This article explores the fitting of Autoregressive (AR) and Threshold AR (TAR) models with a non-Gaussian error structure. This is motivated by the problem of finding a possible probabilistic model for the realized volatility. A Gamma random error is proposed to cater for the non-negativity of...

This paper examines the nonlinear effect of monetary policy decisions on the performance of the Nigerian Stock Exchange market, by employing the Smooth Transition Autoregressive (STAR) model on monthly data from 2013 M4 to 2019 M12 for All Share Index and monetary policy instrument. This study...

It is well-known that stock prices fluctuate far more than dividends. Traditional valuation methods are not able to depict this fact. In this paper we incorporate excess volatility into a simple DCF model by considering an autoregressive cash flows process with random coefficients. We show that...

A large literature on persistence finds that many modern outcomes strongly reflect characteristics of the same places in the distant past. These studies typically combine unusually high t statistics with severe spatial autocorrelation in residuals, suggesting that some findings may be artefacts...

Commonly used tests to assess evidence for the absence of autocorrelation in a univariate time series or serial cross-correlation between time series rely on procedures whose validity holds for i.i.d. data. When the series are not i.i.d., the size of correlogram and cumulative Ljung-Box tests...

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step...

This paper investigates the properties of tests for asymmetric long-run adjustment which are often applied in empirical studies on asymmetric price transmissions. We show that substantial size distortions are caused by preconditioning the test on finding sufficient evidence for cointegration in...

This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects...

This paper considers estimation and inference concerning the autoregressive coefficient (p) in a panel autoregression for which the degree of persistence in the time dimension is unknown. Our main objective is to construct confidence intervals for p that are asymptotically valid, having...

In this paper we propose a Bayesian estimation approach for a spatial autoregressive logit specification. Our approach relieson recent advances in Bayesian computing, making use of Pólya-Gamma sampling for Bayesian Markov-chain Monte Carlo algorithms.The proposed specification assumes that the...

Present paper proposes an autoregressive time series model to study the behaviour of merger and acquire concept which is equally important as other available theories like structural break, detrending etc. The main motivation behind newly proposed merged autoregressive (M-AR) model is to study...

Exchange rate volatility has been identified as one of the drivers of export diversification. Previous studies have assumed a symmetric relationship between the two variables. However, because volatility could be positive or negative and economic agents react to these changes differently, recent...

In this paper, we develop new threshold cointegration tests with SETAR and MTAR adjustment allowing for the presence of structural breaks in the equilibrium equation. We propose a simple procedure to simultaneously estimate the previously unknown breakpoint and test the null hypothesis of no...

We consider the fractional cointegrated vector autoregressive (CVAR) model of Johansen and Nielsen (2012a) and make two distinct contributions. First, in their con- sistency proof, Johansen and Nielsen (2012a) imposed moment conditions on the errors that depend on the parameter space, such that...

The growth of firms has been shown to have a meaningful impact on the health of firms and the economy in general. As the body of literature dedicated to understanding high-growth firms has expanded, an interest in the persistence of growth has become even more relevant. This is because...

Present paper proposes an autoregressive time series model to study the behaviour of merger and acquire concept which is equally important as other available theories like structural break, detrending etc. The main motivation behind newly proposed merged autoregressive (M-AR) model is to study...