Research articles for the 2019-10-28
arXiv
Insurers record detailed information related to claims (e.g. the cause of the claim) and policies (e.g. the value of the insured risk) for pricing insurance contracts. However, this information is largely neglected when estimating the reserve for future liabilities originating from past exposures. We present a flexible, yet highly interpretable framework for including these claim and policy-specific covariates in a reserving model. Our framework focuses on three building blocks in the development process of a claim: the time to settlement, the number of payments and the size of each payment. We carefully choose a generalized linear model (GLM) to model each of these stochastic building blocks in discrete time. Since GLMs are applied in the pricing of insurance contracts, our project bridges the gap between pricing and reserving methodology. We propose model selection techniques for GLMs adapted for censored data to select the relevant covariates in these models and demonstrate how the selected covariates determine the granularity of our reserving model. At one extreme, including many covariates captures the heterogeneity in the development process of individual claims, while at the other extreme, including no covariates corresponds to specifying a model for data aggregated in two-dimensional contingency tables, similar to the run-off triangles traditionally used by reserving actuaries. The set of selected covariates then naturally determines the position the actuary should take in between those two extremes. We illustrate our method with case studies on real life insurance data sets. These case studies provide new insights in the covariates driving the development of claims and demonstrate the accuracy and robustness of the reserving methodology over time.
SSRN
If an entrepreneur files for bankruptcy under Chapter 7, (i) most of her debt is discharged, and (ii) only her non-exempt assets are liquidated. Entrepreneurs can undo this “insurance†by posting collateral. The opportunity cost of doing so is lower for safer entrepreneurs who face a lower probability of default. Accordingly, we show that under adverse selection, as exemption increases, collateral becomes a more effective sorting device. As a result, an entrepreneur’s decision to post collateral improves access to credit and reduces the cost of credit to a greater extent the larger the exemption is. Econometric tests using data from the US Survey of Small Business support our theory.
SSRN
Here this paper means for, to understand the connection between audit committee mechanisms on non -financial information reporting with particular to intellectual capital. In order to study the relationship, data are collected from BSE Sensex listed companies top with their market capitalization for the period of 2017-2018. The content analysis has been applied to calculate the intellectual capital disclosure rate for the selected companies and later, multiple regression has been applied to check the nexus among audit committee attributes with intellectual capital reporting. The study identified that there is a significant effect by audit committee size on intellectual capital disclosure and surprisingly seen an unexpected negative relationship among independence of audit committee with intellectual capital disclosure. So this study argues that function of the audit committee have to be extended to ensure the quality of reporting by including relevant non-financial information like intellectual capital.
SSRN
This paper adopts a business management perspective of studies to examine the distinctive factors of the adoption and diffusion of technological innovation in the economics of payments systems. Different theoretical and empirical perspectives analyse the process of adoption and diffusion of innovations in the banking industry. These different perspectives point out the complexity of the issue and the many implications on the shaping of the boundaries of a payment system. Although the existence of substantial research on payment systems and financial innovation in the literature, none has directly focused on the modularity properties of payment systems and the subsequent processes of network integration, adoption and diffusion of innovations. It leaves a gap that the paper aims to overcome.
SSRN
Consistent with the incentive implication of inside debt, I show that active equity mutual funds invest less in companies whose CEOs are awarded with higher debt-like compensation, whereas corporate bond mutual funds invest more in such companies. This finding persists after accounting for endogeneity: first, I use the first-time disclosure of inside debt following the 2007 SEC disclosure reform as a natural experiment; and second, I use state personal income tax rates as an instrument for CEOs' willingness to receive inside debt. Moreover, during the recent financial crisis, both equity and bond funds were more attracted to high-inside debt firms. Furthermore, the effect of inside debt on portfolio allocation increases as the interest of equity holders and debt holders diverge, by being close to default, having higher risk, suffering from debt overhang, and having low credit ratings. Lastly, I find that funds' investment in inside debt has performance implications: equity funds that underweight high-inside debt firms deliver positive alphas; in contrast, bond funds that overweight inside debt deliver higher alphas.
SSRN
We develop a novel methodology for studying the impact of the timing component of firms’ earnings announcements. Our methodology relies on quasi-exogenous variation attributable to the specific day-of-week on which a calendar month begins. We refer to the resulting variation in firms’ announcement timing as ‘calendar rotations,’ which we verify are uncorrelated with proxies for the news content of firms’ announcements. In applying our methodology, we show firms whose earnings announcements are moved forward by calendar rotations receive greater media coverage, heightened attention from investors, and increases in earnings announcement premia. Taken together, our study details a method for studying how the timing of information flows impacts outcomes of interest to financial economists, and provides evidence that the sequence of news shapes the behavior of informational intermediaries and the dynamics of market prices.
arXiv
When dealing with Heston's stochastic volatility model, the change of measure from the subjective measure P to the objective measure Q is usually investigated under the assumption that the Feller condition is satisfied. This paper closes this gap in the literature by deriving sufficient conditions for the existence of an equivalent (local) martingale measure in the Heston model when the Feller condition is violated. We also supplement the existing literature by the case of a finite lifetime of the Laplace transform of the integrated volatility process. Moreover, we deduce conditions for the stock price process in the Heston model being a true martingale, regardless if the Feller condition is satisfied or not.
arXiv
It is generally understood that a given one-dimensional diffusion may be transformed by Cameron-Martin-Girsanov measure change into another one-dimensional diffusion with the same volatility but a different drift. But to achieve this we have to know that the change-of-measure local martingale that we write down is a true martingale; we provide a complete characterization of when this happens. This is then used to discuss absence of arbitrage in a generalized Heston model including the case where the Feller condition for the volatility process is violated.
arXiv
Two markets should be considered isomorphic if they are financially indistinguishable. We define a notion of isomorphism for financial markets in both discrete and continuous time. We then seek to identify the distinct isomorphism classes, that is to classify markets.
We classify complete one-period markets. We define an invariant of continuous time complete markets which we call the absolute market price of risk. This invariant plays a role analogous to the curvature in Riemannian geometry. We classify markets when the absolute market price of risk is deterministic.
We show that, in general, markets with non-trivial automorphism groups admit mutual fund theorems. We prove a number of such theorems.
SSRN
By running a battery of incentivized and non-incentivized experiments with fund managers from four countries in the European Union, we investigate the impact of fund managers' cognitive skills and economic preferences on the dynamics of the mutual funds they manage. First, we find that fund managers' risk tolerance positively correlates with fund risk when accounting for fund benchmark, fund category, and other controls. Second, we show that fund managers' ambiguity tolerance positively correlates with the funds' tracking error from the benchmark. Finally, we report that cognitive skills do not explain fund performance in terms of excess returns. However, we do find that fund managers with high cognitive reflection abilities generate these returns at lower risk.
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.
SSRN
This paper studies why investors buy dividend-paying assets and how they time their consumption accordingly to anticipated income. We combine administrative bank data linking customers' categorized consumption transactions and income to detailed portfolio and trading data and survey responses on financial behavior. We find that private consumption is excessively sensitive to dividend income. Investors across wealth, income and age distributions increase spending precisely around dividend receipt. Importantly, we find that consumption responses are driven by financially sophisticated investors who select dividend portfolios, anticipate dividend income, and plan consumption accordingly. Our results contribute to the literature on a dividend clientele and provide evidence of 'planned' excess sensitivity.
SSRN
Among U.S. public firms, technological innovation is concentrated in a small set of large players, with innovation “leaders†having considerably lower systematic risk than “laggards.†To understand this fact, we build a winner-takes-all patent-race model and show that a firm’s expected return decreases in its innovation output and increases in that of its rivals. Using a comprehensive firm-level panel of information on patenting activity by fields of technology in 1950-2010, we find strong support for the model’s predictions. Our results highlight that strategic interactions among firms competing in innovation are an important determinant of risk and expected returns.
arXiv
We propose a novel probabilistic model to facilitate the learning of multivariate tail dependence of multiple financial assets. Our method allows one to construct from known random vectors, e.g., standard normal, sophisticated joint heavy-tailed random vectors featuring not only distinct marginal tail heaviness, but also flexible tail dependence structure. The novelty lies in that pairwise tail dependence between any two dimensions is modeled separately from their correlation, and can vary respectively according to its own parameter rather than the correlation parameter, which is an essential advantage over many commonly used methods such as multivariate $t$ or elliptical distribution. It is also intuitive to interpret, easy to track, and simple to sample comparing to the copula approach. We show its flexible tail dependence structure through simulation. Coupled with a GARCH model to eliminate serial dependence of each individual asset return series, we use this novel method to model and forecast multivariate conditional distribution of stock returns, and obtain notable performance improvements in multi-dimensional coverage tests. Besides, our empirical finding about the asymmetry of tails of the idiosyncratic component as well as the market component is interesting and worth to be well studied in the future.
arXiv
This study attempts to analyze patterns in cryptocurrency markets using a special type of deep neural networks, namely a convolutional autoencoder. The method extracts the dominant features of market behavior and classifies the 40 studied cryptocurrencies into several classes for twelve 6-month periods starting from 15th May 2013. Transitions from one class to another with time are related to the maturement of cryptocurrencies. In speculative cryptocurrency markets, these findings have potential implications for investment and trading strategies.
SSRN
We develop a new approach to specifying, solving, and estimating DSGE models with occasionally binding collateral constraints. The new specification of the collateral constraint that we propose assumes that the transition from the unconstrained to the constrained state is a stochastic function of the endogenous level of leverage. This specification results in an endogenous regime-switching model, which we solve with a new general perturbation method, highlighting the importance of using a second-order approximation. Next, using Bayesian full information methods, we estimate a well-known model with an occasionally binding constraint, with Mexican quarterly data since 1981, considering a comprehensive set of shocks. We find that the estimated model fits the data well, characterizing both their second moments and the 1995 Tequila crisis period, without imposing a negative correlation between the productivity and the interest rate process. It yields estimates of the regime probabilities that align well with the narrative of Mexico's financial crisis and business cycle history. We also find that the estimated parameters of the financial friction are tighter than previously assumed in the literature; a cocktail of shocks that co-move in a particularly averse manner, rather than a sequence of unusually large negative shocks, precede large sudden stops episodes like the Tequila crisis; expenditure and impatience shocks drove Mexico's economy into the Tequila crisis, while productivity and interest shocks prevailed during that particular episode.
arXiv
In this paper we propose a general framework for modeling an insurance liability cash flow in continuous time, by generalizing the reduced-form framework for credit risk and life insurance. In particular, we assume a nontrivial dependence structure between the reference filtration and the insurance internal filtration. We apply these results for pricing non-life insurance liabilities in hybrid financial and insurance markets, while taking into account the role of inflation under the benchmark approach. This framework offers at the same time a general and flexible structure, and explicit and treatable pricing formula.
SSRN
Regulators generally have tried to address the problems posed by the excessive risk-taking of Systemically Important Financial Institutions (SIFIs) by placing restrictions on the activities in which SIFIs engage. However, the complexity of these institutions makes such attempts necessarily imperfect. This article proposes to address the problem at its very source, which is the incentives that SIFI owners have to push for excessive risk-taking by managers. Building on the traditional rule of “double liabilityâ€, we propose to modify the current (general) rule limiting the liability of SIFI shareholders to the amount of their initial investments in such companies. We propose replacing the extant limited liability regime with a new system that imposes additional liability over and above what SIFI shareholders already have invested in a pre-set amount that varies with a SIFI’s centrality in the financial network. Our liability regime has a number of advantages. First, by increasing shareholder exposure to downside risk, it discourages excessive risk-taking. At the same time, by placing a clearly defined ceiling on shareholders’ total liability exposure, it will not obliterate shareholders’ incentives to invest in the first place. Second, the liability to which shareholders are exposed is carefully tailored to the level of systemic risk that their institution creates. Thus, our rule induces shareholders to account for the negative externality SIFIs can impose without unduly stifling such financial institutions’ role within the financial system and in the wider economy. Third, as the amount of liability is clearly defined ex ante using the rigorous tools of network theory, our rule minimizes the influence of interest groups and the impact of idiosyncratic government decisions. Last, as markets know in advance the amount of liability to which shareholders are exposed, our rule favors the creation of a vibrant insurance and derivative market so that the risk of SIFIs defaults can be allocated to those who can better bear it.
SSRN
This study elucidates the mixed gamble confronting family firms when considering a related firm acquisition. The socioemotional and financial wealth trade-off associated with related firm acquisitions as well as their long-term horizon turns family firms more likely to undertake a related acquisition than non-family firms, especially when they are performing above their aspiration level. Post-merger performance pattern confirm that family firms are able to create long-term value through these acquisitions and by doing so they surpass non-family firms. These findings stand in contrast to commonly used behavioural agency predictions, but can be reconciled with theory through a mixed gambles’ lens.
SSRN
This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We estimate a latent factor model by applying principal component analysis to an adjusted covariance matrix estimated from partially observed panel data. We derive the asymptotic distribution for the estimated factors, loadings and the imputed values under a general approximate factor model. The key application is to estimate counterfactual outcomes in causal inference from panel data. The unobserved control group is modeled as missing values, which are inferred from the latent factor model. The inferential theory for the imputed values allows us to test for individual treatment effects at any time. We apply our method to portfolio investment strategies and find that around 14% of their average returns are significantly reduced by the academic publication of these strategies.
SSRN
This paper studies bank loan pricing in the euro area over the period October 2008 to October 2014. This period was characterised by market fragmentation and extended liquidity provision by the ECB. For our analysis we develop a theoretical framework which accounts for the main financing risks faced by euro area banks during this challenging period. In our modelling framework risk aversion plays a critical role, and this is challenging for our empirical analysis. We handle this issue by means of a novel econometric approach whereby bank risk aversion is treated as an unobservable random effect. We find that banks that have access to money markets, and are less reliant on ECB financing, can finance at a lower cost, and can thus offer lower bank lending rates. We also find that banks with a lower cost of debt financing offer lower lending rates. Our results also show that after accounting for financing risks, credit risks, market power and bank business model, our empirical estimates of banks' risk aversion are still positively and significantly correlated with country specific effects, thus highlighting the fragmentation of retail banking along national lines within the euro area.
SSRN
One can earn well over 100% of the equity risk premium on macroeconomic announcement days identified by the prior literature. This is a robust phenomenon present across many other subsets of macroeconomic variables. We show how inadvertent sample selection along with the timing of macroeconomic announcements throughout the month produces this too-much-return puzzle. Looking at the entire distribution of macroeconomic variables eliminates this sample selection bias, while including day-of-the-month fixed effects controls for the announcement timing. We find that expected macroeconomic announcements as a whole are responsible for about half of the equity premium. This smaller premium earned over more days means Sharpe ratios are similar on announcement and non-announcement days. We also show that the fit of the CAPM on macroeconomic announcement days is not evidence that those days are special, but only a by-product of those days' high ex-post market returns.
arXiv
We study the multi-level order-flow imbalance (MLOFI), which is a vector quantity that measures the net flow of buy and sell orders at different price levels in a limit order book (LOB). Using a recent, high-quality data set for 6 liquid stocks on Nasdaq, we fit a simple, linear relationship between MLOFI and the contemporaneous change in mid-price. For all 6 stocks that we study, we find that the out-of-sample goodness-of-fit of the relationship improves with each additional price level that we include in the MLOFI vector. Our results underline how order-flow activity deep into the LOB can influence the price-formation process.
arXiv
We develop a dual-control method for approximating investment strategies in incomplete environments that emerge from the presence of trading constraints. Convex duality enables the approximate technology to generate lower and upper bounds on the optimal value function. The mechanism rests on closed-form expressions pertaining to the portfolio composition, from which we are able to derive the near-optimal asset allocation explicitly. In a real financial market, we illustrate the accuracy of our approximate method on a dual CRRA utility function that characterises the preferences of a finite-horizon investor. Negligible duality gaps and insignificant annual welfare losses substantiate accuracy of the technique.
SSRN
The study investigates the bank-specific & macroeconomic determinants of non-performing loans as well as its impact on profitability. It uses secondary data of 7 joint venture from the year 2006 to 2017 and employs a fixed effect panel model in estimating three different empirical equations. The bank-specific variables taken are capital adequacy, net interest margin, the size of banks measured by total assets & loan to deposit ratio. Similarly, the macroeconomic variables include GDP growth, inflation and loan concentration of the banking industry measured by the Herfindahl-Hirschman Index. A non-performing loan is taken as both the independent and dependent variable; firstly to find out its determinants and the variable that comes significant during the process of finding the determinants is taken as the variable that affects the profitability. The study finds the net interest margin and bank size as the determinants of the non-performing loan & suggest that net interest margin has a positive and significant effect while the bank size has a negative and significant relationship. However, the macroeconomic variables do not relate. Furthermore, when the net interest margin, bank size & non-performing loan are used as an independent variable, its significant effect is seen with the profitability. An insignificant relationship is seen with the return on equity in terms of only bank size. Three conclusions derived from this study are: firstly as the net interest margin rises for the banks so does the bankability to earn from the interest income which increases the profitability. Secondly, the increase in the non-performing loan erodes the interest income reducing the profitability & finally, as the asset size increases so do the bad management practices as there are huge operations to be handled by the bank, therefore hindering the profitability.
SSRN
I derive the objective function of a firm with heterogeneous shareholders. In contrast to Fisher separation theorem, I drop the price-taking assumption. Therefore, shareholders have no unanimous preferences for profit maximization. I allow shareholders to act strategically by omitting the conditional sincerity assumption and by accounting for possible correlation in their votes. I derive the exact form of the objective function and provide the equilibrium existence conditions. The resulting objective function can be approximated by a weighed sum of shareholders portfolios' profit. Shareholder groups with positive within group correlation carry greater weight.
SSRN
Sadorsky (2012) analyzes correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. In this paper, we replicate the original analysis of Sadorsky (2012), extend the sample period and present alternative models and new findings. The extended sample confirms the unconditional correlation estimates reported in the original paper but finds different conditional relationships and economically insignificant return and volatility spillovers consistent with efficient markets and economic theory. Our study highlights the importance of a theoretical framework to analyze correlations and spillovers and the value of simple econometric models over more complex models.
SSRN
Data has taken immense importance in the last years. Consider the amount of data that is being collected worldwide every day, industries are reshaping their activities into a data-driven business. The digital transformation of all industries, portent of the fourth industrial revolution, is creating a new kind of economy based on the datafication of almost any aspect of human social, political and economic activity as a result of the information generated by the numerous daily routines of digitally connected individuals and technology. The financial services industry is part of this trend. Embracing the digital revolution and creating the right foundations allow incumbent financial institutions to disrupt their own business model. Hence, financial institutions are creating new businesses within their existing structures that adapt and collaborate to meet the challenges of digital transformation and make better use, faster, of their enduring source of competitive advantage â€" their own customer insight. Open banking and banking as a service (BaaS) are emerging as new forms of intermediation in the financial system that portraits positive and negative externalities for the financial system. Both concepts â€" open banking and BaaS - refer to the use of open Application Programming Interfaces that enable third parties to build applications and services around a financial institution that exposes its data and/or its infrastructure. The use of these schemes represents a new form of intersection between data and finance, which is changing the way traditional products, services and customer experience traditionally work in the financial sector. This paper explains the open banking and BaaS foundations and what they exactly entail. It also explores the benefits and risks that this interaction between financial institutions and third parties portrait for the financial services industry and analyses from a comparative perspective the different approaches financial, data privacy and competition regulators have implemented to boost open banking phenom. This paper argues that the compulsory approach to open banking is not in all cases the best approach for capitalizing the benefits of open banking and managing its risks. Indeed, some regulators have proposed a compulsory approach to open banking regulations to increase competition in retail banking or in the payment systems. In opposition, this paper argues that open banking and BaaS models in the financial industry might lead to more concentration and these risks have been understated by financial regulators and competition authorities. Finally, we provide some policy recommendations regarding open banking regulations, such as: the same regulatory approach should not apply to all jurisdictions, regulators should encourage reciprocity, especially when choosing the compulsory approach, coordination among different regulatory authorities is needed on a national and international levels, risk-based regulation is a correct type of approach, and monetization of data should not be restricted for incumbents.
arXiv
This paper introduces an intermediary between conditional expectation and conditional sublinear expectation, called R-conditioning. The R-conditioning of a random-vector in $L^2$ is defined as the best $L^2$-estimate, given a $\sigma$-subalgebra and a degree of model uncertainty. When the random vector represents the payoff of derivative security in a complete financial market, its R-conditioning with respect to the risk-neutral measure is interpreted as its risk-averse value. The optimization problem defining the optimization R-conditioning is shown to be well-posed. We show that the R-conditioning operators can be used to approximate a large class of sublinear expectations to arbitrary precision. We then introduce a novel numerical algorithm for computing the R-conditioning. This algorithm is shown to be strongly convergent.
Implementations are used to compare the risk-averse value of a Vanilla option to its traditional risk-neutral value, within the Black-Scholes-Merton framework. Concrete connections to robust finance, sensitivity analysis, and high-dimensional estimation are all treated in this paper.
arXiv
With the recent rise of Machine Learning as a candidate to partially replace classic Financial Mathematics methodologies, we investigate the performances of both in solving the problem of dynamic portfolio optimization in continuous-time, finite-horizon setting for a portfolio of two assets that are intertwined.
In Financial Mathematics approach we model the asset prices not via the common approaches used in pairs trading such as a high correlation or cointegration, but with the cointelation model that aims to reconcile both short-term risk and long-term equilibrium. We maximize the overall P&L with Financial Mathematics approach that dynamically switches between a mean-variance optimal strategy and a power utility maximizing strategy. We use a stochastic control formulation of the problem of power utility maximization and solve numerically the resulting HJB equation with the Deep Galerkin method.
We turn to Machine Learning for the same P&L maximization problem and use clustering analysis to devise bands, combined with in-band optimization. Although this approach is model agnostic, results obtained with data simulated from the same cointelation model as FM give an edge to ML.
arXiv
We develop a framework for price-mediated contagion in financial systems where banks are forced to liquidate assets to satisfy a risk-weight based capital adequacy requirement. In constructing this modeling framework, we introduce a two-tier pricing structure: the volume weighted average price that is obtained by any bank liquidating assets and the terminal mark-to-market price used to account for all assets held at the end of the clearing process. We consider the case of multiple illiquid assets and develop conditions for the existence and uniqueness of clearing prices. We provide a closed-form representation for the sensitivity of these clearing prices to the system parameters, and use this result to quantify: (1) the cost of regulation faced by the system as a whole and the individual banks, and (2) the value of providing bailouts and bail-ins to consider when such notions are financially advisable. Numerical case studies are provided to study the application of this model to data.
arXiv
We develop a model of electoral selection and accountability in the presence of mainstream and alternative media outlets. In addition to standard high and low competence types, the incumbent may be an aspiring autocrat, who controls the mainstream media and will cause substantial harm if not removed from office. Alternative media can help voters identify and remove aspiring autocrats and can enable voters to focus on honest mainstream media assessments of incumbents' competence. But malicious alternative media that peddle false conspiracy theories about the incumbent and the mainstream media can induce voters to mistakenly remove nonautocratic incumbents, which in turn demotivates incumbent effort and undermines accountability. The alternative media is most dangerous when it is sufficiently credible that voters pay attention to it, but sufficiently likely to be malicious that it undermines accountability.
arXiv
We consider a general framework of optimal mechanism design under adverse selection and ambiguity about the type distribution of agents. We prove the existence of optimal mechanisms under minimal assumptions on the contract space and prove that centralized contracting implemented via mechanisms is equivalent to delegated contracting implemented via a contract menu under these assumptions. Our abstract existence results are applied to a series of applications that include models of optimal risk sharing and of optimal portfolio delegation.
SSRN
We find that mutual fund shareholders are particularly supportive of directors who serve simultaneously on a corporate board and a mutual fund board (overlapping directors). Such support is observed both for connected funds, which share a director with a company, and for non-connected funds, which do not share a director with the company. Our results imply that the benefits overlapping directors offer to all fund shareholders exceed the costs. Mutual funds are particularly supportive of overlapping directors when monitoring is needed. Our results suggest overlapping directors are more valuable to fund shareholders than to other types of shareholders.
SSRN
We show that household consumption displays excess sensitivity to salient macro-economic news. When the announced local unemployment rate reaches a 12-month maximum, local consumers in that area reduce discretionary spending by 2% relative to consumers in areas with the same macro-economic fundamentals. The consumption of low-income households displays greater excess sensitivity to salience. The decrease in spending is not reversed in subsequent months; instead, negative news persistently reduces future spending for two to four months. Announcements of 12-month unemployment maximums also lead consumers to reduce their credit card repayments by 3.6%.
SSRN
This paper studies how systemic risk can appear when rational agents establish derivative contracts in a fixed network. Agents are endowed with risks. They optimize their situation by designing and trading derivative contracts. Those deals spread the risks throughout the network and harmonize the amount of capital agents tie up respectively to their exposure. This leads to the emergence of a tipping point of systemic failure. Only the agents who made benefits from the trades can survive past this tipping point. When taking into account the contagion of failures, survivors quickly default as well. This occurs for a large class of networks and behaviors.
arXiv
Rational respondents to economic surveys may report as a point forecast any measure of the central tendency of their (possibly latent) predictive distribution, for example the mean, median, mode, or any convex combination thereof. We propose tests of forecast rationality when the measure of central tendency used by the respondent is unknown. These tests require us to overcome an identification problem when the measures of central tendency are equal or in a local neighborhood of each other, as is the case for (exactly or nearly) symmetric and unimodal distributions. As a building block, we also present novel tests for the rationality of mode forecasts. We apply our tests to survey forecasts of individual income, Greenbook forecasts of U.S. GDP, and random walk forecasts for exchange rates. We find that the Greenbook and random walk forecasts are best rationalized as mean, or near-mean forecasts, while the income survey forecasts are best rationalized as mode forecasts.
SSRN
The banking reforms that followed the financial crisis of 2007â€"08 led to an increase in UK banking regulation from almost 400,000 to over 720,000 words, and to concerns about their complexity. We define complexity in terms of the difficulty of processing linguistic units, both in isolation and within a broader context, and use natural language processing and network analysis to calculate complexity measures on a novel dataset that covers the near universe of prudential regulation for banks in the United Kingdom before (2007) and after (2017) the reforms. Linguistic, ie textual and network, complexity in banking regulation is concentrated in a relatively small number of provisions, and the post-crisis reforms have accentuated this feature. In particular, the comprehension of provisions within a tightly connected ‘core’ requires following long chains of cross-references.
SSRN
Shadow banks had a negligible presence in the US corporate loan market in the 1990s, but by 2016 they funded about 45% of the outstanding corporate term loans. Consistent with banking theories on liquidity provision, shadow banks remained absent from the credit line business. Nonetheless, they had a negative impact on the liquidity insurance provided by credit lines. The arrival of shadow banks increased competition in the term loan business and triggered a substitution of traditional term loans that amortize linearly with bullet loans that are paid at maturity. These changes led to the exit of banks, in particular those with lower risk appetite, not only from term loans but also from credit lines that are part of the deals containing those term loans. As a result, credit line syndicates have become more concentrated and funded by riskier banks, thereby, reducing the liquidity insurance they offer to corporations.
SSRN
With the continuous questioning of the efficient market hypothesis and the booming development of behavioral finance theory, the basic framework of financial economics becomes increasingly blurred. Meanwhile, the adaptive market hypothesis proposed by Lo (2004), underscoring time-varying market efficiency, introduces the idea of biology and evolution into modern finance to reconcile the two mainstream theories. This paper uses the Shanghai Composite Index and Shenzhen Composite Index from two Chinese stock markets and adopts AR(1) model, ARIMA model, variance ratio test and automatic portmanteau test with rolling windows to test the time-varying efficiency of Chinese markets. On the other hand, GARCH model is adopted to study the dynamic relationship between return and risk with analysis of the mechanism behind the market. The results of the research support the adaptive market hypothesis: the results of the full sample test show that the Chinese market as a whole is inefficient, and there is no trend of efficiency growing monotonously. Although most results of sub-sample analysis demonstrate weak-form market efficiency, there exist numerous periods with predictable return, which demonstrates a two-or-three-year cycle. Besides, the relationship between risk and return is very unstable, reflecting investors’ time-varying risk preferences.
SSRN
Ukraine urgently needs a complex programme of far-reaching economic and institutional reform, which will include both short-term fiscal and macroeconomic adjustment measures and medium- to long-term structural and institutional changes.
SSRN
The environmental issues have long been an important topic in China, the studies combining the environment and the capital market are scant in the existing literature. This study examines the effect of an environmentally friendly documentary, Under the Dome, on the stock prices of the environmentally friendly and the polluting companies listed on the Chinese A-share market. To avoid the problem of endogeneity and omitted variables, this study applies the propensity scoring method (PSM) and the difference in difference (DID) technique to the panel data obtained from the Wind Financial Terminal. Specifically, each environmentally friendly company or polluting company, the treatment group, is matched with two companies selected as the control group. The result indicates a significantly positive effect on the environmentally friendly companies’ stock prices and a negative effect on the polluting ones. Furthermore, this study tries to find the mechanism behind the phenomenon. The empirical results show that state holding and the market power of an enterprise can explain the price change of two types of companies. Moreover, several enterprise scale factors are also found related to the phenomenon.
arXiv
Input Output (IO) tables provide a standardised way of looking at monetary flows between all industries in an economy. IO tables can be thought of as networks - with the nodes being different industries and the edges being the flows between them. We develop a network-based analysis to consider a multi-regional IO network at regional and subregional level within a country. We calculate both traditional matrix-based IO measures (e.g. 'multipliers') and new network theory-based measures at this higher spatial resolution. We contrast these methods with the results of a disruption model applied to the same IO data in order to demonstrate that betweenness centrality gives a good indication of flow on economic disruption, while eigenvector-type centrality measures give results comparable to traditional IO multipliers.We also show the effects of treating IO networks at different levels of spatial aggregation.
SSRN
In this study, we investigate the wealth decumulation decision from the perspective of a retiree who is averse to the prospect of fully annuitizing her accumulated savings. We field a large online survey of hypothetical product choices for phased drawdown offerings and annuities. While the demand for annuities remains low in our sample, we find significant demand for phased withdrawal products with equity-based asset allocations and flexible payout structures. Consistent with the product choice, the most important self-reported considerations for the wealth decumulation decision are low default risk in the products they purchase, the size of the withdrawal rates, and flexibility in the timing of their withdrawal. As determinants of the decision of how much wealth individuals are willing to draw down, we identify consumers’ attitudes towards future economic conditions, the extent to which they are protected against longevity risk, and their desire to leave bequests. Policy implications are discussed.
