A Partially Observed Model for Micromovement of Asset Prices with Bayes Estimation via Filtering

A general micromovement model that describes transactional price behavior is proposed. The model ties the sample characteristics of micromovement and macromovement in a consistent manner. An important feature of the model is that it can be transformed to a filtering problem with counting process observations. Consequently, the complete information of price and trading time is captured and then utilized in Bayes estimation via filtering for the parameters. The filtering equations are derived. A theorem on the convergence of conditional expectation of the model is proved. A consistent recursive algorithm is constructed via the Markov chain approximation method to compute the approximate posterior and then the Bayes estimates. A simplified model and its recursive algorithm are presented in detail. Simulations show that the computed Bayes estimates converge to their true values. The algorithm is applied to one month of intraday transaction prices for Microsoft and the Bayes estimates are obtained.     

Bayesian time series analysis of periodic behaviour and spectral structure

Analysis, model selection and forecasting in univariate time series models can be routinely carried out for models in which the model order is relatively small. Under an ARMA assumption, classical estimation, model selection and forecasting can be routinely implemented with the Box–Jenkins time domain representation. However, this approach becomes at best prohibitive and at worst impossible when the model order is high. In particular, the standard assumption of stationarity imposes constraints on the parameter space that are increasingly complex. One solution within the pure AR domain is the latent root factorization in which the characteristic polynomial of the AR model is factorized in the complex domain, and where inference questions of interest and their solution are expressed in terms of the implied (reciprocal) complex roots; by allowing for unit roots, this factorization can identify any sustained periodic components. In this paper, as an alternative to identifying periodic behaviour, we concentrate on frequency domain inference and parameterize the spectrum in terms of the reciprocal roots, and, in addition, incorporate Gegenbauer components. We discuss a Bayesian solution to the various inference problems associated with model selection involving a Markov chain Monte Carlo (MCMC) analysis. One key development presented is a new approach to forecasting that utilizes a Metropolis step to obtain predictions in the time domain even though inference is being carried out in the frequency domain. This approach provides a more complete Bayesian solution to forecasting for ARMA models than the traditional approach that truncates the infinite AR representation, and extends naturally to Gegenbauer ARMA and fractionally differenced models.     

马尔可夫随机场在SAR图像处理中的应用

旅游业是一种外向性特征鲜明、关联带动性强、可持续发展性强的产业。它对地方政府的招商引资工作有着独特而重要的促进作用:旅游项目本身是良好的引资项目;商务旅游的开发直接促进招商引资工作;旅游业的发展有助于塑造和改善地方形象,改善地方投资环境。要充分发挥这些积极作用,就应在商务旅游产品开发、旅游与招商工作相结合、城市建设与旅游开发一体化、旅游与相关产业相结合等方面多下功夫。     

Monitoring recessions: A Bayesian sequential quickest detection method

Monitoring business cycles faces two potentially conflicting objectives: accuracy and timeliness. To strike a balance between these dual objectives, we propose a Bayesian sequential quickest detection method to identify turning points in real time with a sequential stopping time as a solution. Using four monthly indexes of real economic activity in the United States, we evaluated the method’s real-time ability to date the past five recessions. The proposed method identified similar turning-point dates as the National Bureau of Economic Research (NBER), with no false alarms, but on average, it dated peaks four months faster and troughs 10 months faster relative to the NBER announcement. The timeliness of our method is also notable compared to the dynamic factor Markov-switching model: the average lead time was about five months when dating peaks and two months when dating troughs.     

基于顾客满意度的绿色采购决策研究

从提高顾客满意度(CS)的视角,研究了生态供应链(ESC)环境下绿色采购决策优化问题。利用两状态、时间离散的Markov链描述了CS的动态转移过程,以赋值形式引入利润变量,建立了ESC绿色采购决策的随机优化模型,体现了顾客导向的思想;并且给出了应用算法释例。     

EMBEDDED MATRICES FOR FINITE MARKOV CHAINS

For an arbitrary subset A of the finite state space 5 of a Markov chain the so–called embedded matrix P _A is introduced. By use of these matrices formulas expressing all kinds of probabilities can be written down almost automatically, and calculated very easily on a computer. Also derivations can be given very systematically.     

Dynamic Currency Linkages and Their Determinants: An Empirical Study for East Asian Economic Community Region

In this article, Copula GARCH models have been employed to study the inter-temporal process of currency market co-movements between ASEAN+6 countries (referred to in this study as East Asian Economic Community) and ASEAN+6 currency market index. Empirical results show that the sample countries of the region exhibit varying levels of currency co-movements with the Asian benchmark. Markov regime switching results show that many of the countries which had high dependences with the regional currency index as was found in copula estimations had also overlapping currency market cycles. Using Principal Component Analysis, we find that three statistical factors explain exchange rate co-movements which came out to be trade linkages, economic risk, and currency market openness in our dynamic panel data estimation.     

U.S. Monetary Regimes and Optimal Monetary Policy in the Euro Area

Monetary policy in the United States has been documented to have switched from reacting weakly to inflation fluctuations during the 1970s, to fighting inflation aggressively from the early 1980s onward. In this paper, I analyze the impact of the U.S. monetary policy regime switches on the Eurozone. I construct a New Keynesian two‐country model where foreign (U.S.) monetary policy switches regimes over time. I estimate the model for the U.S. and the Euro Area using quarterly data and find that the United States has switched between those two regimes, in line with existing evidence. I show that foreign regime switches affect home (Eurozone) inflation and output volatility and their responses to shocks, substantially, as long as the home central bank commits to a time‐invariant interest rate rule reacting to domestic conditions only. Optimal policy in the home country instead requires that the home central bank reacts strongly to domestic producer‐price inflation and to international variables, such as imported goods relative prices. In fact, I show that currency misalignments and relative prices play a crucial role in the transmission of foreign monetary policy regime switches internationally. Interestingly, I show that only marginal gains arise for the Euro Area when the European Central Bank (ECB) adjusts its policy according to the monetary regime in the United States. Thus, a simple time‐invariant monetary policy rule with a strong reaction to Producer Price Index (PPI) inflation and relative prices is enough to counteract the effects of monetary policy switches in the United States.     

Moments of renewal shot-noise processes and their applications

In this paper, we study the family of renewal shot-noise processes. The Feynmann–Kac formula is obtained based on the piecewise deterministic Markov process theory and the martingale methodology. We then derive the Laplace transforms of the conditional moments and asymptotic moments of the processes. In general, by inverting the Laplace transforms, the asymptotic moments and the first conditional moments can be derived explicitly; however, other conditional moments may need to be estimated numerically. As an example, we develop a very efficient and general algorithm of Monte Carlo exact simulation for estimating the second conditional moments. The results can be then easily transformed to the counterparts of discounted aggregate claims for insurance applications, and we apply the first two conditional moments for the actuarial net premium calculation. Similarly, they can also be applied to credit risk and reliability modelling. Numerical examples with four distribution choices for interarrival times are provided to illustrate how the models can be implemented.     

Pricing mortgage insurance contracts under housing price cycles with jump risk: evidence from the U.K. housing market

Previous studies have investigated the determinants of housing price cycles in the housing market; however, we observed the phenomenon of housing price jumps in the 2007 subprime crisis. This paper presents a discussion on the housing price cycle and abnormal price jumps to describe the behavior of housing prices in the United Kingdom. The empirical results show that the impact factors of housing cycles are market risk and the switching factor. Furthermore, the impact factors of jump risks include the bursting of the housing bubble and financial crises. Therefore, in this paper, we employ the Markov switching model with jump risks to value the MI contracts and analyze the influences of housing price cycles, jump risks, risks of market interest rate, and the prepayment risks on MI premiums. The results of sensitivity analysis show that more volatile housing price index returns, as well as longer periods of higher volatility in housing prices, raise MI premiums. Moreover, the MI premium is positively related to the absolute value of the average jump amplitude and the shock frequency of abnormal events. There is the tradeoff between the market interest rate and the prepayment risk. The influences of market interest rate are different on MI premium with/without prepayment risks.