Presidential debates are thought to provide an important public good by revealing information on candidates to voters. However, this may not always be the case. We consider an endogenous model of presidential debates in which an incumbent and a contender (who is privately informed about her own quality) publicly announce whether they are willing to participate in a public debate, after taking into account that a voter's choice of candidate depends on her beliefs regarding the candidates' qualities and on the state of nature. Surprisingly, it is found that in equilibrium a debate occurs or does not occur independently of the contender's quality or the sequence of the candidates' announcements to participate and therefore the announcements are uninformative.

We develop an agent-based simulation of the catastrophe insurance and reinsurance industry and use it to study the problem of risk model homogeneity. The model simulates the balance sheets of insurance firms, who collect premiums from clients in return for ensuring them against intermittent, heavy-tailed risks. Firms manage their capital and pay dividends to their investors, and use either reinsurance contracts or cat bonds to hedge their tail risk. The model generates plausible time series of profits and losses and recovers stylized facts, such as the insurance cycle and the emergence of asymmetric, long tailed firm size distributions. We use the model to investigate the problem of risk model homogeneity. Under Solvency II, insurance companies are required to use only certified risk models. This has led to a situation in which only a few firms provide risk models, creating a systemic fragility to the errors in these models. We demonstrate that using too few models increases the risk of nonpayment and default while lowering profits for the industry as a whole. The presence of the reinsurance industry ameliorates the problem but does not remove it. Our results suggest that it would be valuable for regulators to incentivize model diversity. The framework we develop here provides a first step toward a simulation model of the insurance industry for testing policies and strategies for better capital management.

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.

Data providers such as government statistical agencies perform a balancing act: maximising information published to inform decision-making and research, while simultaneously protecting privacy. The emergence of identified administrative datasets with the potential for sharing (and thus linking) offers huge potential benefits but significant additional risks. This article introduces the principles and methods of linking data across different sources and points in time, focusing on potential areas of risk. We then consider confidentiality risk, focusing in particular on the "intruder" problem central to the area, and looking at both risks from data producer outputs and from the release of micro-data for further analysis. Finally, we briefly consider potential solutions to micro-data release, both the statistical solutions considered in other contributed articles and non-statistical solutions.

Using microscopic price models based on Hawkes processes, it has been shown that under some no-arbitrage condition, the high degree of endogeneity of markets together with the phenomenon of metaorders splitting generate rough Heston-type volatility at the macroscopic scale. One additional important feature of financial dynamics, at the heart of several influential works in econophysics, is the so-called feedback or Zumbach effect. This essentially means that past trends in returns convey significant information on future volatility. A natural way to reproduce this property in microstructure modeling is to use quadratic versions of Hawkes processes. We show that after suitable rescaling, the long term limits of these processes are refined versions of rough Heston models where the volatility coefficient is enhanced compared to the square root characterizing Heston-type dynamics. Furthermore the Zumbach effect remains explicit in these limiting rough volatility models.

We study the \emph{multi-level order-flow imbalance (MLOFI)}, which 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 use Ridge regression to 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 goodness-of-fit of the relationship improves with each additional price level that we include in the MLOFI vector. Our results underline how the complex order-flow activity deep into the LOB can influence the price-formation process.

The pricing of Bermudan options amounts to solving a dynamic programming principle , in which the main difficulty, especially in large dimension, comes from the computation of the conditional expectation involved in the continuation value. These conditional expectations are classically computed by regression techniques on a finite dimensional vector space. In this work, we study neural networks approximation of conditional expectations. We prove the convergence of the well-known Longstaff and Schwartz algorithm when the standard least-square regression is replaced by a neural network approximation.

As the rental housing market moves online, the Internet offers divergent possible futures: either the promise of more-equal access to information for previously marginalized homeseekers, or a reproduction of longstanding information inequalities. Biases in online listings' representativeness could impact different communities' access to housing search information, reinforcing traditional information segregation patterns through a digital divide. They could also circumscribe housing practitioners' and researchers' ability to draw broad market insights from listings to understand rental supply and affordability. This study examines millions of Craigslist rental listings across the US and finds that they spatially concentrate and over-represent whiter, wealthier, and better-educated communities. Other significant demographic differences exist in age, language, college enrollment, rent, poverty rate, and household size. Most cities' online housing markets are digitally segregated by race and class, and we discuss various implications for residential mobility, community legibility, gentrification, displacement, housing voucher utilization, and automated monitoring and analytics in the smart cities paradigm. While Craigslist contains valuable crowdsourced data to better understand affordability and available rental supply in real-time, it does not evenly represent all market segments. The Internet promises information democratization, and online listings can reduce housing search costs and increase choice sets. However, technology access/preferences and information channel segregation can concentrate such information-broadcasting benefits in already-advantaged communities, reproducing traditional inequalities and reinforcing residential sorting and segregation dynamics. Technology platforms construct new institutions with the power to shape spatial economies and human interactions.

This paper considers the problem of optimal liquidation of a position in a risky security in a financial market, where price evolution are risky and trades have an impact on price as well as uncertainty in the filling orders. The problem is formulated as a continuous time stochastic optimal control problem aiming at maximizing a generalized risk-adjusted profit and loss function. The expression of the risk adjustment is derived from the general theory of dynamic risk measures and is selected in the class of $g$-conditional risk measures. The resulting theoretical framework is nonclassical since the target function depends on backward components. We show that, under a quadratic specification of the driver of a backward stochastic differential equation, it is possible to find a closed form solution and an explicit expression of the optimal liquidation policies. In this way it is immediate to quantify the impact of risk-adjustment on the profit and loss and on the expression of the optimal liquidation policies.

Oscillations in the complementary cumulative distribution function (CCDF) of individual income data have been found in the data of various countries studied by different authors at different time periods, but the dynamical origins of this behavior are currently unknown. Although these datasets can be fitted by different functions at different income ranges, the Tsallis distribution has recently been found capable of fitting the whole distribution by means of only two parameters. This procedure showed clearly such oscillatory feature in the entire income range feature, but made it particularly visible at the tail of the distribution. Although log-periodic functions fitted to the data are capable of describing this behavior, a different approach to naturally disclose such oscillatory characteristics is to allow the Tsallis $q$-parameter to become complex. In this paper we use this idea in order to describe the behavior of the CCDF of the Brazilian personal income recently studied empirically by Soares et al.\ (2016). Typical elements of periodic motion, such as amplitude and angular frequency coupled to this income analysis, were obtained by means of this approach. A highly non-linear function for the CCDF was obtained through this methodology and a numerical test showed it capable of recovering the main oscillatory feature of the original CCDF of the personal income data of Brazil.

We generalize Quasi-Linear Means by restricting to the tail of the risk distribution and show that this can be a useful quantity in risk management since it comprises in its general form the Value at Risk, the Tail Value at Risk and the Entropic Risk Measure in a unified way. We then investigate the fundamental properties of the proposed measure and show its unique features and implications in the risk measurement process. Furthermore, we derive formulas for truncated elliptical models of losses and provide formulas for selected members of such models.

We develop an extended multifractal analysis based on the Legendre-Fenchel transform (sometimes referred to as Legendre multi-branched one) rather than the routinely used canonical Legendre transform. In our variant of coarse-graining pre-processing, the local detrending of time series has been replaced by an appropriate averaging over days combined with properly-suited detrending on a daily time scale. This new approach is devoid of troublesome artifacts in the form of innumerable faults of these local trends that can deform the hierarchy of fluctuations and hence the final multifractality. Notably, our analysis is sensitive to the change of time scale as it should be. This analysis has developed, e.g., for empirical time series of inter-event or waiting times, which are an essential element of the popular continuous-time random walk formalism. The core of this extended multifractal analysis is the non-monotonic behavior of the generalized Hurst exponent -- the fundamental exponent of the study -- and hence a multi-branched spectrum of dimensions, which for our case is additionally of the left-sided one. We examine the main thermodynamic consequences of the existence of this type of multifractality. They can be expressed directly in the language of thermally stable, metastable, and unstable phases, and phase transitions between them as well. These phase transitions are of the first and second orders according to the modified Ehrenfest classification, sometimes called the Mandelbrot one.