scholarly journals Using Markov switching models to infer dry and rainy periods from telecommunication microwave link signals

2012 ◽  
Vol 5 (7) ◽  
pp. 1847-1859 ◽  
Author(s):  
Z. Wang ◽  
M. Schleiss ◽  
J. Jaffrain ◽  
A. Berne ◽  
J. Rieckermann
Keyword(s):  
Markov Switching ◽  
State Space Model ◽  
Classification Performance ◽  
Markov Switching Model ◽  
Detection Rates ◽  
Switching Algorithm ◽  
Markov Switching Models ◽  
Switching Models ◽  
Set Up ◽  
Microwave Link

Abstract. A Markov switching algorithm is introduced to classify attenuation measurements from telecommunication microwave links into dry and rainy periods. It is based on a simple state-space model and has the advantage of not relying on empirically estimated threshold parameters. The algorithm is applied to data collected using a new and original experimental set-up in the vicinity of Zürich, Switzerland. The false dry and false rain detection rates of the algorithm are evaluated and compared to 3 other algorithms from the literature. The results show that, on average, the Markov switching model outperforms the other algorithms. It is also shown that the classification performance can be further improved if redundant information from multiple channels is used.

scholarly journals Using Markov switching models to infer dry and rainy periods from telecommunication microwave link signals

2012 ◽  
Vol 5 (1) ◽  
pp. 411-445 ◽  
Author(s):  
Z. Wang ◽  
M. Schleiss ◽  
J. Jaffrain ◽  
A. Berne ◽  
J. Rieckermann
Keyword(s):  
Markov Switching ◽  
State Space Model ◽  
Classification Performance ◽  
Markov Switching Model ◽  
Detection Rates ◽  
Switching Algorithm ◽  
Markov Switching Models ◽  
Switching Models ◽  
Set Up ◽  
Microwave Link

Abstract. A Markov switching algorithm is introduced to classify attenuation measurements from telecommunication microwave links into dry and rainy periods. It is based on a simple state-space model and has the advantage of not relying on empirically estimated threshold parameters. The algorithm is applied to data collected using a new and original experimental set-up in the vicinity of Zürich, Switzerland. The false dry and false rain detection rates of the algorithm are evaluated and compared to 3 other algorithms from the literature. The results show that, on average, the Markov switching model outperforms the other algorithms. It is also shown that the classification performance can be further improved if redundant information from multiple channels is used.

scholarly journals Dating US business cycles with macro factors

2016 ◽  
Vol 20 (5) ◽  
Author(s):  
Sebastian Fossati
Keyword(s):  
Markov Switching ◽  
Dynamic Factor ◽  
Markov Switching Model ◽  
Switching Model ◽  
Markov Switching Models ◽  
Us Economy ◽  
Switching Models ◽  
The Us ◽  
Out Of Sample ◽  
Macroeconomic Indicators

AbstractLatent factors estimated from panels of macroeconomic indicators are used to generate recession probabilities for the US economy. The focus is on current (rather than future) business conditions. Two macro factors are considered: (1) a dynamic factor estimated by maximum likelihood from a set of 4 monthly series; (2) the first of eight static factors estimated by principal components using a panel of 102 monthly series. Recession probabilities generated using standard probit, autoregressive probit, and Markov-switching models exhibit very different properties. Overall, a simple Markov-switching model based on the big data macro factor generates the sequence of out-of-sample class predictions that better approximates NBER recession months. Nevertheless, it is shown that the selection of the best performing model depends on the forecaster’s relative tolerance for false positives and false negatives.

scholarly journals Markov Switching

2021 ◽  
Author(s):  
Yong Song ◽  
Tomasz Woźniak
Keyword(s):  
Markov Process ◽  
Time Variation ◽  
Transition Probabilities ◽  
Markov Switching ◽  
The State ◽  
Markov Switching Model ◽  
Property A ◽  
Switching Model ◽  
Markov Switching Models ◽  
Switching Models

Markov switching models are a family of models that introduces time variation in the parameters in the form of their state, or regime-specific values. This time variation is governed by a latent discrete-valued stochastic process with limited memory. More specifically, the current value of the state indicator is determined by the value of the state indicator from the previous period only implying the Markov property. A transition matrix characterizes the properties of the Markov process by determining with what probability each of the states can be visited next period conditionally on the state in the current period. This setup decides on the two main advantages of the Markov switching models: the estimation of the probability of state occurrences in each of the sample periods by using filtering and smoothing methods and the estimation of the state-specific parameters. These two features open the possibility for interpretations of the parameters associated with specific regimes combined with the corresponding regime probabilities. The most commonly applied models from this family are those that presume a finite number of regimes and the exogeneity of the Markov process, which is defined as its independence from the model’s unpredictable innovations. In many such applications, the desired properties of the Markov switching model have been obtained either by imposing appropriate restrictions on transition probabilities or by introducing the time dependence of these probabilities determined by explanatory variables or functions of the state indicator. One of the extensions of this basic specification includes infinite hidden Markov models that provide great flexibility and excellent forecasting performance by allowing the number of states to go to infinity. Another extension, the endogenous Markov switching model, explicitly relates the state indicator to the model’s innovations, making it more interpretable and offering promising avenues for development.

scholarly journals A Markov-Switching VSTOXX Trading Algorithm for Enhancing EUR Stock Portfolio Performance

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1030
Author(s):  
Oscar V. De la Torre-Torres ◽  
Evaristo Galeana-Figueroa ◽  
José Álvarez-García
Keyword(s):  
Markov Switching ◽  
Stock Index ◽  
Portfolio Performance ◽  
Trading Costs ◽  
Weighting Method ◽  
Treasury Bills ◽  
Markov Switching Models ◽  
Switching Models ◽  
Asset Portfolio ◽  
Stock Portfolio

In the present paper, we test the benefit of using Markov-Switching models and volatility futures diversification in a Euro-based stock portfolio. With weekly data of the Eurostoxx 50 (ESTOXX50) stock index, we forecasted the smoothed regime-specific probabilities at T + 1 and used them as the weighting method of a diversified portfolio in ESTOXX50 and ESTOSS50 volatility index (VSTOXX) futures. With the estimated smoothed probabilities from 9 July 2009 to 29 September 2020, we simulated the performance of three theoretical investors who paid different trading costs and invested in ESTOXX50 during calm periods (low volatility regime) or VSTOXX futures and the three-month German treasury bills in distressed or highly distressed periods (high and extreme volatility regimes). Our results suggest that diversification benefits hold in the short-term, but if a given investor manages a two-asset portfolio with ESTOXX50 and our simulated portfolios, the stock portfolio’s performance is enhanced significantly, in the long term, with the presence of trading costs. These results are of use to practitioners for algorithmic and active trading applications in ESTOXX50 ETFs and VSTOXX futures.

scholarly journals Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran

2021 ◽  
Vol 3 (8) ◽  
Author(s):  
Majid Javari
Keyword(s):  
Environmental Planning ◽  
Rainfall Variability ◽  
Markov Switching ◽  
Daily Rainfall ◽  
First Order ◽  
Markov Switching Models ◽  
Markov Transition Matrix ◽  
Switching Models ◽  
Markov Transition ◽  
Modeling And Forecasting

AbstractThis paper represents the recurrence (reoccurrence) changes in the rainfall series using Markov Switching models (MSM). The switching employs a dynamic pattern that allows a linear model to be combined with nonlinearity models a discrete structure. The result is the Markov Switching models (MSM) reoccurrence predicting technique. Markov Switching models (MSM) were employed to analyze rainfall reoccurrence with spatiotemporal regime probabilities. In this study, Markov Switching models (MSM) were used based on the simple exogenous probability frame by identifying a first-order Markov process for the regime probabilities. The Markov transition matrix and regime probabilities were used to analyze the rainfall reoccurrence in 167 synoptic and climatology stations. The analysis results show a low distribution from 0.0 to 0.2 (0–20%) per day spatially from selecting stations, probability mean of daily rainfall recurrence is 0.84, and a different distribution based on the second regime was found to be more remarkable to the rainfall variability. The rainfall reoccurrence in daily rainfall was estimated with relatively low variability and strong reoccurrence daily with ranged from 0.851 to 0.995 (85.1–99.5%) per day based on the spatial distribution. The variability analysis of rainfall in the intermediate and long variability and irregular variability patterns would be helpful for the rainfall variability for environmental planning.

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