不同步交易的滑动平均模型及其回报分析

不同步交易乃金融中高频数据处理的重要课题之一。本文对文[1]和[2]给出的金融证券的不同步交易模型进行了推广，并对推广的模型考察了可观察回报的有关统计特性，最后给出了模型的参数估计。     

不同步交易模型及其回报分析

不同步交易乃金融中高频数据处理的重要课题之一。该文对文［1 ］和［2］给出的金融证券的不同步交易模型中关于证券回报率序列是独立同分布(i.i. d.) 的假设推广为一个平稳自回归滑动平均模型。并对推广的模型考虑了可观察回报的有关统计特性，从而说明了原来i.i.d.假设下得到的关于可观察回报有负的自相关的结论不再成立 。这显然与实际更吻合。最后给出了模型的参数估计。     

Chance and necessity in sociological theory

This paper proposes a mathematical model of financial markets as networks. The model examines the effect of network structure on market behavior (price volatility and trading volume). In the model, investors are arrayed in various network configurations through which they gather information to make trading decisions. The basic network considered is a chain graph with two parameters, number of investors (n) and the length of time in which information is transmitted (k). Closed‐form expressions for price volatility and expected trading volume are provided. The model is generalized to more complex networks, focusing on the hub‐and‐spoke network. The network configurations analyzed do not represent the real (and unknown) communication network among investors, but predictions from the model are consistent with price and volume patterns observed in sociological and economic research on financial markets. The main result is that network structure alone influences price volatility and expected trading volume even though investors are homogeneous and the information introduced into the system is unbiased and random. This result suggests that the structure of the real communication network among investors may influence market behavior.     

Hedging unit‐linked life insurance contracts in a financial market driven by shot‐noise processes

We consider the risk‐minimizing hedging problem for unit‐linked life insurance in a financial market driven by a shot‐noise process. Because the financial market is incomplete, the insurance claims cannot be hedged completely by trading stocks and bonds only, leaving some risk to the insurer. The theory of ((pseudo) locally) risk‐minimization is applied after a change of measure. Then the risk‐minimizing trading strategies and the associated intrinsic risk processes are determined for two types of unit‐linked contracts represented by the pure endowment and the term insurance. Copyright © 2009 John Wiley & Sons, Ltd.     

Change points in heavy‐tailed multivariate time series: Methods using precision matrices

We propose two robust data‐driven techniques for detecting network structure change points between heavy‐tailed multivariate time series for situations where both the placement and number of change points are unknown. The first technique utilizes the graphical lasso method to estimate the change points, whereas the second technique utilizes the tlasso method. The techniques not only locate the change points but also estimate an undirected graph (or precision matrix) representing the relationship between the time series within each interval created by pairs of adjacent change points. An inference procedure on the edges is used in the graphs to effectively remove false‐positive edges, which are caused by the data deviating from normality. The techniques are compared using simulated multivariate t‐distributed (heavy‐tailed) time series data and the best method is applied to two financial returns data sets of stocks and indices. The results illustrate the method's ability to determine how the dependence structure of the returns changes over time. This information could potentially be used as a tool for portfolio optimization.     

Bayesian Fused Lasso Regression for Dynamic Binary Networks

We propose a multinomial logistic regression model for link prediction in a time series of directed binary networks. To account for the dynamic nature of the data, we employ a dynamic model for the model parameters that is strongly connected with the fused lasso penalty. In addition to promoting sparseness, this prior allows us to explore the presence of change points in the structure of the network. We introduce fast computational algorithms for estimation and prediction using both optimization and Bayesian approaches. The performance of the model is illustrated using simulated data and data from a financial trading network in the NYMEX natural gas futures market. Supplementary material containing the trading network dataset and code to implement the algorithms is available online.     

Dual representation of superhedging costs in illiquid markets

This paper studies superhedging of contingent claims in illiquid markets where trading costs may depend nonlinearly on the traded amounts and portfolios may be subject to constraints. We give dual expressions for superhedging costs of financial contracts where claims and premiums are paid possibly at multiple points in time. Besides classical pricing problems, this setup covers various swap and insurance contracts where premiums are paid in sequences. Validity of the dual expressions is proved under new relaxed conditions related to the classical no-arbitrage condition. A new version of the fundamental theorem of asset pricing is given for unconstrained models with nonlinear trading costs.     

On the Modeling of CO2 EUA and CER Prices of EU‐ETS for the 2008–2012 Period

Increased consumption of fossil fuels in industrial production has led to a significant elevation in the emission of greenhouse gases and to global warming. The most effective international action against global warming is the Kyoto Protocol, which aims to reduce carbon emissions to desired levels in a certain time span. Carbon trading is one of the mechanisms used to achieve the desired reductions. One of the most important implications of carbon trading for industrial systems is the risk of uncertainty about the prices of carbon allowance permits traded in the carbon markets. In this paper, we consider stochastic and time series modeling of carbon market prices and provide estimates of the model parameters involved, based on the European Union emissions trading scheme carbon allowances data obtained for 2008–2012 period. In particular, we consider fractional Brownian motion and autoregressive moving average–generalized autoregressive conditional heteroskedastic modeling of the European Union emissions trading scheme data and provide comparisons with benchmark models. Our analysis reveals evidence for structural changes in the underlying models in the span of the years 2008–2012. Data‐driven methods for identifying possible change‐points in the underlying models are employed, and a detailed analysis is provided. Our analysis indicated change‐points in the European Union Allowance (EUA) prices in the first half of 2009 and in the second half of 2011, whereas in the Certified Emissions Reduction (CER) prices three change‐points have appeared, in the first half of 2009, the middle of 2011, and in the second half of 2012. These change‐points seem to parallel the global economic indicators as well. Copyright © 2016 John Wiley & Sons, Ltd.     

Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets

The development of new models that would enhance predictability for time series with dynamic time-varying, nonlinear features is a major challenge for speculators. Boundedly rational investors called “chartists” use advanced heuristics and rules-of-thumb to make profit by trading, or even hedge against potential market risks. This paper introduces a hybrid neurofuzzy system for decision-making and trading under uncertainty. The efficiency of a technical trading strategy based on the neurofuzzy model is investigated, in order to predict the direction of the market for 10 of the most prominent stock indices of U.S.A, Europe and Southeast Asia. It is demonstrated via an extensive empirical analysis that the neurofuzzy model allows technical analysts to earn significantly higher returns by providing valid information for a potential turning point on the next trading day. The total profit of the proposed neurofuzzy model, including transaction costs, is consistently superior to a recurrent neural network and a Buy & Hold strategy for all indices, particularly for the highly speculative, emerging Southeast Asian markets. Optimal prediction is based on the dynamic update and adaptive calibration of the heuristic fuzzy learning rules, which reflect the psychological and behavioral patterns of the traders.     

夜盘交易对我国农产品期货市场影响的实证研究

随着我国农产品期货与国际市场的联动性进一步加强,为防止相关期货产品的隔夜风险和价格跳水问题,对部分农产品期货实行夜盘交易制度。为测度夜盘交易制度是否有益于农产品期货市场朝着稳定、理性的方向发展,本文采用了适合刻画金融序列波动性的GARCH族模型,实证检验得出GARCH、GARCH-M和EGARCH模型能够高度拟合农产品期货的价格序列并显著衡量夜盘交易对于我国农产品期货市场的影响。研究结论如下:第一、基于GRACH模型实证结果,夜盘交易制度变量的回归结果显著,该制度能减轻农产品期货的价格波动,且其影响是显著的;第二、EGARCH模型的回归结果同样显著,分别对比不同样本期的EGARCH模型实证结果可以得到,夜盘交易的开放减少了农产品期货市场的非对称性,使得市场趋向于理性的方向发展。     

Reduction in mean residual life in the presence of a constant competing risk

The addition of a constant ‘competing risk’ corresponding to an additional, usually less significant, source of failure, frequently improves the fit in reliability and survival analysis. This is often termed a ‘lift’, as the effect is to increase the hazard rate (HR) function by a constant, which does not, of course, change the shape and hence the turning points of the HR function. However, lifting the HR function does not, in general, mean lowering the corresponding mean residual life (MRL) function by a constant, and so the MRL turning points, unlike those of the HR function are not invariant. The MRL turning points are used in, for example, defining burn‐in procedures in reliability engineering, and determining premiums in insurance. Hence, it is of interest to examine the changes in the shape of the MRL function, and in the locations of its turning points, resulting from a lift in the HR function. We discuss these problems in detail, with reference to a number of common distributions in reliability and mortality modeling. Copyright © 2007 John Wiley & Sons, Ltd.     

Uniform methods for semilinear problems with attractive boundary turning points

An upwind finite‐difference scheme for the numerical solution of semilinear convection‐diffusion problems with attractive boundary turning points is considered. We show that the maximum nodal error is bounded by a special weighted ℓ₁‐type norm of the truncation error. This result is used to establish uniform convergence with respect to the perturbation parameter on Shishkin meshes.     

Some theoretical properties of Silverman's method for Smoothed functional principal component analysis

Principal component analysis (PCA) is one of the key techniques in functional data analysis. One important feature of functional PCA is that there is a need for smoothing or regularizing of the estimated principal component curves. Silverman’s method for smoothed functional principal component analysis is an important approach in a situation where the sample curves are fully observed due to its theoretical and practical advantages. However, lack of knowledge about the theoretical properties of this method makes it difficult to generalize it to the situation where the sample curves are only observed at discrete time points. In this paper, we first establish the existence of the solutions of the successive optimization problems in this method. We then provide upper bounds for the bias parts of the estimation errors for both eigenvalues and eigenfunctions. We also prove functional central limit theorems for the variation parts of the estimation errors. As a corollary, we give the convergence rates of the estimations for eigenvalues and eigenfunctions, where these rates depend on both the sample size and the smoothing parameters. Under some conditions on the convergence rates of the smoothing parameters, we can prove the asymptotic normalities of the estimations.     

A Relation between formal and Asymptotic Equivalence for linear Systems Containing A Parameter

We use an elementary method to draw analytic conclusions from divergent formal power series solutions of systems of differential equations containing a parameter and give some applications to the theory of turning points. Our main result shows that a divergent formal series transformation of one system into another in which the coefficients satisfy certain estimates is necessarily the asymptotic expansion of an actual transformation. We use it to show the following. Given a two dimensional system ε^Pdy/dx = A(x,ε)y with A holomorphic at (x₀,0), suppose that x₀ is formally not a turning point in the sense that no singularities appear at x₀ during the standard formal solution procedure with formal fractional power series in ε. Then the formal solution is necessarily a uniform asymptotic representation of a fundamental matrix of the system on a full neighborhood of x₀. (This conclusion is known to fail under weaker hypotheses on A). We also obtain similar but less complete results for higher order systems under more specialized hypotheses.     

Minimizing coherent risk measures of shortfall in discrete‐time models with cone constraints

The paper studies the problem of minimizing coherent risk measures of shortfall for general discrete‐time financial models with cone‐constrained trading strategies, as developed by Pham and Touzi. It is shown that the optimal strategy is obtained by super‐hedging a contingent claim, which is represented as a Neyman–Pearson‐type random variable.     

A smoothing sample average approximation method for stochastic optimization problems with CVaR risk measure

This paper is concerned with solving single CVaR and mixed CVaR minimization problems. A CHKS-type smoothing sample average approximation (SAA) method is proposed for solving these two problems, which retains the convexity and smoothness of the original problem and is easy to implement. For any fixed smoothing constant ε, this method produces a sequence whose cluster points are weak stationary points of the CVaR optimization problems with probability one. This framework of combining smoothing technique and SAA scheme can be extended to other smoothing functions as well. Practical numerical examples arising from logistics management are presented to show the usefulness of this method.     

Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization

The motivation for this paper is to introduce a hybrid neural network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF–PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a neural network fitness function for financial forecasting purposes. This is done by benchmarking the ARBF–PSO results with those of three different neural networks architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD) plus a na?¨ve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time series over the period January 1999–March 2011 using the last 2 years for out-of-sample testing.     

Calculating hedge fund risk: the draw down and the maximum draw down

Hedge funds, defined in this context as geared financial entities, frequently use some measure of point loss as a risk measure. This paper considers the statistical properties of an uninterrupted fall in a security price; called a draw down. The distribution of the draw downs in an N‐trading period is derived together with an approximation to the distribution of the maximum. Complementary results are provided which are useful for risk calculations. A brief empirical study of the S&P futures is included in order to highlight some of the limitations in the presence of extreme events.     

Using information to generate derivative coordinates from noisy time series

This paper describes an approach for recovering a signal, along with the derivatives of the signal, from a noisy time series. To mimic an experimental setting, noise was superimposed onto a deterministic time series. Data smoothing was then used to successfully recover the derivative coordinates; however, the appropriate level of data smoothing must be determined. To investigate the level of smoothing, an information theoretic is applied to show a loss of information occurs for increased levels of noise; conversely, we have shown data smoothing can recover information by removing noise. An approximate criterion is then developed to balance the notion of information recovery through data smoothing with the observation that nearly negligible information changes occur for a sufficiently smoothed time series.     

The approximation of generalized turning points by projection methods with superconvergence to the critical parameter

Summary A procedure is given that generates characterizations of singular manifolds for mildly nonlinear mappings between Banach spaces. This characterization is used to develop a method for determining generalized turning points by using projection methods as a discretization. Applications are given to parameter dependent two-point boundary value problems. In particular, collocation at Gauss points is shown to achieve superconvergence in approximating the parameter at simple turning points.