scholarly journals Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models

2021 ◽  
Author(s):  
Luan Vo
Keyword(s):  
Signal Processing ◽  
Kalman Filter ◽  
Random Walk ◽  
Systematic Risk ◽  
Time Varying ◽  
Random Coefficient Models ◽  
Piecewise Constant ◽  
Kalman Filter Algorithm ◽  
Empirical Tests ◽  
Piecewise Constant Model

This thesis applies the time-varying signal processing models to track the multifactor systematic risk in the Fama-French model. The mean reverting, random walk and random coefficient models are used to analyze the time-varying multifactor beta based on the multivariate Kalman filter algorithm. The sudden changes in the mutifactor beta ar e captured by the piecewise constant model. Our case studies explain the impacts of economic events on the sudden changes in betas for both individual stocks and industrial portfolios. We propose a new time-varying beta model based on a piecewise mean reverting process to express the effects of different types of events on the multifactor beta.The tracking of the piecewise mean reverting beta, using the modified multivariate Kalman filter with the maximum log likelihood estimator, outperforms the traditional piecewise constant and random walk models as demonstrated in our simulations. The empirical tests indicate that the new model effectively captures the different changes in beta depending on the type of event.

scholarly journals Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models

2021 ◽  
Author(s):  
Luan Vo
Keyword(s):  
Signal Processing ◽  
Kalman Filter ◽  
Random Walk ◽  
Systematic Risk ◽  
Time Varying ◽  
Random Coefficient Models ◽  
Piecewise Constant ◽  
Kalman Filter Algorithm ◽  
Empirical Tests ◽  
Piecewise Constant Model

This thesis applies the time-varying signal processing models to track the multifactor systematic risk in the Fama-French model. The mean reverting, random walk and random coefficient models are used to analyze the time-varying multifactor beta based on the multivariate Kalman filter algorithm. The sudden changes in the mutifactor beta ar e captured by the piecewise constant model. Our case studies explain the impacts of economic events on the sudden changes in betas for both individual stocks and industrial portfolios. We propose a new time-varying beta model based on a piecewise mean reverting process to express the effects of different types of events on the multifactor beta.The tracking of the piecewise mean reverting beta, using the modified multivariate Kalman filter with the maximum log likelihood estimator, outperforms the traditional piecewise constant and random walk models as demonstrated in our simulations. The empirical tests indicate that the new model effectively captures the different changes in beta depending on the type of event.

scholarly journals Piecewise Constant Modeling and Tracking of Systematic Risk in Financial Market

2021 ◽  
Author(s):  
Triloke Rajbhandary
Keyword(s):  
Kalman Filter ◽  
Random Walk ◽  
Capital Market ◽  
Significant Risk ◽  
Systematic Risk ◽  
Efficient Market Hypothesis ◽  
Statistical Hypothesis ◽  
Time Varying ◽  
Statistical Hypothesis Testing ◽  
Piecewise Constant

The objective of this thesis is to study the time-varying systematic risk in capital market represented by beta. By using statistical hypothesis testing, we show that beta changes in a piecewise constant pattern in which the changes are governed by triggering economic events. This pattern of beta is different from previously modeled time-varying patterns in literature, such as random walk and mean-reverting models and is consistent with the efficient market hypothesis. We also present a new modeling technique based on Poisson process to represent piecewise constant beta. We develop a new tracking algorithm based on Kalman Filter in which Bayes' selection criteria is incorporated to track piecewise constant beta. Our simulation results show that our proposed tracking method outperforms the traditional random walk and mean reverting model based Kalman Filter tracking. Our empirical case studies also show that our method is efficient in capturing the significant risk changes which are attributed to economic events.

scholarly journals Piecewise Constant Modeling and Tracking of Systematic Risk in Financial Market

2021 ◽  
Author(s):  
Triloke Rajbhandary
Keyword(s):  
Kalman Filter ◽  
Random Walk ◽  
Capital Market ◽  
Significant Risk ◽  
Systematic Risk ◽  
Efficient Market Hypothesis ◽  
Statistical Hypothesis ◽  
Time Varying ◽  
Statistical Hypothesis Testing ◽  
Piecewise Constant

The objective of this thesis is to study the time-varying systematic risk in capital market represented by beta. By using statistical hypothesis testing, we show that beta changes in a piecewise constant pattern in which the changes are governed by triggering economic events. This pattern of beta is different from previously modeled time-varying patterns in literature, such as random walk and mean-reverting models and is consistent with the efficient market hypothesis. We also present a new modeling technique based on Poisson process to represent piecewise constant beta. We develop a new tracking algorithm based on Kalman Filter in which Bayes' selection criteria is incorporated to track piecewise constant beta. Our simulation results show that our proposed tracking method outperforms the traditional random walk and mean reverting model based Kalman Filter tracking. Our empirical case studies also show that our method is efficient in capturing the significant risk changes which are attributed to economic events.

Evaluating the Observability in the Combination Process of the Height Measurement Signals

2020 ◽  
Vol 10 (4) ◽  
pp. 24-36
Author(s):  
Nguyen Quang Vinh
Keyword(s):  
Signal Processing ◽  
Kalman Filter ◽  
Height Measurement ◽  
Signal Processing Algorithm ◽  
Combination Process ◽  
High Measurement ◽  
Measurement Signal ◽  
Kalman Filter Algorithm ◽  
Channel Combination

In the modern navigation system, the height channel is always the most unstable channel. Combination processing the height measurement signals by using the Kalman filter algorithm can improve the precision of the high measurement. However, in the process of performing the signal processing algorithm by using the Kalman filter, the transition time to obtain the set status is long. Moreover, within different flight conditions, the inertia height meter will be combined with the supporting height meter to get the structure of the combination height meter in order to process the height measurement signals more precisely. In this article, the authors proposed using the criterion for evaluating the observable level to improve the quality of height measurement signal processing. The research results were simulated on three combined high measurements, in which the inertia height meter (IHM) (the basic meter) was combined with one or two supporting height meters (the radio height meter [RHM] and the barometer [AHM]) to show the correctness of the proposed algorithm.

scholarly journals PERAMALAN JUMLAH KUNJUNGAN WISATAWAN AUSTRALIA YANG BERKUNJUNG KE BALI MENGGUNAKAN MODEL TIME VARYING PARAMETER (TVP)

2016 ◽  
Vol 5 (3) ◽  
pp. 117
Author(s):  
I PUTU GEDE DIAN GERRY SUWEDAYANA ◽  
I WAYAN SUMARJAYA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Kalman Filter ◽  
State Space Model ◽  
Percentage Error ◽  
Time Varying ◽  
Mean Square ◽  
Explanatory Variables ◽  
Space Model ◽  
A Value ◽  
Time Varying Parameter ◽  
Kalman Filter Algorithm

The purpose of this research is to forecast the number of Australian tourists arrival to Bali using Time Varying Parameter (TVP) model based on inflation of Indonesia and exchange rate AUD to IDR from January 2010 – December 2015 as explanatory variables. TVP model is specified in a state space model and estimated by Kalman filter algorithm. The result shows that the TVP model can be used to forecast the number of Australian tourists arrival to Bali because it satisfied the assumption that the residuals are distributed normally and the residuals in the measurement and transition equations are not correlated. The estimated TVP model is . This model has a value of mean absolute percentage error (MAPE) is equal to dan root mean square percentage error (RMSPE) is equal to . The number of Australian tourists arrival to Bali for the next five periods is predicted: ; ; ; ; and (January - May 2016).

scholarly journals Control Optimization of Stochastic Systems Based on Adaptive Correction CKF Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
FengJun Hu ◽  
Qian Zhang ◽  
Gang Wu
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Stochastic System ◽  
Stochastic Systems ◽  
System Control ◽  
Time Varying ◽  
Cubature Kalman Filter ◽  
Adaptive Correction ◽  
Kalman Filter Algorithm ◽  
Control Accuracy

Standard cubature Kalman filter (CKF) algorithm has some disadvantages in stochastic system control, such as low control accuracy and poor robustness. This paper proposes a stochastic system control method based on adaptive correction CKF algorithm. Firstly, a nonlinear time-varying discrete stochastic system model with stochastic disturbances is constructed. The control model is established by using the CKF algorithm, the covariance matrix of standard CKF is optimized by square root filter, the adaptive correction of error covariance matrix is realized by adding memory factor to the filter, and the disturbance factors in nonlinear time-varying discrete stochastic systems are eliminated by multistep feedback predictive control strategy, so as to improve the robustness of the algorithm. Simulation results show that the state estimation accuracy of the proposed adaptive cubature Kalman filter algorithm is better than that of the standard cubature Kalman filter algorithm, and the proposed adaptive correction CKF algorithm has good control accuracy and robustness in the UAV control test.

scholarly journals The Attitude Signal Processing of Four Rotor Aircraft based on a Kalman Filter

2015 ◽  
Vol 11 (8) ◽  
pp. 8
Author(s):  
Zhang Tao ◽  
PEI Jin-xin ◽  
SONG Yu-guang ◽  
LUO Peng
Keyword(s):  
Signal Processing ◽  
Kalman Filter ◽  
Aircraft Control ◽  
Filter Model ◽  
Complementary Filter ◽  
Attitude Angle ◽  
System Optimal ◽  
Kalman Filter Algorithm ◽  
The Stability ◽  
And Control

This paper puts forward Kalman filter algorithm to improve the accuracy and the stability of four rotor aircraft control. The algorithm principle is that making use of covariance to estimate the system optimal output.The flight and control theory of four rotor aircraft is first analysed, furthermore, it is found that the attitude angle has the biggest influence to its control. Then, the Kalman filter model is established for processing attitude angle information from gyro and accelerometer sensors. Finally, the algorithm is compared with complementary filter. The flight conditions is simulated in different flight environment, it is shown that Kalman filter algorithm can improve the precision and real time more greatly in attitude angle acquisition.

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