We propose an algorithm which predicts each subsequent time step relative to the previous timestep of intractable short rate model (when adjusted for drift and overall distribution of previous percentile result) and show that the method achieves superior outcomes to the unbiased estimate both on the trained dataset and different validation data.

Cross-correlations in fluctuations of the daily exchange rates within the basket of the 100 highest-capitalization cryptocurrencies over the period October 1, 2015, through March 31, 2019, are studied. The corresponding dynamics predominantly involve one leading eigenvalue of the correlation matrix, while the others largely coincide with those of Wishart random matrices. However, the magnitude of the principal eigenvalue, and thus the degree of collectivity, strongly depends on which cryptocurrency is used as a base. It is largest when the base is the most peripheral cryptocurrency; when more significant ones are taken into consideration, its magnitude systematically decreases, nevertheless preserving a sizable gap with respect to the random bulk, which in turn indicates that the organization of correlations becomes more heterogeneous. This finding provides a criterion for recognizing which currencies or cryptocurrencies play a dominant role in the global crypto-market. The present study shows that over the period under consideration, the Bitcoin (BTC) predominates, hallmarking exchange rate dynamics at least as influential as the US dollar. The BTC started dominating around the year 2017, while further cryptocurrencies, like the Ethereum (ETH) and even Ripple (XRP), assumed similar trends. At the same time, the USD, an original value determinant for the cryptocurrency market, became increasingly disconnected, its related characteristics eventually approaching those of a fictitious currency. These results are strong indicators of incipient independence of the global cryptocurrency market, delineating a self-contained trade resembling the Forex.

Islamic banks being commercial entities strive to earn profit within shariah ambit. Therefore, they seem to be basing themselves upon two knowledge streams namely i) Islamic jurisprudence principles, and ii) banking principles. Islamic jurisprudence principles primarily aim at bringing shariah compliance while banking principles focus profitability. These principles, making two schools of thought in the discipline, however, have their unique philosophies, principles, and practices, which are now gradually diffusing into an emergent set of governance principles basing the contemporary Islamic banking theory and practice. Governance systems of Islamic banks have elements of both conventional as well as Shariah, and need to have principles having components of banking and shariah sufficiently diffused for their successful operations in a longer term. Aim of this research is to review the literature about the knowledge diffusion process of islamic banking principles which guides the governance of Islamic banks. This study review the literature using a method in which focus remain on bridging different areas which in this case are knowledge diffusion and islamic banking governance principles.

We consider a sequential social-learning environment with rational agents and Gaussian private signals, focusing on how the observation network affects the speed of learning. Agents learn about a binary state and take turns choosing actions based on own signals and network neighbors' behavior. Equilibrium learning may be slow when agents do not observe all predecessors, as agents compromise between incorporating the signals of the observed neighbors and not over-counting the confounding signals of the unobserved early movers. We show that on any network, equilibrium actions are a log-linear function of observations and each agent's accuracy admits a signal-counting interpretation. Adding links to the observation network can harm agents even without introducing new confounds. We then consider a network structure where agents move in generations and observe some members of the previous generation. When this observation structure is sufficiently symmetric, the additional information aggregated by each generation is asymptotically equivalent to fewer than two independent signals, even when generations are arbitrarily large. When agents observe all predecessors from the previous generation, social learning aggregates no more than three signals per generation starting from the third generation, and the long-run learning rate is slower when generations are larger.

Abundant literature has been published on approximation methods for the forward initial margin. The most popular ones being the family of regression methods. This paper describes the mathematical foundations on which these regression approximation methods lie. We introduce mathematical rigor to show that in essence, all the methods propose variations of approximations for the conditional expectation function, which is interpreted as an orthogonal projection on Hilbert spaces. We show that each method is simply choosing a different functional form to numerically estimate the conditional expectation. We cover in particular the most popular methods in the literature so far, Polynomial approximation, Kernel regressions and Neural Networks.

Scaling and multiscaling financial time series have been widely studied in the literature. The research on this topic is vast and still flourishing. One way to analyse the scaling properties of time series is through the estimation of scaling exponents. These exponents are recognized as being valuable measures to discriminate between random, persistent, and anti-persistent behaviours in time series. In the literature, several methods have been proposed to study the multiscaling property and in this paper we use the generalized Hurst exponent (GHE). On the base of this methodology, we propose a novel statistical procedure to robustly estimate and test the multiscaling property and we name it RNSGHE. This methodology, together with a combination of t-tests and F-tests to discriminated between real and spurious scaling. Moreover, we also introduce a new methodology to estimate the optimal aggregation time used in our methodology. We numerically validate our procedure on simulated time series using the Multifractal Random Walk (MRW) and then apply it to real financial data. We also present results for times series with and without anomalies and we compute the bias that such anomalies introduce in the measurement of the scaling exponents. Finally, we show how the use of proper scaling and multiscaling can ameliorate the estimation of risk measures such as Value at Risk (VaR). We also propose a methodology based on Monte Carlo simulation, that we name Multiscaling Value at Risk (MSVaR), which takes into account the statical properties of multiscaling time series. We show that by using this statistical procedure in combination with the robustly estimated multiscaling exponents, the one year forecasted MSVaR mimics the VaR on the annual data for the majority of the stocks analysed.

The decision to locate an economic activity of one or several countries is made taking into account numerous parameters and criteria. Several studies have been carried out in this field, but they generally use information in a reduced context. The majority are based solely on parameters, using traditional methods which often lead to unsatisfactory solutions.This work consists in hybridizing through genetic algorithms, economic intelligence (EI) and multicriteria analysis methods (MCA) to improve the decisions of territorial localization. The purpose is to lead the company to locate its activity in the place that would allow it a competitive advantage. This work also consists of identifying all the parameters that can influence the decision of the economic actors and equipping them with tools using all the national and international data available to lead to a mapping of countries, regions or departments favorable to the location. Throughout our research, we have as a goal the realization of a hybrid conceptual model of economic intelligence based on multicriteria on with genetic algorithms in order to optimize the decisions of localization, in this perspective we opted for the method of PROMETHEE (Preference Ranking Organization for Method of Enrichment Evaluation), which has made it possible to obtain the best compromise between the various visions and various points of view.

We explore nature of price formation in financial markets and develop a theory of bid and ask price dynamics in which the two prices form due to quantum-chaotic interaction between buy and sell orders. In this model bid and ask prices are represented by eigenvalues of a 2x2 price operator corresponding to 'bid' and 'ask' eigenstates, while randomness of price operator results in price fluctuations that destroy oscillatory effects. We show that this theory adequately captures behavior of bid-ask spread and allows to model bid and ask price dynamics in a coordinated way. We also discuss ergodicity properties of price formation and show how directional price movement occurs due to ergodicity violation in a quantum process instead of the commonly believed forces acting on price. This theory has wide range of applications such as trade execution modeling, large order pricing and risk valuation for illiquid securities.

We consider a frictionless constant endowment economy based on Leeper (1991). In this economy, it is shown that, under an ad-hoc monetary rule and an ad-hoc fiscal rule, there are two equilibria. One has active monetary policy and passive fiscal policy, while the other has passive monetary policy and active fiscal policy. We consider an extended setup in which the policy maker minimizes a loss function under quasi-commitment, as in Schaumburg and Tambalotti (2007). Under this formulation there exists a unique Ramsey equilibrium, with an interest rate peg and a passive fiscal policy. We thank John P. Conley, Luis de Araujo and one referree for their very helpful comments.

High future discounting rates favor inaction on present expending while lower rates advise for a more immediate political action. A possible approach to this key issue in global economy is to take historical time series for nominal interest rates and inflation, and to construct then real interest rates and finally obtaining the resulting discount rate according to a specific stochastic model. Extended periods of negative real interest rates, in which inflation dominates over nominal rates, are commonly observed, occurring in many epochs and in all countries. This feature leads us to choose a well-known model in statistical physics, the Ornstein-Uhlenbeck model, as a basic dynamical tool in which real interest rates randomly fluctuate and can become negative, even if they tend to revert to a positive mean value. By covering 14 countries over hundreds of years we suggest different scenarios and include an error analysis in order to consider the impact of statistical uncertainty in our results. We find that only 4 of the countries have positive long-run discount rates while the other ten countries have negative rates. Even if one rejects the countries where hyperinflation has occurred, our results support the need to consider low discounting rates. The results provided by these fourteen countries significantly increase the priority of confronting global actions such as climate change mitigation. We finally extend the analysis by first allowing for fluctuations of the mean level in the Ornstein-Uhlenbeck model and secondly by considering modified versions of the Feller and lognormal models. In both cases, results remain basically unchanged thus demonstrating the robustness of the results presented.

We build a methodology that takes a given option price in the tails with strike $K$ and extends (for calls, all strikes > $K$, for puts all strikes $< K$) assuming the continuation falls into what we define as "Karamata Constant" over which the strong Pareto law holds. The heuristic produces relative prices for options, with for sole parameter the tail index $\alpha$, under some mild arbitrage constraints.

Usual restrictions such as finiteness of variance are not required.

The methodology allows us to scrutinize the volatility surface and test various theories of relative tail option overpricing (usually built on thin tailed models and minor modifications/fudging of the Black-Scholes formula).

We present a simple model that uses time series momentum in order to construct strategies that systematically outperform their benchmark. The simplicity of our model is elegant: We only require a benchmark time series and several related investable indizes, not requiring regression or other models to estimate our parameters. We find that our one size fits all approach delivers significant outperformance in both equity and bond markets while meeting the ex-ante risk requirements, nearly doubling yearly returns vs. the MSCI World and Bloomberg Barclays Euro Aggregate Corporate Bond benchmarks in a long-only backtest. We then combine both approaches into an absolute return strategy by benchmarking vs. the Eonia Total Return Index and find significant outperformance at a sharpe ratio of 1.8. Furthermore, we demonstrate that our model delivers a benefit versus a static portfolio with fixed mean weights, showing that timing of excess return momentum has a sizeable benefit vs. static allocations. This also applies to the passively investable equity factors, where we outperform a static factor exposure portfolio with statistical significance. Also, we show that our model delivers an alpha after deducting transaction costs.

We introduce simple cost and risk proxy metrics that can be attached to Treasury issuance strategy to complement analysis of the resulting portfolio weighted-average maturity (WAM). These metrics are based on mapping issuance fractions to their long-term, asymptotic portfolio implications for cost and risk under mechanical debt-rolling dynamics. The resulting mapping enables one to visualize tradeoffs involved in contemplated issuance reallocation, and identify an efficient frontier and optimal tenor. Historical Treasury issuance strategy is analyzed empirically using these cost and risk metrics to illustrate how changes in issuance needs and strategy have translated into structural shifts in the cost and risk stance of Treasury issuance.