Extreme dependence in price transmission analysis

We propose a generalized procedure that combines conventional price transmission analysis with copula-based dynamic tail dependence, to examine price relationships under extreme conditions. This approach is used to examine Ukrainian wheat markets where export restrictions combined with price surges, 2006–2008 and 2010–2012, have contributed to a turbulent market. The results indicate that domestic prices were effectively insulated from world price shocks, but that a ‘rocket and feathers’ price relation held between domestic flour and wheat prices. These asymmetric price co-movements changed with the degree of restrictiveness of the export prohibitions.     

Change-point analysis through integer-valued autoregressive process with application to some COVID-19 data

In this article, we consider the problem of change-point analysis for the count time series data through an integer-valued autoregressive process of order 1 (INAR(1)) with time-varying covariates. These types of features we observe in many real-life scenarios especially in the COVID-19 data sets, where the number of active cases over time starts falling and then again increases. In order to capture those features, we use Poisson INAR(1) process with a time-varying smoothing covariate. By using such model, we can model both the components in the active cases at time-point t namely, (i) number of nonrecovery cases from the previous time-point and (ii) number of new cases at time-point t. We study some theoretical properties of the proposed model along with forecasting. Some simulation studies are performed to study the effectiveness of the proposed method. Finally, we analyze two COVID-19 data sets and compare our proposed model with another PINAR(1) process which has time-varying covariate but no change-point, to demonstrate the overall performance of our proposed model.     

PPP test for Asian countries and regions: new evidence from a wild bootstrap AESTAR test

This article investigates the effects of time-varying variance on the asymmetric exponential smooth transition autoregressive (AESTAR) unit root test. We propose a wild bootstrap-based implementation of the test, which is asymptotically valid under time-varying variance. We apply our proposed method to test the Purchasing Power Parity (PPP) hypothesis for Asian countries and regions, and find that our proposed test provides stronger evidence against the PPP hypothesis than the conventional AESTAR test.     

The Variation of Economic Risk Premiums in Real Estate Returns

We examine the predictable components of returns on stocks, bonds, and real estate investment trusts (REITs). We employ a multiple-beta asset pricing model and find that there are varying degrees of predictability among stocks, bonds, and REITs. Furthermore, we find that most of the predictability of returns is associated with the economic variables employed in the asset pricing model. The stock market risk premium is highly important in capturing the predictable variation in stock portfolios, and the bond market risk premiums (term and risk structure of interest rates) are important in capturing the predictable variation in bond portfolios. For REITs, however, both the stock and bond market risk premiums capture the predictable variation in returns. REITs have comparable return predictability to stock portfolios. We conclude that there is an important economic risk premium for REITs that are not captured by traditional multiple-beta asset pricing models.     

Time varying structural VARs with sign restrictions: The case of Taiwan

I apply a Bayesian approach to a time-varying structural vector autoregression model with stochastic volatility (TVP-SVAR-SV) to study the time-varying nature of the Taiwanese economy. In particular, the structural parameters are identified via the sign information in a three-variable VAR system. The estimated results show that TVP-SVAR-SV model has the best fit to the data, compared to the time-varying parameters VAR model with constant volatility and a classical VAR model with constant parameters and volatilities. Moreover, I find the time-varying contemporaneous relationship between the output growth and inflation rates, particularly significant before the year 2000. Lastly, the impulse responses and the volatilities of all the variables are found to be time-varying.     

Tree networks to assess financial contagion

We propose a two-layered tree network model that decomposes financial contagion into a global component, composed of inter-country contagion effects, and a local component, made up of inter-institutional contagion channels. The model is effectively applied to a database containing time series of daily CDS spreads of major European financial institutions (banks and insurance companies), and reveals the importance of monitoring both channels to assess financial contagion. Our empirical application reveals evidence of a high inter-country and inter-institutional vulnerability at the onset of the global financial crisis in 2008 and during the sovereign crisis in 2011. The results identify France as central to the inter-country contagion in the Euro area during the financial crisis, while Italy dominates during the sovereign crisis. The application of the model to detect contagion between sectors of the European economy reveals similar findings, and identifies the manufacturing sector as the most central, while, at the company level, financial institutions dominate during the 2008 crisis.     

The dynamic effectiveness of monetary policy in China: evidence from a TVP-SV-FAVAR model

We use a broad set of China’s macroeconomic indicators and a dynamic factor model to estimate latent factors of economic output and inflation, which are used to measure the ultimate objectives of monetary policy. The above factors and policy variables are incorporated into a TVP-SV-FAVAR model to investigate the dynamic effectiveness of Chinese monetary policy. Our results confirm that the effects of Chinese monetary policy are time-varying. By comparing the quantity rule with the price rule, we find that the price rule is more effective in managing China’s macro-economy, especially after the financial crisis. Moreover, the results can be regarded as a division of policy rules in a way that different rules are directed at different objectives.     

Oil shocks and stock volatility: new evidence via a Bayesian,graph-based VAR approach

ABSTRACT

This article examines the temporal dependence between three oil shocks and realized volatility in the stock markets of G20 countries between 1994 and 2019. By applying a novel, graphical, Bayesian VAR (BGVAR) model, we calculate unidirectional linkages of oil and stock volatility with a full and segmented sample. The results suggest an overall causality from stock volatility to oil shocks. For certain short, specific periods, the causal direction reverses. Depending on the country and the source of an oil shock, the magnitude and type of the effect can vary considerably. Specific oil-market shocks occur most often in our full sample. In a time-varying structure, oil supply shocks’ impact on stock volatility is more prominent, and net oil-importing countries’ responses to these shocks are greater than for oil-exporting countries. In addition, we find that relationship dynamics can capture market information, such as global economic growth during the 2008–2009 financial crisis.     

Estimating Beta-Coefficients of German Stock Data: A Non-Parametric Approach

Abstract

Although the consumption based asset pricing theory appears to be theoretically superior and more elegant than the beta pricing model, in practice the beta pricing model is more widely applied. Indeed, beta pricing models are one of the most widely adopted tools in financial analysis. They readily allow handling systematic risk as priced in financial assets. However, accurately estimating beta-coefficients is not as straightforward as implicitly suggested by Sharpe's standard market model, i.e. simply using the ordinary least-squares (OLS) regression. This is primarily because beta-coefficients cannot generally be assumed to be stable over time. In order to overcome this deficiency, we present and apply a non-parametric estimation technique that allows capturing this time effect and promises both more reliable estimates than obtained with an OLS regression as well as better manageability compared with the existing time-series approaches dealing with time-varying beta-coefficients. Estimation results for constant and time-varying betas are presented for portfolios of German industries.     

Effect of oil price risk on systematic risk from transportation services industry evidence

This paper investigates the effects of oil price risk on systematic risk using the transportation service industries as samples across eight representative nations. The researchers estimate the systematic risk by the use of time-varying models including the Schwert and Seguin model, the Multi-GARCH model and the Kalman filter algorithm as well as the market model. The empirical results show that the Kalman filter algorithm appears to be the superior model for capturing systematic risk in the transportation industry. The betas of the marine industry decrease as it suffers from oil price risk, while the airline industry sees the reverse. Therefore, the influence of oil price risk is more critical for the airline industry.