Volume 26, Issue 3

A new class of multivariate nonlinear quasi-vector autoregressive (QVAR) models is introduced. It is a Markov switching score-driven model with stochastic seasonality for the multivariate t -distribution (MS-Seasonal- t -QVAR). As an extension, we allow for the possibility of having common-trends and nonlinear co-integration. Score-driven nonlinear updates of local level and seasonality are used, which are robust to outliers within each regime. We show that VAR integrated moving average (VARIMA) type filters are special cases of QVAR filters. Using exclusion, sign, and elasticity identification restrictions in MS-Seasonal- t -QVAR with common-trends, we provide short-run and long-run impulse response functions for the global crude oil market.

We propose a structural representation of the correlated unobserved components model, which allows for a structural interpretation of the interactions between trend and cycle shocks. We show that point identification of the full contemporaneous matrix which governs the structural interaction between trends and cycles can be achieved via heteroskedasticity. We develop an efficient Bayesian estimation procedure that breaks the multivariate problem into a recursion of univariate ones. An empirical implementation for the US Phillips curve shows that our model is able to identify the magnitude and direction of spillovers of the trend and cycle components both within-series and between-series.

Unfortunately, time series problems do not appear in data singly. We focus on the joint occurrence of nonstationarity, seasonality and bounded data. Seasonal unit root tests and bounded unit root tests already exist in the literature, yet when all these issues are combined their performance needs improvement. That is why we offer a testing procedure for bounded seasonal unit root processes. The combination of these tests is not straightforward as the nonlinearity coming from bounds causes the limiting distribution of the proposed test statistic to be multivariate Brownian motion while the others have univariate distributions. The simulation exercises reveal that the existing tests, which ignores the presence of bounds or seasonality, suffer significant size problems. Our statistic removes the size distortions and also maintain satisfactory power performance.

The dynamic IS equation implies that if the real interest rate is I(1), then so is the output growth rate with possible cointegration, and log output is I(2). This paper extends the Beveridge–Nelson decomposition to such a case, and develops a Bayesian method to obtain error bands. The method is valid whether log output is I(1) or I(2). The paper applies the method to US data to estimate the natural rates (or their permanent components) and gaps of output, inflation, interest, and unemployment jointly, and finds that allowing for cointegration gives much bigger estimates of the gaps for all variables.

This study investigates the long-run and short-run relationship between consumption, income, financial and housing wealth, and a long-term interest rate for the 50 US states. Using an updated set of quarterly data from 1975 to 2018, we perform panel cointegration analysis allowing for cross-sectional dependence. We obtain the following results. First, there is strong evidence for cointegration among consumption and its determinants. Second, estimates of the housing wealth and financial wealth elasticity of consumption range from 0.072 to 0.115 and 0.044 to 0.080, respectively. Finally, Granger causality tests show that there is a bidirectional short-term causality between per capita consumption, income, and financial wealth in the short run and between all the variables in the long run.

This paper proposes quantile Rogers–Satchell (QRS) measure to ensure robustness to intraday extreme prices. We add an efficient term to correct the downward bias of Rogers–Satchell (RS) measure and provide scaling factors for different interquantile range levels to ensure unbiasedness of QRS. Simulation studies confirm the efficiency of QRS measure relative to the intraday squared returns and RS measures in the presence of extreme prices. To smooth out noises, QRS measures are fitted to the CARR model with different asymmetric mean functions and error distributions. By comparing to two realised volatility measures as proxies for the unobserved true volatility, results from Standard and Poor 500 and Dow Jones Industrial Average indices show that QRS estimates using asymmetric bilinear mean function provide the best in-sample model fit based on two robust loss functions with heavier penalty for under-prediction. These fitted volatilities are then incorporated into return models to capture the heteroskedasticity of returns. Model with a constant mean, Student- t errors and QRS estimates gives the best in-sample fit. Different value-at-risk (VaR) and conditional VaR forecasts are provided based on this best return model. Performance measures including Kupiec test for VaRs are evaluated to confirm the accuracy of the VaR forecasts.

This study analyzes the co-integration relationship between sovereign bonds and credit default swaps (CDS) and then examines the impact of CDS-bond deviation from the relationship on market volatility using the Markov-switching approach. Our empirical sample consists of the daily CDS premium and bond yield spread obtained with the DataStream database for the period from 2008 to 2014. Our empirical results show that the absolute value of the CDS-bond deviation is positively related to the probability of a high volatility regime and negatively related to the probability of a low volatility regime. This result implies a positive association between the CDS-bond deviation and the volatility in the CDS-bond market. Our findings are consistent across mature-market and emerging-market countries. Moreover, the evidence we uncover suggests that the practice of managing default risk of bonds via the use of CDS may increase the interest rate risk of the bond, which implies both wins and woes from the introduction of CDS, particularly for mature-market countries.