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Mining companies to properly manage their operations and be ready to make business decisions, are required to analyze potential scenarios for main market risk factors. The most important risk factors for KGHM, one of the biggest companies active in the metals and mining industry, are the price of copper (Cu), traded in US dollars, and the Polish zloty (PLN) exchange rate (USDPLN). The main scope of the paper is to understand the mid- and long-term dynamics of these two risk factors. For a mining company it might help to properly evaluate potential downside market risk and optimise hedging instruments. From the market risk management perspective, it is also important to analyze the dynamics of these two factors combined with the price of copper in Polish zloty (Cu in PLN), which jointly drive the revenues, cash flows, and financial results of the company. Based on the relation between analyzed risk factors and distribution analysis, we propose to use two-dimensional vector autoregressive (VAR) model with the $\alpha-$stable distribution. The non-homogeneity of the data is reflected in two identified regimes: first - corresponding to the 2008 crisis and second - to the stable market situation. As a natural implication of the model fitted to market assets, we derive the dynamics of the copper price in PLN, which is not a traded asset but is crucial for the KGHM company risk exposure. A comparative study is performed to demonstrate the effect of including dependencies of the assets and the implications of the regime change. Since for various international companies, risk factors are given rather in the national than the market currency, the approach is universal and can be used in different market contexts, like mining or oil companies, but also other commodities involved in the global trading system., Comment: 12 pages, 10 figures, 4 tables

Single-particle tracking (SPT) has become a popular tool to study the intracellular transport of molecules in living cells. Inferring the character of their dynamics is important, because it determines the organization and functions of the cells. For this reason, one of the first steps in the analysis of SPT data is the identification of the diffusion type of the observed particles. The most popular method to identify the class of a trajectory is based on the mean square displacement (MSD). However, due to its known limitations, several other approaches have been already proposed. With the recent advances in algorithms and the developments of modern hardware, the classification attempts rooted in machine learning (ML) are of particular interest. In this work, we adopt two ML ensemble algorithms, i.e. random forest and gradient boosting, to the problem of trajectory classification. We present a new set of features used to transform the raw trajectories data into input vectors required by the classifiers. The resulting models are then applied to real data for G protein-coupled receptors and G proteins. The classification results are compared to recent statistical methods going beyond MSD., 32 pages, 5 figures

In this paper, we have proposed a statistical procedure for detecting transitions of the mean-square-displacement exponent value within a single trajectory. With this procedure, we have identified three regimes of proteins dynamics on a cell membrane, namely, subdiffusion, free diffusion, and immobility. The fourth considered dynamics type, namely, superdiffusion was not detected. We show that the analyzed protein trajectories are not stationary and not ergodic. Moreover, classification of the dynamics type performed without prior detection of transitions may lead to the overestimation of the proportion of subdiffusive trajectories.

Taking advantage of recent single-particle tracking data, we compare the popular standard mean-squared displacement method with a statistical testing hypothesis procedure for three testing statistics and for two particle types: membrane receptors and the G proteins coupled to them. Each method results in different classifications. For this reason, more rigorous statistical tests are analyzed here in detail. The main conclusion is that the statistical testing approaches might provide good results even for short trajectories, but none of the proposed methods is ``the best'' for all considered examples; in other words, one needs to combine different approaches to get a reliable classification.

In this paper, we deal with a problem of local damage detection in bearings in the presence of a high-energy impulsive noise. Such a problem was identified during diagnostics of bearings in raw materials crusher. Unfortunately, classical approaches cannot be applied due to the impulsive character of the noise. In this paper we propose, a procedure that cancels out impulsive noise rather than extracts signal of interest. The methodology is based on the regime switching model with two regimes: first corresponding to high-energy noncyclic impulses and second to the rest of the signal. We apply the proposed technique to a simulated signal as well as to the real one. Effectiveness of the method is presented graphically using time series, time-frequency spectrogram, and classical envelope analysis. The obtained results indicate efficiency of the method in impulsive noise cancellation and improve the ability to detect a damage.

Solar flares are large explosions in the sun’s atmosphere. They can damage satellites and overload electrical systems. To manage that risk, finding methods of efficiently predicting future events is very important. In this paper we introduce a full-Sun flare prediction method based on the Hidden Markov modelling with two hidden states. We concentrate on the soft X-ray emission data near the minimum of solar cycle and consider two different driving dynamics for both states, namely the independent identically distributed (IID) random variables and the autoregressive (AR) processes, the latter introducing a memory structure. We compare prediction performance for the IID and AR approaches and also with a naive prediction equal to the last observation. The solar X-flux dynamics is predicted by using the day-ahead forecasts. We calculate point and interval forecasts and perform relevant statistical tests to choose the best method. It appears that the AR approach is clearly superior to the IID and naive both by means of point and interval forecasts. Moreover, it can well detect the higher state which can lead to very strong energy releases. Significant development of the model would be necessary to forecast solar flares over an entire solar cycle.

In this paper, we study ergodic properties of α-stable autoregressive fractionally integrated moving average (ARFIMA) processes which form a large class of anomalous diffusions. A crucial practical question is how long trajectories one needs to observe in an experiment in order to claim that the analyzed data are ergodic or not. This will be solved by checking the asymptotic convergence to 0 of the empirical estimator F(n) for the dynamical functional D(n) defined as a Fourier transform of the n-lag increments of the ARFIMA process. Moreover, we introduce more flexible concept of the e-ergodicity.

We present a systematic statistical analysis of the recently measured individual trajectories of fluorescently labeled telomeres in the nucleus of living human cells. The experiments were performed in the U2OS cancer cell line. We propose an algorithm for identification of the telomere motion. By expanding the previously published data set, we are able to explore the dynamics in six time orders, a task not possible earlier. As a result, we establish a rigorous mathematical characterization of the stochastic process and identify the basic mathematical mechanisms behind the telomere motion. We find that the increments of the motion are stationary, Gaussian, ergodic, and even more chaotic—mixing. Moreover, the obtained memory parameter estimates, as well as the ensemble average mean square displacement reveal subdiffusive behavior at all time spans. All these findings statistically prove a fractional Brownian motion for the telomere trajectories, which is confirmed by a generalized p-variation test. Taking into account the biophysical nature of telomeres as monomers in the chromatin chain, we suggest polymer dynamics as a sufficient framework for their motion with no influence of other models. In addition, these results shed light on other studies of telomere motion and the alternative telomere lengthening mechanism. We hope that identification of these mechanisms will allow the development of a proper physical and biological model for telomere subdynamics. This array of tests can be easily implemented to other data sets to enable quick and accurate analysis of their statistical characteristics.

This paper complements a recently published study (Janczura and Weron in AStA-Adv Stat Anal 96(3):385–407, 2012) on efficient estimation of Markov regime-switching models. Here, we propose a new goodness-of-fit testing scheme for the marginal distribution of such models. We consider models with an observable (like threshold autoregressions) as well as a latent state process (like Markov regime-switching). The test is based on the Kolmogorov–Smirnov supremum-distance statistic and the concept of the weighted empirical distribution function. The motivation for this research comes from a recent stream of literature in energy economics concerning electricity spot price models. While the existence of distinct regimes in such data is generally unquestionable (due to the supply stack structure), the actual goodness-of-fit of the models requires statistical validation. We illustrate the proposed scheme by testing whether commonly used Markov regime-switching models fit deseasonalized electricity prices from the NEPOOL (US) day-ahead market.

We develop a simple test for deviations from power law tails. Actually, from the tails of any distribution. We use this test – which is based on the asymptotic properties of the empirical distribution function – to answer the question whether great natural disasters, financial crashes or electricity price spikes should be classified as dragon-kings or ‘only’ as black swans.

One of the most profound features of electricity spot prices are the price spikes. Markov regime-switching (MRS) models seem to be a natural candidate for modeling this spiky behavior. However, in the studies published so far, the goodness-of-fit of the proposed models has not been a major focus. While most of the models were elegant, their fit to empirical data has either been not examined thoroughly or the signs of a bad fit ignored. With this paper we want to fill the gap. We calibrate and test a range of MRS models in an attempt to find parsimonious specifications that not only address the main characteristics of electricity prices but are statistically sound as well. We find that the most universal and robust structure is that of an independent spike 3-regime model with heteroscedastic diffusion-type base regime dynamics and shifted spike regime distributions.

In this paper we study asymptotic behavior of a dynamical functional for an α-stable autoregressive fractionally integrated moving average (ARFIMA) process. We find an analytical formula for this important statistics and show its usefulness as a diagnostic tool for ergodic properties. The obtained results point to the very fast convergence of the dynamical functional and show that even for short trajectories one may obtain reliable conclusions on the ergodic properties of the ARFIMA process. Moreover we use the obtained theoretical results to illustrate how the dynamical functional statistics can be used in the verification of the proper model for an analysis of some biophysical experimental data.

The analysis of trajectories recorded in experiments often requires calculating time averages instead of ensemble averages. According to the Boltzmann hypothesis, they are equivalent only under the assumption of ergodicity. In this paper, we implement tools that allow to study ergodic properties. This analysis is conducted in two classes of anomalous diffusion processes: fractional Brownian motion and subordinated Ornstein-Uhlenbeck process. We show that only first of them is ergodic. We demonstrate this by applying rigorous statistical methods: mean square displacement, confidence intervals, and dynamical functional test. Our methodology is universal and can be implemented for analysis of many experimental data not only if a large sample is available but also when there are only few trajectories recorded.

In the last decade Markov regime-switching (MRS) models have been extensively used for modeling the unique behavior of spot prices in wholesale electricity markets. This popularity stems from the models’ relative parsimony and the ability to capture the stylized facts, in particular the mean-reverting character of electricity spot prices, the regime changes implied by fundamentals, and the resulting extreme price spikes. Due to the unobservable switching mechanism, the estimation of MRS models requires inferring model parameters and state process values at the same time. The situation becomes more complicated when the individual regimes are independent from each other and at least one of them is mean-reverting. Statistical validation of such models is also nontrivial. In this paper we review the available techniques and suggest efficient tools for statistical inference of MRS models.

Electromagnetic field (EMF) measurements have limited accuracy, which is additionally impaired by meter self-noise influence. In this paper, a novel noise cancellation method is proposed, based on the Hidden Markov Model (HMM) methodology. It allows us to calculate the overall field intensity with a much higher accuracy than that obtained from other popular approaches, especially when EMF measurements are close to the noise level. The effectiveness of the new method is illustrated on two EMF datasets, one recorded in an urban and the other in a rural area. Its performance is further evaluated in a thorough simulation study using datasets representing the two distinct noisy environments.

An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations, known as electricity price spikes. Improved robustness of the model can be achieved by (a) filtering the data with some reasonable procedure for outlier detection, and then (b) using estimation and testing procedures on the filtered data. In this paper we examine the effects of different treatments of extreme observations on model estimation and on determining the number of spikes (outliers). In particular we compare results for the estimation of the seasonal and stochastic components of electricity spot prices using either the original or filtered data. We find significant evidence for a superior estimation of both the seasonal short-term and long-term components when the data have been treated carefully for outliers. Overall, our findings point out the substantial impact the treatment of extreme observations may have on these issues and, therefore, also on the pricing of electricity derivatives like futures and option contracts. An added value of our study is the ranking of different filtering techniques used in the energy economics literature, suggesting which methods could be and which should not be used for spike identification.

In the last decade Markov-regime switching (MRS) models have been extensively used for modeling the unique behavior of spot prices in wholesale electricity markets. This popularity stems from the models’ relative parsimony and the ability to capture the stylized facts, in particular the mean reverting character of electricity spot prices, the regime changes implied by fundamentals and the resulting extreme price spikes. Due to the unobservable switching mechanism, the estimation of MRS models requires inferring model parameters and state process values at the same time. The situation becomes more complicated when the individual regimes are independent from each other and at least one of them is mean-reverting. Statistical validation of such models is also non-trivial. In this paper we review the available techniques and suggest efficient tools for statistical inference of MRS models.

A loss model or actuarial risk model is a parsimonious mathematical description of the behavior of a collection of risks constituting an insurance portfolio. It is not intended to replace sound actuarial judgment. In fact, according to Willmot (2001), a well formulated model is consistent with and adds to intuition, but cannot and should not replace experience and insight. Moreover, a properly constructed loss model should reflect a balance between simplicity and conformity to the data since overly complex models may be too complicated to be useful.

The classical financial models are based on the standard Brownian diffusion-type processes. However, in exhibition of some real market data (like interest or exchange rates) we observe characteristic periods of constant values. Moreover, in the case of financial data, the assumption of normality is often unsatisfied. In such cases the popular Vasicek model, that is a mathematical system describing the evolution of interest rates based on the Ornstein-Uhlenbeck process, seems not to be applicable. Therefore we propose an alternative approach based on a combination of the popular Ornstein-Uhlenbeck process with a stable distribution and subdiffusion systems that demonstrate such characteristic behavior. The probability density function of the proposed process can be described by a Fokker-Planck type equation and therefore it can be examined as an extension of the basic Ornstein-Uhlenbeck model. In this paper we propose the parameters' estimation method and calibrate the subordinated Vasicek model to the interest rate data.

We calibrate Markov regime-switching (MRS) models to mean daily spot prices from the EEX market. Our empirical study shows that (i) models with shifted spike regime distributions lead to more realistic models of electricity spot prices and that (ii) introducing heteroskedasticity in the base regime leads to better spike identification and goodness-of-fit than in MRS models with the standard mean-reverting, constant volatility dynamics.

In exhibition of many real market data we observe characteristic traps. This behavior is especially noticeable for processes corresponding to stock prices. Till now, such economic systems were analyzed in the following manner: before the further investigation trap-data were removed or omitted and then the conventional methods used. Unfortunately, for many observations this approach seems not to be reasonable therefore we propose an alternative approach based on the subdiffusion models that demonstrate such characteristic behavior and their corresponding probability density function (pdf) is described by the fractional Fokker-Planck equation. In this paper we model market data using subdiffusion with a constant force. We demonstrate properties of the considered systems and propose estimation methods.

The main purpose of the paper is to present, how derivatives valuing methodology, known from financial and commodities markets, can be applied to the electricity market. We compare an application of three recent models. We start with the convenience yield approach, then we analyse the application of the interest rates methodology, proposed by Hinz et al. (2005). Finally, the last approach built by Bjerksund et al (2000) on direct modelling of the forward price dynamics is discussed. We also calibrate the theoretical models to the Nord Pool market data. The empirical analysis shows how these models can be used for evaluation of options prices. Moreover, data study gives an evidence of the seasonal term structure of the returns variance.

A problem of rolling element bearings diagnostics for different machines is widely discussed in the literature. Most of the methods are based on the vibration signal analysis. However for some real signals the classical methods of damage detection are insufficient because of the specific nature of examined data. This specific nature is very often manifested through overlapping, mixing or interleaving of processes with different statistical properties and may be the result of different sources that have influence on the analysed signal. The problem of different sources identification and parametrisation of processes which are related to them is very challenging and requires advanced techniques. There are many methods which can be useful in this context however each signal should be analysed separately and there is no universal technique adequate to all possible time series. In this paper we propose a method of sources identification for vibration signal from the heavy duty crusher used in mineral processing plant. A crusher is a kind of machine which use a metal surface to crumble materials into small fractional pieces. During this process, as well as during entering material stream into the crusher, a lot of impacts appear. They are present in vibration signal acquired from bearings housing. Moreover, for some cases we also observe cyclic impulses which may be related to damage of rolling element bearings in the machine. The proposed sources identification method, especially useful for crushers vibrations, is based on the statistical analysis of examined data. Moreover by using advanced techniques of time series theory we propose a stochastic model that exhibits similar statistical properties as analysed signals. The introduced technique can be a starting point to damage detection of rolling element bearings of copper ore crushers.

Temperature is one of the main parameters describing thermal comfort and indoor air quality. In this paper we propose an approach, based on a modification of the continuous time random walk, to model the indoor air temperature. We perform a statistical analysis of the recorded time series, that allows us to point out the main statistical properties of the recorded variable. The obtained conclusions about the nature of the process lead to a contin- uous time random walk, that in contrast to the classical approach, models time dependence of the jumps distribution. Moreover, we show that the waiting times can be modeled by a tempered stable distribution, which yields a subdiffusive behavior in short times and diffu- sive behavior in longer times. Finally, by conducting a simulation study we illustrate possible applications of the presented approach in the thermal comfort monitoring and forecasting.

In this paper we discuss the calibration of models built on mean-reverting processes combined with Markov regime-switching (MRS). We propose a method that greatly reduces the computational burden induced by the introduction of independent regimes and perform a simulation study to test its efficiency. Our method allows for a 100 to over 1000 times faster calibration than in case of a competing approach utilizing probabilities of the last 10 observations. It is also more general and admits any value of gamma in the base regime dynamics. Since the motivation for this research comes from a recent stream of literature in energy economics, we apply the new method to sample series of electricity spot prices from the German EEX and Australian NSW markets. The proposed MRS models fit these datasets well and replicate the major stylized facts of electricity spot price dynamics.