scholarly journals Unobserved Components with Stochastic Volatility: Simulation‐based Estimation and Signal Extraction

2021 ◽  
Author(s):  
Mengheng Li ◽  
Siem Jan Koopman
Keyword(s):  
Stochastic Volatility ◽  
Signal Extraction ◽  
Unobserved Components ◽  
Simulation Based

scholarly journals Seasonal adjustment of the Spanish sales daily data

SERIEs ◽  
2021 ◽  
Author(s):  
Ángel Cuevas ◽  
Ramiro Ledo ◽  
Enrique M. Quilis
Keyword(s):  
Value Added ◽  
Signal Extraction ◽  
Economic Conditions ◽  
Seasonal Adjustment ◽  
Unobserved Components ◽  
Spanish Economy ◽  
Short Term ◽  
Daily Data ◽  
Daily Frequency ◽  
Unobserved Components Model

AbstractWe present a procedure to perform seasonal adjustment over daily sales data. The model adjusts daily information from the Immediate Supply of Information System for Value Added Tax declaration forms compiled by the Spanish Tax Agency. The procedure performs signal extraction and forecasting at the daily frequency, by means of an unobserved components model. The daily information allows a permanently updated monitoring of the short-term economic conditions of the Spanish economy.

scholarly journals Signal extraction and the formulation of unobserved components models

2000 ◽  
Vol 3 (1) ◽  
pp. 84-107 ◽  
Author(s):  
Andrew Harvey ◽  
Siem Jan Koopman
Keyword(s):  
Signal Extraction ◽  
Unobserved Components

scholarly journals Mean-Reverting 4/2 Principal Components Model. Financial Applications

Risks ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 141
Author(s):  
Marcos Escobar-Anel ◽  
Zhenxian Gong
Keyword(s):  
Stochastic Volatility ◽  
Value At Risk ◽  
Degrees Of Freedom ◽  
Risk Measures ◽  
Principal Component ◽  
Risk Level ◽  
Investment Strategies ◽  
Risky Assets ◽  
Financial Applications ◽  
Simulation Based

In this paper, we propose a new multivariate mean-reverting model incorporating state-of-the art 4/2 stochastic volatility and a convenient principal component stochastic volatility (PCSV) decomposition for the stochastic covariance. We find a quasi closed-form characteristic function and propose analytic approximations, which aid in the pricing of derivatives and calculation of risk measures. Parameters are estimated on three bivariate series, using a two-stage methodology involving method of moments and least squares. Moreover, a scaling factor is added for extra degrees of freedom to match data features. As an application, we consider investment strategies for a portfolio with two risky assets and a risk-free cash account. We calculate value-at-risk (VaR) values at a 95% risk level using both simulation-based and distribution-based methods. A comparison of these VaR values supports the effectiveness of our approximations and the potential for higher dimensions.

Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008

2018 ◽  
Author(s):  
José Mario Quintana ◽  
Carlos Carvalho ◽  
James Scott ◽  
Thomas Costigliola
Keyword(s):  
Stochastic Volatility ◽  
Particle Filtering ◽  
Financial Time Series ◽  
Sequential Estimation ◽  
Credit Crisis ◽  
Daily Data ◽  
Simulation Based ◽  
Standard And Poor's ◽  
Financial Equity ◽  
Equity Index

This article demonstrates the utility of Bayesian modelling and inference in financial market volatility analysis, using the 2007-2008 credit crisis as a case study. It first describes the applied problem and goal of the Bayesian analysis before introducing the sequential estimation models. It then discusses the simulation-based methodology for inference, including Markov chain Monte Carlo (MCMC) and particle filtering methods for filtering and parameter learning. In the study, Bayesian sequential model choice techniques are used to estimate volatility and volatility dynamics for daily data for the year 2007 for three market indices: the Standard and Poor’s S&P500, the NASDAQ NDX100 and the financial equity index called XLF. Three models of financial time series are estimated: a model with stochastic volatility, a model with stochastic volatility that also incorporates jumps in volatility, and a Garch model.

SIGNAL EXTRACTION IN LONG MEMORY STOCHASTIC VOLATILITY

2014 ◽  
Vol 31 (6) ◽  
pp. 1382-1402 ◽  
Author(s):  
Josu Arteche
Keyword(s):  
Stochastic Volatility ◽  
Long Memory ◽  
Financial Time Series ◽  
Signal Extraction ◽  
Typical Characteristic ◽  
Nonstationary Signals ◽  
Financial Time ◽  
Daily Series ◽  
Parametric Specification ◽  
A New Technique

Long memory in stochastic volatility (LMSV) models are flexible tools for the modeling of persistent dynamic volatility, which is a typical characteristic of financial time series. However, their empirical applicability is limited because of the complications inherent in the estimation of the model and in the extraction of the volatility component. This paper proposes a new technique for volatility extraction, based on a semiparametric version of the optimal Wiener–Kolmogorov filter in the frequency domain. Its main characteristics are its simplicity and generality, because no parametric specification is needed for the volatility component and it remains valid for both stationary and nonstationary signals. The applicability of the proposal is shown in a Monte Carlo and in a daily series of returns from the Dow Jones Industrial index.

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