We propose a microstructural modeling framework for studying optimal market making policies in a FIFO (first in first out) limit order book (LOB). In this context, the limit orders, market orders, and cancel orders arrivals in the LOB are modeled as Cox point processes with intensities that only depend on the state of the LOB. These are high-dimensional models which are realistic from a micro-structure point of view and have been recently developed in the literature. In this context, we consider a market maker who stands ready to buy and sell stock on a regular and continuous basis at a publicly quoted price, and identifies the strategies that maximize her P\&L penalized by her inventory. We apply the theory of Markov Decision Processes and dynamic programming method to characterize analytically the solutions to our optimal market making problem. The second part of the paper deals with the numerical aspect of the high-dimensional trading problem. We use a control randomization method combined with quantization method to compute the optimal strategies. Several computational tests are performed on simulated data to illustrate the efficiency of the computed optimal strategy. In particular, we simulated an order book with constant/ symmet-ric/ asymmetrical/ state dependent intensities, and compared the computed optimal strategy with naive strategies. Some codes are available on https://github.com/comeh.

We consider a large population dynamic game in discrete time. The peculiarity of the game is that players are characterized by time-evolving types, and so reasonably their actions should not anticipate the future values of their types. When interactions between players are of mean-field kind, we relate Nash equilibria for such games to an asymptotic notion of dynamic Cournot-Nash equilibria. Inspired by the works of Blanchet and Carlier for the static situation, we interpret dynamic Cournot-Nash equilibria in the light of causal optimal transport theory. Further specializing to games of potential type, we establish existence, uniqueness and characterization of equilibria. Moreover we develop, for the first time, a numerical scheme for causal optimal transport, which is then leveraged in order to compute dynamic Cournot-Nash equilibria. This is illustrated in a detailed case study of a congestion game.

Two taxonomies of money that include cryptocurrencies are analyzed. A definition of the term cryptocurrency is given and a taxonomy of them is presented, based on how its price is fixed. The characteristics of the use of current fiat money and the operation of two-level banking systems are discussed. Cryptocurrencies are compared with fiat money and the aspects in which the latter cannot be overcome are indicated. The characteristics of blockchains and databases are described. The possible cases of use of both technologies are compared, and it is noted that blockchains, in addition to cryptocurrencies and certain records, have not yet shown their usefulness, while databases constitute the foundation of most of the automated systems in operation.

Derivative pricing is about cash flow discounting at the riskfree rate. This teaching has lost its meaning post the financial crisis, due to the addition of extra value adjustments (XVA), which also made derivatives pricing and valuation a very difficult task for investors. This article recovers a properly defined discount rate that corresponds to different collateral and margin schemes. A binomial tree model is developed, enabling end-users to price in counterparty default and funding risk. Coherent XVAs, if needed, naturally result from decomposing the discount rate, and can be computed on the same tree.

This article presents a deep reinforcement learning approach to price and hedge financial derivatives. This approach extends the work of Guo and Zhu (2017) who recently introduced the equal risk pricing framework, where the price of a contingent claim is determined by equating the optimally hedged residual risk exposure associated respectively with the long and short positions in the derivative. Modifications to the latter scheme are considered to circumvent theoretical pitfalls associated with the original approach. Derivative prices obtained through this modified approach are shown to be arbitrage-free.

The current paper also presents a general and tractable implementation for the equal risk pricing framework inspired by the deep hedging algorithm of Buehler et al. (2019). An $\epsilon$-completeness measure allowing for the quantification of the residual hedging risk associated with a derivative is also proposed. The latter measure generalizes the one presented in Bertsimas et al. (2001) based on the quadratic penalty.

Monte Carlo simulations are performed under a large variety of market dynamics to demonstrate the practicability of our approach, to perform benchmarking with respect to traditional methods and to conduct sensitivity analyses.

Multivariate volatility modeling and forecasting are crucial in financial economics. This paper develops a copula-based approach to model and forecast realized volatility matrices. The proposed copula-based time series models can capture the hidden dependence structure of realized volatility matrices. Also, this approach can automatically guarantee the positive definiteness of the forecasts through either Cholesky decomposition or matrix logarithm transformation. In this paper we consider both multivariate and bivariate copulas; the types of copulas include Student's t, Clayton and Gumbel copulas. In an empirical application, we find that for one-day ahead volatility matrix forecasting, these copula-based models can achieve significant performance both in terms of statistical precision as well as creating economically mean-variance efficient portfolio. Among the copulas we considered, the multivariate-t copula performs better in statistical precision, while bivariate-t copula has better economical performance.

A negative basis trade enters a long bond position and buys protection on the issuer of the bond through credit default swap (CDS), aiming at arbitrage profit due to the bond-CDS basis. To classic reduced form model theorists, the existence of the basis is an abnormality or merely liquidity noise. Such a view, however, fails to explain large basis trading losses incurred during the financial crisis. Employing a bond continuously hedged by CDS under a dynamic spread model with bond repo financing, we find that there is unhedged and unhedgeable residual jump to default risk that can't be diversified because of credit correlation. An economic capital approach has to apply and a charge on the use of capital follows. Together with the hedge funding cost, it allows us to better understand the basis's economics and to predict its fair level.

We study how news personalization affects policy polarization. In a two-candidate electoral competition model, an attention-maximizing infomediary aggregates information about candidate valence into news, whereas voters decide whether to consume news, trading off the expected utility gain from improved expressive voting against the attention cost. Broadcast news attracts a broad audience by offering a symmetric signal. Personalized news serves extreme voters with skewed signals featuring own-party bias and occasional big surprise. Rational news aggregation yields policy polarization even if candidates are office-motivated. Personalization makes extreme voters the disciplining entity for equilibrium polarization and increases polarization through occasional big surprise.