co-jumps on their correlation structure. Empirical findings reveal much stronger co-jumping behavior of the U.S. yield curves in comparison to the European one. Further, we connect co-jumping behavior to the monetary policy announcements, and study effect of 103 FOMC and 119 ECB announcements on the identified co-jumps during the period from January 2007 to December 2017.

arXiv

We apply the procedure of Lee et al. to the problem of performing inference on the signal noise ratio of the asset which displays maximum sample Sharpe ratio over a set of possibly correlated assets. We find a multivariate analogue of the commonly used approximate standard error of the Sharpe ratio to use in this conditional estimation procedure. We also consider the simple Bonferroni correction for multiple hypothesis testing, fixing it for the case of positive common correlation among assets.

Testing indicates the conditional inference procedure achieves nominal type I rate, and does not appear to suffer from non-normality of returns. The conditional estimation test has low power under the alternative where there is little spread in the signal noise ratios of the assets, and high power under the alternative where a single asset has high signal noise ratio.

RePEC

Credit default swaps (CDS) played an important role in the financial crisis of 2008. While CDS can be used to hedge risks, they can also be used for speculative purposes (as occurred during the financial crisis) and regulations have been proposed to limit such speculative use. Here, we provide the first controlled experiment analyzing the pricing of credit default swaps in a bond market subject to default risk. We further use the laboratory as a testbed to analyze CDS regulation. Our results show that the regulation achieves the goal of increasing the use of CDS for hedging purposes while reducing the use of CDS for speculation. This success does not come at the expense of lower bond IPO revenues and does not negatively affect CDS prices or bond prices in the secondary market.

SSRN

Analyzing unique data on credit granted to individuals who are majority owners of small firms, we detail how a bankâ€™s credit decisions affect the future income of accepted versus denied loan applicants. The bankâ€™s cutoff rule, which is based on the applicantsâ€™ credit scores, creates a sharp discontinuity in the decision to grant loans. We show that application acceptance increases recipientsâ€™ income five years later by more than 10% compared to denied applicants. The effect is more pronounced in low-income areas, during a crisis period, and when positive soft information contributes to loan approval. We thus quantify acutely unequal income developments following the bankâ€™s credit decisions across regions, time and information strata.

SSRN

Smart-beta providers exploit commonly known characteristics such as size, value, and momentum, while other financial institutions provide liquidity for smart-beta trades. We develop a game-theoretic model to study the effect of competition amongst smart-beta and liquidity providers on liquidity and profits. We characterize the equilibrium in closed form and calibrate it using empirical data. We find that a liquidity provider competing with a single smart-beta provider reduces liquidity by half compared to the centralized setting where a single financial institution both exploits the smart-beta characteristic and provides liquidity. However, competition amongst either smart-beta or liquidity providers restores liquidity almost to that in the centralized setting. But, competition amongst smart-beta providers transfers profits to liquidity providers and vice versa. Thus, a regulator concerned with the quality of smart-beta products available to retail investors should limit excessive competition amongst smart-beta providers.

arXiv

Recent progress in the field of artificial intelligence, machine learning and also in computer industry resulted in the ongoing boom of using these techniques as applied to solving complex tasks in both science and industry. Same is, of course, true for the financial industry and mathematical finance. In this paper we consider a classical problem of mathematical finance - calibration of option pricing models to market data, as it was recently drawn some attention of the financial society in the context of deep learning and artificial neural networks. We highlight some pitfalls in the existing approaches and propose resolutions that improve both performance and accuracy of calibration. We also address a problem of no-arbitrage pricing when using a trained neural net, that is currently ignored in the literature.

SSRN

This study investigates the determinants of banking stability in Nigeria. Banking stability is crucial for economic growth and financial development. This study uses aggregate outcomes rather than individual bank performance to analyze the determinants of banking stability in Nigeria. Using aggregate outcomes allows us to focus on the changes occurring in the banking industry as a whole. The findings reveal that bank efficiency, the size of nonperforming loans, regulatory capital ratios, greater financial depth and banking concentration are significant determinants of banking stability in Nigeria. The findings have implications. One implication of this study is that bank supervisors should intensify its effort in addressing the nonperforming loans, capital adequacy problems issues in Nigeria. Also, bank supervisors should ensure that policies designed to improve the workings of the financial system are complied with.

arXiv

We provide a framework for determining the centralities of agents in a broad family of random networks. Current understanding of network centrality is largely restricted to deterministic settings, but practitioners frequently use random network models to accommodate data limitations or prove asymptotic results. Our main theorems show that on large random networks, centrality measures are close to their expected values with high probability. We illustrate the economic consequences of these results by presenting three applications: (1) In network formation models based on community structure (called stochastic block models), we show network segregation and differences in community size produce inequality. Benefits from peer effects tend to accrue disproportionately to bigger and better-connected communities. (2) When link probabilities depend on geography, we can compute and compare the centralities of agents in different locations. (3) In models where connections depend on several independent characteristics, we give a formula that determines centralities 'characteristic-by-characteristic'. The basic techniques from these applications, which use the main theorems to reduce questions about random networks to deterministic calculations, extend to many network games.

SSRN

We propose that firms pay out excess cash as dividends and markets react to these payouts when excess cash conveys information about unexpected earnings. For firms with weak (strong) investment opportunities, earnings surprises consist of more (less) excess cash so boards change dividends rapidly (slowly), and dividend announcements communicate more (less) of the surprise. An inter-temporal application of our findings, that firms payout a higher percentage of earnings when investment opportunities are weaker, explains the variation in payout and valuation previously attributed to the â€˜dividend premium.â€™ Cross-sectional applications show earnings information, rather than a change in agency costs, explains market reactions to dividend changes. Even though market reactions are higher for firms with greater free cash flow problems, these firms do not fund dividend changes out of foregone investment. An analysis of press releases confirms managers predominantly discuss earnings, consistent with dividend changes communicating earnings news.

SSRN

This study empirically examines the spillover effect from US monetary policy to nineteen European economies using Markov-switching models. The results of the univariate Markov-switching models validate the presence of two distinct regimes for both US monetary policy and the stock markets. We find mixed results when applying the multivariate Markov-switching models. The results report a positive relationship between the US interest rate and developed stock markets except for the Finish, Swiss, Swedish and UK stock markets whereas our findings confirm a positive relationship with the developing stock markets except for the Slovenian and Ukraine stock markets. Importantly, the nature of this effect varies during the economic crisis period. This study also compares the spillover effect between Asian and European stock markets and concludes that the effect of US monetary policy varies from market to market, however, changes in US monetary policy have greater effects on developed markets.

arXiv

Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempts for extracting relevant features based on LOB data. In this paper, we address this problem by designing a new set of handcrafted features and performing an extensive experimental evaluation on both liquid and illiquid stocks. More specifically, we implement a new set of econometrical features that capture statistical properties of the underlying securities for the task of mid-price prediction. Moreover, we develop a new experimental protocol for online learning that treats the task as a multi-objective optimization problem and predicts i) the direction of the next price movement and ii) the number of order book events that occur until the change takes place. In order to predict the mid-price movement, the features are fed into nine different deep learning models based on multi-layer perceptrons (MLP), convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks. The performance of the proposed method is then evaluated on liquid and illiquid stocks, which are based on TotalView-ITCH US and Nordic stocks, respectively. For some stocks, results suggest that the correct choice of a feature set and a model can lead to the successful prediction of how long it takes to have a stock price movement.

SSRN

We investigate the forecasting ability of the most commonly used benchmarks in financial economics. We approach the main methodological caveats of probabilistic forecasts studies â€" small samples, limited models and non-holistic validations â€" by performing a comprehensive comparison of 15 predictive schemes during a time period of over 21 years. All densities are evaluated in terms of their statistical consistency, local accuracy and forecasting errors. Through the development of a new indicator, the Integrated Forecast Score (IFS), we show that risk-neutral densities outperform historical-based predictions in terms of information content. We find that the Variance Gamma model generates the highest out-of-sample likelihood of observed prices and the lowest predictive errors, whereas the ARCH-based GJR-FHS delivers the most consistent forecasts across the entire density range. In contrast, log-normal densities, the Heston model or the non-parametric Breeden-Litzenberger formula yield biased predictions and are rejected in statistical tests.

SSRN

Recent research has aimed to understand how people consider financial decisions because they have important consequences for well-being. Yet, existing research has largely failed to examine how attitudes and behaviors vary as a function of the specific financial product (e.g., debt type). We ask to what extent people differentiate between similarly categorized financial products (e.g., debt or investment) as a function of their terms (e.g., interest costs, expected returns), and whether such differentiation predicts financial health. Across four studies, we find not only that there are individual differences in attitudes toward similar financial products (e.g., two distinct loans), but also that the extent to which a consumer is averse to high-cost versus low-cost products predicts financial health. This relationship cannot be fully explained by financial literacy, numeracy, or intertemporal discounting. In addition, nudging people toward differentiating between financial products promotes decisions that are aligned with financial health.

arXiv

We consider a two-person trading game in continuous time whereby each player chooses a constant rebalancing rule $b$ that he must adhere to over $[0,t]$. If $V_t(b)$ denotes the final wealth of the rebalancing rule $b$, then Player 1 (the `numerator player') picks $b$ so as to maximize $\mathbb{E}[V_t(b)/V_t(c)]$, while Player 2 (the `denominator player') picks $c$ so as to minimize it. In the unique Nash equilibrium, both players use the continuous-time Kelly rule $b^*=c^*=\Sigma^{-1}(\mu-r\textbf{1})$, where $\Sigma$ is the covariance of instantaneous returns per unit time, $\mu$ is the drift vector of the stock market, and $\textbf{1}$ is a vector of ones. Thus, even over very short intervals of time $[0,t]$, the desire to perform well relative to other traders leads one to adopt the Kelly rule, which is ordinarily derived by maximizing the asymptotic exponential growth rate of wealth. Hence, we find agreement with Bell and Cover's (1988) result in discrete time.

SSRN

This paper explores the effects of shifts in interest rates on corporate leverage and default. We develop a dynamic model in which the relationship between firms and their outside financiers is affected by a moral hazard problem and entrepreneursâ€™ initial wealth is scarce. The endogenous link between leverage and default risk comes from the lower incentives of overindebted entrepreneurs to guarantee the survival of their firms. Firms start up with leverage typically higher than some state-contingent target leverage ratio, and adjust gradually to it through earnings retention. The dynamic response of leverage and default to cuts and rises in interest rates is both asymmetric (since it is easier to adjust to a higher target leverage than to a lower one) and heterogeneously distributed across firms (since interest rates affect the burden of outstanding leverage, which differs across firms). We find that both interest rate rises and interest rate cuts increase the aggregate default rate in the short-run. Instead, higher rates produce lower default rates in the longer run since they induce lower target leverage across all firms. These results help rationalize some of the empirical evidence regarding the so-called risk-taking channel of monetary policy.

arXiv

Supply chains lend themselves to blockchain technology, but certain challenges remain, especially around invoice financing. For example, the further a supplier is removed from the final consumer product, the more difficult it is to get their invoices financed. Moreover, for competitive reasons, retailers and manufacturers do not want to disclose their supply chains. However, upstream suppliers need to prove that they are part of a `stable' supply chain to get their invoices financed, which presents the upstream suppliers with huge, and often unsurmountable, obstacles to get the necessary finance to fulfil the next order, or to expand their business. Using a fictitious supply chain use case, which is based on a real world use case, we demonstrate how these challenges have the potential to be solved by combining more advanced and specialised blockchain technologies with other technologies such as Artificial Intelligence. We describe how atomic crosschain functionality can be utilised across private blockchains to retrieve the information required for an invoice financier to make informed decisions under uncertainty, and consider the effect this decision has on the overall stability of the supply chain.

arXiv

Market sectors play a key role in the efficient flow of capital through the modern Global economy. We analyze existing sectorization heuristics, and observe that the most popular - the GICS (which informs the S&P 500), and the NAICS (published by the U.S. Government) - are not entirely quantitatively driven, but rather appear to be highly subjective and rooted in dogma. Building on inferences from analysis of the capital structure irrelevance principle and the Modigliani-Miller theoretic universe conditions, we postulate that corporation fundamentals - particularly those components specific to the Modigliani-Miller universe conditions - would be optimal descriptors of the true economic domain of operation of a company. We generate a set of potential candidate learned sector universes by varying the linkage method of a hierarchical clustering algorithm, and the number of resulting sectors derived from the model (ranging from 5 to 19), resulting in a total of 60 candidate learned sector universes. We then introduce reIndexer, a backtest-driven sector universe evaluation research tool, to rank the candidate sector universes produced by our learned sector classification heuristic. This rank was utilized to identify the risk-adjusted return optimal learned sector universe as being the universe generated under CLINK (i.e. complete linkage), with 17 sectors. The optimal learned sector universe was tested against the benchmark GICS classification universe with reIndexer, outperforming on both absolute portfolio value, and risk-adjusted return over the backtest period. We conclude that our fundamentals-driven Learned Sector classification heuristic provides a superior risk-diversification profile than the status quo classification heuristic.

SSRN

This paper aims to examine the innovations introduced by Directive 2014/95/EU of the European Parliament and of the Council of 22 October 2014 and its transposition measures in Italy (considering the Legislative Decree No. 254 of 30 December 2016, and the recent regulation by the national Supervisory Authority) and in other European countries, as part of a wider research work on non-financial information statements (â€œNFSsâ€) and listed companies operating within the European markets. It is designed to verify the effectiveness of the tools offered, with the intent of developing a system which can (i) combine, also through the NFSs, long-term profitability, social justice, and environmental protection, and thus (ii) prevent risks to sustainability and (iii) increase the confidence of investors and consumers.The article is structured in several parts, striving to examining the European regulation, focusing on the NFS comparative and Italian scenario, by offering a descriptive and empirical analysis of the matter, as well as offering some systemic conclusions, in particular with reference to social interest and to the most suitable way to disclose such information.Ultimately, the paper is intended to provide the reader with a critical overview of the current non-financial information framework, as it applies at European and at Member State level. Nevertheless, in a forward-looking sense, this piece seeks to understand whether, and how, the issue of non-financial statements can actually (i) modify the actual corporate dialectic within companies required to disclose non-financial information; (ii) improve the accountability of such companies, as well as from the point of view of corporate social responsibility (â€œCSRâ€); and (iii) involve investors, primarily institutional ones, in the â€œlifeâ€ of those companies which are subject to the NFS regime.

SSRN

During the Great Recession, liquidity did not flow out of the banking sector but transferred internally. Deposits increased, but the volumes of all other short-term debt financing instruments except for T-Bills decreased. Commercial banks, which have stable funding sources from deposits, did not render liquidity backup to shadow banks but held the increased deposits as cash on hand. This paper uses deposits and financial commercial paper outstanding as proxies for commercial and shadow banking financing instruments because they are unique liabilities of commercial and shadow banks, respectively. I provide evidence that when liquidity falls in shadow banks, commercial banks experience funding inflows. In normal times, commercial banks render liquidity backup to shadow banks in the following weeks using the increased deposits. However, the dynamic correlation breaks down in crisis times.

SSRN

Using big data on the near-universe of US firms' job postings, we document measurable, negative effects of local personal income taxes on the level of education, experience, and professional skills demanded by firms when hiring workers (downskilling). Tax-induced downskilling is identified both at the county level and at individual firms' local branches. It is solely driven by changes in high-income earners' tax rates. Multi-state firms internally reassign their hiring of low- vs. high-quality workers according to local personal income tax movements. The effect is more pronounced in industries that rely less on skilled labor and on local resources in the production processes, yet mitigated in firms' headquarter states and states that account for a large fraction of sales. Critically, firms cut investment and exit the labor markets of states that increase personal taxes. Our findings point to a "brain-drain" in states with high personal income taxes, showing how those taxes influence the local demand for human capital and labor market composition.

arXiv

We introduce a computational framework for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the replicating martingale of a portfolio from a finite sample of its terminal cumulative cash flow. The learned replicating martingale is given in closed form thanks to a suitable choice of the kernel. We develop an asymptotic theory and prove convergence and a central limit theorem. We also derive finite sample error bounds and concentration inequalities. Numerical examples show good results for a relatively small training sample size.

arXiv

We study discretizations of polynomial processes using finite state Markov processes satisfying suitable moment matching conditions. The states of these Markov processes together with their transition probabilities can be interpreted as Markov cubature rules. The polynomial property allows us to study such rules using algebraic techniques. Markov cubature rules aid the tractability of path-dependent tasks such as American option pricing in models where the underlying factors are polynomial processes.

SSRN

Assessing liquidity in fixed-income markets is becoming very important in the cur-rent context of extremely low interest rates which, in general terms, is encouraging the acquisition of riskier and (potentially) less liquid assets. Although there is the perception that bond market liquidity could have worsened over the last years in international markets, none of the current studies has reached a clear conclusion. In this paper, we propose a liquidity synthetic indicator (LSI) on Spanish debt, applying the methodology that Broto and Lamas (2016) used for US markets. We compute six individual liquidity indicators that represent the elements that characterise a liquid market (tightness, resilience, depth and breadth). We use price and transaction-based indicators for government and corporate debt when data is available for the period 2005-2016. Our LSI shows several episodes of significant worsening in liquidity conditions, related to the Lehman Brothersâ€™ collapse and the European sovereign debt crisis. After a sizeable improvement of liquidity in 2013-2014, the liquidity indicator has deteriorated over the past months as a consequence of lower trading volumes. The current ultra-low interest rate environment and more capital demanding regulations could partially explain these results.

SSRN

We examine the effects of monetary policy on household self-assessed financial stress and durable consumption using panel data from eighteen annual waves of the British Household Panel Survey. For identification, we exploit random variation in household exposure to interest rates generated by the random timing of household interview dates with respect to policy rate changes. After accounting for household and month-year-of-interview fixed effects, we uncover significant heterogeneities in the way monetary policy affects household groups that differ in housing and saving status. In particular, an increase in the interest rate induces financial stress among mortgagors and renters, while it lessens financial stress of savers. We find symmetric effects on durable consumption, mainly driven by mortgagors with high debt burden or limited access to liquidity and younger renters who are prospective home buyers.

SSRN

This appendix presents additional tables for â€œThe Time-Varying Diversifiability of Corporate Foreign Exchange Exposure.â€

SSRN

The paper investigates the effectiveness of dividend-based macro-prudential rules in complementing capital requirements to promote bank soundness and sustained lending over the cycle. First, some evidence on bank dividends and earnings in the euro area is presented. When shocks hit their profits, banks adjust retained earnings to smooth dividends. This generates bank equity and credit supply volatility. Then, a DSGE model with key financial frictions and a banking sector is developed to assess the virtues of what shall be called dividend prudential targets. Welfare-maximizing dividend-based macroprudential rules are shown to have important proper-ties: (i) they are effective in smoothing the financial cycle by means of less volatile bank retained earnings, (ii) they induce welfare gains associated to a Basel III-type of capital regulation, (iii) they mainly operate through their cyclical component, en-suring that long-run dividend payouts remain unaffected, (iv) they are flexible enough so as to allow bank managers to optimally deviate from the target, and (v) they act as an insurance scheme for the real economy.

arXiv

We study dynamic optimal portfolio allocation for monotone mean--variance preferences in a general semimartingale model. Armed with new results in this area we revisit the work of Cui, Li, Wang and Zhu (2012, MAFI) and fully characterize the circumstances under which one can set aside a non-negative cash flow while simultaneously improving the mean--variance efficiency of the left-over wealth. The paper analyzes, for the first time, the monotone hull of the Sharpe ratio and highlights its relevance to the problem at hand.

SSRN

The newspaper industry has struggled in recent decades, as readers and marketing revenue have migrated to digital outlets. Facing these pressures, many US newspapers have downsized or closed completely. We examine changes in firm behavior after they experience a shock to the local newspaper industry. Compared to a sample of matched control firms, we find that following newspaper closures and large industry layoffs, nearby public companies react by (1) increasing voluntary disclosure, consistent with firms responding to an increased demand for their information, and (2) increasing dividend payouts, consistent with firms attempting to alleviate concerns about agency problems exacerbated by the loss of a local watchdog. Cross-sectional analyses confirm that these results are driven by geographically-concentrated firms that rely more heavily on local newspapers to create and disseminate information about them to the public. We match treatment and control firms on both firm and local economic characteristics to mitigate concerns that differences between the two groups are driven by changes between local economies. Finally, we observe no change in either the financial reporting quality or performance of treatment firms, relative to control firms, in the post period, suggesting that the changes we document to payout and disclosure policy are not driven by changes in firm performance or firmsâ€™ accounting choices.

SSRN

Madhavan (1992,The Journal of Finance, 47, 2, 607-641) recommends a temporary switch to a call auction rather than a trading halt in times of market stress. He predicts the call auction to aggregate information more efficiently and to facilitate the resumption of the continuous session. In this paper, we test the properties of the switching mechanism proposed by Madhavan using data from the Spanish Stock Exchange (SSE). The SSE implements rule-based call auctions to stabilize prices. Onthe positive side, we find there is price learning during the auction, and price reversals dominate price continuations after the auction. On the negative side, we conclude rule-based auctions do not calm the market and do not reduce information asymmetries, except for small-caps. Our findings suggest the switching mechanism performs better with thinly traded stocks.

arXiv

This paper investigates the effects of the launch of Bitcoin futures on the intraday volatility of Bitcoin. Based on one-minute price data collected from four cryptocurrency exchanges, we first examine the change in realized volatility after the introduction of Bitcoin futures to investigate their aggregate effects on the intraday volatility of Bitcoin. We then analyze the effects in more detail utilizing the discrete Fourier transform. We show that although the Bitcoin market became more volatile immediately after the introduction of Bitcoin futures, over time it has become more stable than it was before the introduction.

SSRN

This paper analyzes the conflict of interest that exists when a financial institution issues and places a financial asset with credit risk among retail investors. Four regulatory measures are presented and analyzed in order to improve retail investors protection. It is shown that in this type of issues the most effective regulatory measure is that the supervisor sets a price cap. A close approach to this measure would be that the supervisor asks for independent valuations of the financial assets to provide investors and the supervisor itself with a well-founded opinion about the price of the issue. Under this approach, at least a second best social optimum is achieved.

SSRN

This paper studies economic effects of the gender composition of corporate boards, employing a new and unique longitudinal dataset of virtually all Russian companies whose shares were traded on the national stock market between 1998 and 2014. Using multiple identification approaches, alternative measures of gender diversity, and several performance indicators, we find some evidence that companies with gender-diverse boards have higher market values and better profitability. These effects are particularly pronounced when firms appoint several women directors, which is consistent with the critical mass theory. The effects appear to be stronger in bad economic times or for firms experiencing economic difficulties. Overall, the Russian data lend some support to "the business case" for more women on corporate boards.

SSRN

In contrast to financial arbitrage, which causes prices of economically equivalent transactions to converge in the direction of one price, regulatory arbitrage does not lead to such price convergence. In contrast, regulatory arbitrage tends to produce two different prices for economically equivalent transactions that are subject to different regulatory costs: this is what I call the â€œlaw of two prices.â€ The key insight here is that regulatory costs can persist as a â€œwedgeâ€ between the prices of economically equivalent transactions that are subject to differing regulatory costs. Unlike the price gap that financial arbitrage reduces or eliminates, this regulatory cost wedge will persist as long as the relevant regulatory cost differential persists.The persistence of the regulatory arbitrage wedge raises important and interesting policy concerns that the literature has not previously addressed. Specifically, the analysis here suggests that scholars should no longer describe regulatory arbitrage as â€œperfectly legal.â€ Instead, the persistent gap between the prices of transactions subject to differential regulatory costs warrants a more nuanced approach to the analysis of regulatory arbitrage. With respect to the normative analysis of the efficiency and fairness of the regulatory arbitrage wedge, scholars should consider, among other factors, the intentions and expectations of the decisionmakers engaging in regulatory arbitrage to determine whether they reasonably believe certain transactions should receive favorable regulatory treatment. Scholars should consider the law of two prices when addressing questions related to regulatory arbitrage.

arXiv

In this paper, which is the third installment of the author's trilogy on margin loan pricing, we analyze $1,367$ monthly observations of the U.S. broker call money rate, which is the interest rate at which stock brokers can borrow to fund their margin loans to retail clients. We describe the basic features and mean-reverting behavior of this series and juxtapose the empirically-derived laws of motion with the author's prior theories of margin loan pricing (Garivaltis 2019a-b). This allows us to derive stochastic differential equations that govern the evolution of the margin loan interest rate and the leverage ratios of sophisticated brokerage clients (namely, continuous time Kelly gamblers). Finally, we apply Merton's (1974) arbitrage theory of corporate liability pricing to study theoretical constraints on the risk premia that could be generated in the market for call money. Apparently, if there is no arbitrage in the U.S. financial markets, the implication is that the total volume of call loans must constitute north of $70\%$ of the value of all leveraged portfolios.

SSRN

With special focus on the role and impact of forensic science in criminal investigation. It will also try to explore the causes as to why the role of forensic science in India is still at a very fundamental stage or prohibitory in character, although, due to a remarkable technological advancement in scientific era has been made in a last decade. There is a necessity that legal systems in India including its subsidiaries need to be reshaped towards the accomplishment of result oriented forensic investigation and speedy trials, so that remedy & justice to victims of heinous crimes may be provide.

arXiv

This paper supplies two possible resolutions of Fortune's (2000) margin-loan pricing puzzle. Fortune (2000) noted that the margin loan interest rates charged by stock brokers are very high in relation to the actual (low) credit risk and the cost of funds. If we live in the Black-Scholes world, the brokers are presumably making arbitrage profits by shorting dynamically precise amounts of their clients' portfolios. First, we extend Fortune's (2000) application of Merton's (1974) no-arbitrage approach to allow for brokers that can only revise their hedges finitely many times during the term of the loan. We show that extremely small differences in the revision frequency can easily explain the observed variation in margin loan pricing. In fact, four additional revisions per three-day period serve to explain all of the currently observed heterogeneity. Second, we study monopolistic (or oligopolistic) margin loan pricing by brokers whose clients are continuous-time Kelly gamblers. The broker solves a general stochastic control problem that yields simple and pleasant formulas for the optimal interest rate and the net interest margin. If the author owned a brokerage, he would charge an interest rate of $(r+\nu)/2-\sigma^2/4$, where $r$ is the cost of funds, $\nu$ is the compound-annual growth rate of the S&P 500 index, and $\sigma$ is the volatility.

SSRN

This paper proposes a market consistent valuation framework for variable annuities with guaranteed minimum accumulation benefit, death benefit and surrender benefit features. The setup is based on a hybrid model for the financial market and uses time-inhomogeneous LÃ©vy processes as risk drivers. Further, we allow for dependence between financial and surrender risks. Our model leads to explicit analytical formulas for the quantities of interest, and practical and efficient numerical procedures for the evaluation of these formulas. We illustrate the tractability of this approach by means of a detailed sensitivity analysis of the price of the variable annuity and its components with respect to the model parameters. The results highlight the role played by the surrender behaviour and the importance of its appropriate modelling.

SSRN

We examine the influence of financial asset historical price path characteristics on investorsâ€™ risk perception, return beliefs and investment decisions. To that end, we run a series of survey experiments in which we present various price patterns to actual and potential investors. Our findings reveal that price paths with identical daily and monthly returns (and consequently identical return standard deviation) can lead to substantially different risk perception by investors, indicating that historical volatility is not sufficient to explain risk perception. Salient features such as highs, lows and crashes are the most influential drivers of perceived risk in price paths. Return forecasts are primarily driven by past final returns and the most recent price trends. Perceived risk and return beliefs strongly predict investment decisions.

SSRN

We analyse the differences between the optimal portfolio of funds that a fully in-formed investor might select and the current structure of the mutual fund markets as characterized by the fundsâ€™ risk profile (conservative or aggressive) and target investor type (retail or wholesale). We find that the relationship between fund age, market share and change in total net assets â€" but not fees â€" and the optimal portfo-lio of funds depends on the structure of the mutual fund market.