scholarly journals Research on RMB Exchange Rate Volatility Risk Based on MSGARCH-VaR Model

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaofei Wu ◽  
Shuzhen Zhu ◽  
Junjie Zhou
Keyword(s):  
Exchange Rate ◽  
Value At Risk ◽  
Financial Risk ◽  
Deviance Information Criterion ◽  
Exchange Rate Volatility ◽  
Information Criterion ◽  
Model Parameters ◽  
Volatility Risk ◽  
Rmb Exchange Rate ◽  
Trade Friction

This paper captures the RMB exchange rate volatility using the Markov-switching GARCH (MSGARCH) models and traditional single-regime GARCH models. Through the Markov Chain Monte Carlo (MCMC) method, the model parameters are estimated to study the volatility dynamics of the RMB exchange rate. Furthermore, we compare the MSGARCH models to the single-regime GARCH specifications in terms of Value-at-Risk (VaR) prediction accuracy. According to the Deviance information criterion method, the research shows that MSGARCH models outperform the single-regime specifications in capturing the complexity of RMB exchange rate volatility. After the RMB exchange rate reform in 2015, the volatility is more asymmetric and persistent, and the probability of being in the turbulent volatility regime is significantly increased. The continuous escalation of Sino-US trade friction has increased the VaR of RMB exchange rate log-returns. From the evaluation results of the actual over expected exceedance ratio (AE), the conditional coverage (CC) test, and the dynamic quantile (DQ) test, we find strong evidence that two-regime MSGARCH models could forecast VaR more accurately, which provides practical value for China’s foreign exchange management authorities to manage the financial risk.

Mortality models incorporating long memory for life table estimation: a comprehensive analysis

2021 ◽  
pp. 1-38
Author(s):  
Hongxuan Yan ◽  
Gareth W. Peters ◽  
Jennifer Chan
Keyword(s):  
Time Series ◽  
Life Expectancy ◽  
Long Memory ◽  
Age Groups ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Life Tables ◽  
Mortality Data ◽  
Model Parameters ◽  
Mortality Models

Abstract Mortality projection and forecasting of life expectancy are two important aspects of the study of demography and life insurance modelling. We demonstrate in this work the existence of long memory in mortality data. Furthermore, models incorporating long memory structure provide a new approach to enhance mortality forecasts in terms of accuracy and reliability, which can improve the understanding of mortality. Novel mortality models are developed by extending the Lee–Carter (LC) model for death counts to incorporate a long memory time series structure. To link our extensions to existing actuarial work, we detail the relationship between the classical models of death counts developed under a Generalised Linear Model (GLM) formulation and the extensions we propose that are developed under an extension to the GLM framework known in time series literature as the Generalised Linear Autoregressive Moving Average (GLARMA) regression models. Bayesian inference is applied to estimate the model parameters. The Deviance Information Criterion (DIC) is evaluated to select between different LC model extensions of our proposed models in terms of both in-sample fits and out-of-sample forecasts performance. Furthermore, we compare our new models against existing models structures proposed in the literature when applied to the analysis of death count data sets from 16 countries divided according to genders and age groups. Estimates of mortality rates are applied to calculate life expectancies when constructing life tables. By comparing different life expectancy estimates, results show the LC model without the long memory component may provide underestimates of life expectancy, while the long memory model structure extensions reduce this effect. In summary, it is valuable to investigate how the long memory feature in mortality influences life expectancies in the construction of life tables.

scholarly journals Bayesian Variable Selection Utilizing Posterior Probability Credible Intervals

2021 ◽  
Author(s):  
Mengtian Du ◽  
Stacy L. Andersen ◽  
Thomas T. Perls ◽  
Paola Sebastiani
Keyword(s):  
Variable Selection ◽  
Posterior Probability ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Bayesian Variable Selection ◽  
Model Parameters ◽  
Mixed Effect ◽  
Stochastic Search Variable Selection ◽  
Mixed Effect Models ◽  
Credible Intervals

AbstractIn recent years, there has been growing interest in the problem of model selection in the Bayesian framework. Current approaches include methods based on computing model probabilities such as Stochastic Search Variable Selection (SSVS) and Bayesian LASSO and methods based on model choice criteria, such as the Deviance Information Criterion (DIC). Methods in the first group compute the posterior probabilities of models or model parameters often using a Markov Chain Monte Carlo (MCMC) technique, and select a subset of the variables based on a prespecified threshold on the posterior probability. However, these methods rely heavily on the prior choices of parameters and the results can be highly sensitive when priors are changed. DIC is a Bayesian generalization of the Akaike’s Information Criterion (AIC) that penalizes for large number of parameters, it has the advantage that can be used for selection of mixed effect models but tends to prefer overparameterized models. We propose a novel variable selection algorithm that utilizes the parameters credible intervals to select the variables to be kept in the model. We show in a simulation study and a real-world example that this algorithm on average performs better than DIC and produces more parsimonious models.

Incorporation of bomb-produced14C into fish otoliths. An example of basin-specific rates from the North Pacific Ocean

2015 ◽  
Vol 72 (6) ◽  
pp. 879-892 ◽  
Author(s):  
Stephen G. Wischniowski ◽  
Craig R. Kastelle ◽  
Timothy Loher ◽  
Thomas E. Helser
Keyword(s):  
North Pacific Ocean ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Gulf Of Alaska ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Age Validation ◽  
The North ◽  
Pacific Halibut ◽  
Hippoglossus Stenolepis

Sagittal otoliths from juvenile Pacific halibut (Hippoglossus stenolepis) of known age were used to create a bomb-produced radiocarbon reference chronology for the eastern Bering Sea (EBS) by fitting a coupled-function model to Δ14C values from each specimen’s birth year. The newly created EBS reference chronology was then compared with a reference chronology previously created for Pacific halibut from the Gulf of Alaska (GOA). Adult Pacific halibut age-validation samples from the EBS were also analyzed for14C and modeled to validate age-estimation accuracy. A Bayesian model was developed and Markov chain Monte Carlo simulation was used to estimate model parameters and adult Pacific halibut ageing bias. Differences in reference chronologies between ocean basins were reflected in a large deviance information criterion (ΔDIC) between models, supporting the hypothesis that two separate coupled-function models were required to adequately describe the data, one each for the EBS and GOA. We determined that regionally specific GOA and EBS oceanography plays a considerable role in the Δ14C values and must be taken into consideration when selecting a reference chronology for bomb-produced14C age-validation studies. The age-validation samples indicated that the current ageing methodology used in Pacific halibut assessments is accurate and has provided accurate age assignments for Pacific halibut in the EBS.

scholarly journals Bayesian analysis of periodic asymmetric power GARCH models

2019 ◽  
Vol 24 (4) ◽  
Author(s):  
Abdelhakim Aknouche ◽  
Nacer Demmouche ◽  
Stefanos Dimitrakopoulos ◽  
Nassim Touche
Keyword(s):  
Value At Risk ◽  
Deviance Information Criterion ◽  
Estimation Method ◽  
Real Data ◽  
Information Criterion ◽  
Tail Behavior ◽  
Risk Forecasting ◽  
Asymmetric Power ◽  
Competing Models ◽  
Set Up

AbstractIn this paper, we set up a generalized periodic asymmetric power GARCH (PAP-GARCH) model whose coefficients, power, and innovation distribution are periodic over time. We first study its properties, such as periodic ergodicity, finiteness of moments and tail behavior of the marginal distributions. Then, we develop an MCMC algorithm, based on the Griddy-Gibbs sampler, under various distributions of the innovation term (Gaussian, Student-t, mixed Gaussian-Student-t). To assess our estimation method we conduct volatility and Value-at-Risk forecasting. Our model is compared against other competing models via the Deviance Information Criterion (DIC). The proposed methodology is applied to simulated and real data.

scholarly journals Determination of China’s foreign exchange intervention: evidence from the Yuan/Dollar market

2017 ◽  
Vol 34 (1) ◽  
pp. 62-81 ◽  
Author(s):  
He Li ◽  
Zhixiang Yu ◽  
Chuanjie Zhang ◽  
Zhuang Zhang
Keyword(s):  
Exchange Rate ◽  
Central Bank ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Exchange Rate Regime ◽  
Exchange Rate Volatility ◽  
Rmb Exchange Rate ◽  
Content Type ◽  
Foreign Exchange Intervention ◽  
Basic Model

Purpose The paper aims to investigate the determinants of China’s daily intervention in the foreign exchange market since the 2005 reform aimed at moving the Renminbi (RMB) exchange rate regime towards greater flexibility. Design/methodology/approach The paper uses bivariate probit models to test whether China’s intervention decision is driven by three sets of factors, comprising Model I (basic model), Model II and Model III. Findings Evidence from the models suggests that medium-term Chinese interventions tend to be leaning-against-the-wind, whereas long-term interventions are leaning-with-the-wind. Furthermore, by analyzing exchange rate volatility, this paper finds that intervention is used by the Chinese central bank to ensure that there are no big swings in the RMB exchange rate. Originality/value The paper will be of value to other researchers attempting to understand the policy of the central bank and, in particular, the factors that can lead to interventions during periods of financial crisis.

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