cicyt UNIZAR

Quantitative Finance

New submissions

[ total of 12 entries: 1-12 ]
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New submissions for Tue, 20 Mar 18

[1]  arXiv:1803.06460 [pdf, other]
Title: Mean Reverting Portfolios via Penalized OU-Likelihood Estimation
Comments: 7 pages, 6 figures
Subjects: Portfolio Management (q-fin.PM); Optimization and Control (math.OC); Machine Learning (stat.ML)

We study an optimization-based approach to con- struct a mean-reverting portfolio of assets. Our objectives are threefold: (1) design a portfolio that is well-represented by an Ornstein-Uhlenbeck process with parameters estimated by maximum likelihood, (2) select portfolios with desirable characteristics of high mean reversion and low variance, and (3) select a parsimonious portfolio, i.e. find a small subset of a larger universe of assets that can be used for long and short positions. We present the full problem formulation, a specialized algorithm that exploits partial minimization, and numerical examples using both simulated and empirical price data.

[2]  arXiv:1803.06653 [pdf]
Title: Modeling stock markets through the reconstruction of market processes
Comments: 49 pages, dissertation
Subjects: Statistical Finance (q-fin.ST); Computational Finance (q-fin.CP)

There are two possible ways of interpreting the seemingly stochastic nature of financial markets: the Efficient Market Hypothesis (EMH) and a set of stylized facts that drive the behavior of the markets. We show evidence for some of the stylized facts such as memory-like phenomena in price volatility in the short term, a power-law behavior and non-linear dependencies on the returns.
Given this, we construct a model of the market using Markov chains. Then, we develop an algorithm that can be generalized for any N-symbol alphabet and K-length Markov chain. Using this tool, we are able to show that it's, at least, always better than a completely random model such as a Random Walk. The code is written in MATLAB and maintained in GitHub.

[3]  arXiv:1803.06917 [pdf, other]
Title: Universal features of price formation in financial markets: perspectives from Deep Learning
Subjects: Statistical Finance (q-fin.ST); Trading and Market Microstructure (q-fin.TR); Machine Learning (stat.ML)

Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of electronic market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary price formation mechanism relating the dynamics of supply and demand for a stock, as revealed through the order book, to subsequent variations in its market price. We assess the model by testing its out-of-sample predictions for the direction of price moves given the history of price and order flow, across a wide range of stocks and time periods. The universal price formation model is shown to exhibit a remarkably stable out-of-sample prediction accuracy across time, for a wide range of stocks from different sectors. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific.
The universal model --- trained on data from all stocks --- outperforms, in terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear models trained on time series of any given stock, showing that the universal nature of price formation weighs in favour of pooling together financial data from various stocks, rather than designing asset- or sector-specific models as commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations is shown to improve forecasting performance, showing evidence of path-dependence in price dynamics.

[4]  arXiv:1803.06922 [pdf, ps, other]
Title: Approximation of Some Multivariate Risk Measures for Gaussian Risks
Authors: E. Hashorva
Subjects: Risk Management (q-fin.RM); Probability (math.PR)

Gaussian random vectors exhibit the loss of dimension phenomena, which relate to their joint survival tail behaviour. Besides, the fact that the components of such vectors are light-tailed complicates the approximations of various multivariate risk measures significantly. In this contribution we derive precise approximations of marginal mean excess, marginal expected shortfall and multivariate conditional tail expectation of Gaussian random vectors and highlight links with conditional limit theorems. Our study indicates that similar results hold for elliptical and Gaussian like multivariate risks.

[5]  arXiv:1803.07021 [pdf, other]
Title: Jumping VaR: Order Statistics Volatility Estimator for Jumps Classification and Market Risk Modeling
Comments: 30 pages, 29 figures, source code available at this https URL
Subjects: Risk Management (q-fin.RM)

This paper proposes a new integrated variance estimator based on order statistics within the framework of jump-diffusion models. Its ability to disentangle the integrated variance from the total process quadratic variation is confirmed by both simulated and empirical tests. For practical purposes, we introduce an iterative algorithm to estimate the time-varying volatility and the occurred jumps of log-return time series. Such estimates enable the definition of a new market risk model for the Value at Risk forecasting. We show empirically that this procedure outperforms the standard historical simulation method applying standard back-testing approach.

[6]  arXiv:1803.07041 [pdf, ps, other]
Title: Spatial risk measures and rate of spatial diversification
Authors: Erwan Koch
Subjects: Risk Management (q-fin.RM)

An accurate assessment of the risk of extreme environmental events is of great importance for populations, authorities and the banking/insurance industry. Koch (2017) introduced a notion of spatial risk measure and a corresponding set of axioms which are well suited to analyse the risk due to events having a spatial extent, precisely such as environmental phenomena. The axiom of asymptotic spatial homogeneity is of particular interest since it allows to quantify the rate of spatial diversification when the region under consideration becomes large. In this paper, we first investigate the general concepts of spatial risk measures and corresponding axioms further. Second, in the case of a general cost field, we especially give sufficient conditions such that spatial risk measures associated with expectation, variance, Value-at-Risk as well as expected shortfall and induced by this cost field satisfy the axioms of asymptotic spatial homogeneity of order 0, -2, -1 and -1, respectively. Last but not least, in the case where the cost field is a function of a max-stable random field, we mainly provide conditions on both the function and the max-stable field ensuring the latter properties. Max-stable random fields are relevant when assessing the risk of extreme events since they appear as a natural extension of multivariate extreme-value theory to the level of random fields. Overall, this paper improves our understanding of spatial risk measures as well as of their properties with respect to the space variable and generalises many results obtained in Koch (2017).

Cross-lists for Tue, 20 Mar 18

[7]  arXiv:1803.06738 (cross-list from stat.ME) [pdf, other]
Title: Large-Scale Dynamic Predictive Regressions
Subjects: Methodology (stat.ME); Econometrics (econ.EM); Statistical Finance (q-fin.ST)

We develop a novel "decouple-recouple" dynamic predictive strategy and contribute to the literature on forecasting and economic decision making in a data-rich environment. Under this framework, clusters of predictors generate different latent states in the form of predictive densities that are later synthesized within an implied time-varying latent factor model. As a result, the latent inter-dependencies across predictive densities and biases are sequentially learned and corrected. Unlike sparse modeling and variable selection procedures, we do not assume a priori that there is a given subset of active predictors, which characterize the predictive density of a quantity of interest. We test our procedure by investigating the predictive content of a large set of financial ratios and macroeconomic variables on both the equity premium across different industries and the inflation rate in the U.S., two contexts of topical interest in finance and macroeconomics. We find that our predictive synthesis framework generates both statistically and economically significant out-of-sample benefits while maintaining interpretability of the forecasting variables. In addition, the main empirical results highlight that our proposed framework outperforms both LASSO-type shrinkage regressions, factor based dimension reduction, sequential variable selection, and equal-weighted linear pooling methodologies.

Replacements for Tue, 20 Mar 18

[8]  arXiv:1704.05276 (replaced) [pdf, other]
Title: Best reply structure and equilibrium convergence in generic games
Comments: Main paper + Supplemental Information
Subjects: Physics and Society (physics.soc-ph); Adaptation and Self-Organizing Systems (nlin.AO); Economics (q-fin.EC)
[9]  arXiv:1707.04475 (replaced) [pdf, ps, other]
Title: Reduced-form framework under model uncertainty
Subjects: Mathematical Finance (q-fin.MF)
[10]  arXiv:1708.02563 (replaced) [pdf, other]
Title: Turbocharging Monte Carlo pricing for the rough Bergomi model
Comments: 16 pages, 10 figures, v3: minor amendments and reformatted
Subjects: Computational Finance (q-fin.CP); Pricing of Securities (q-fin.PR)
[11]  arXiv:1802.10117 (replaced) [pdf, other]
Title: Fundamental Values of Cryptocurrencies and Blockchain Technology
Comments: 36 pages
Subjects: Pricing of Securities (q-fin.PR); Cryptography and Security (cs.CR); Economics (q-fin.EC)
[12]  arXiv:1803.02570 (replaced) [pdf, ps, other]
Title: Why Black Swan events must occur
Subjects: Risk Management (q-fin.RM); Logic (math.LO)
[ total of 12 entries: 1-12 ]
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