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The purpose of this paper is to investigate the causal link between foreign direct investment (FDI) and exports in China based on the knowledge capital model (KK model, Markusen, 2002).

The bootstrap Granger full-sample and sub-sample rolling window causality test is used to determine whether FDI can promote exports.

The full-sample causality test indicates no causal relationship from FDI to exports. However, considering structural changes of exports and FDI, the authors’ find that the full-sample test is not reliable. Instead, the authors use the rolling window causality test to revisit the dynamic causal relationship, and the results present significant effects from FDI on exports, mostly around periods in which the proportion of FDI from Hong Kong, Macao and Taiwan is increasing. Specifically, positive impacts of FDI on exports are stronger than the negative impacts in China.

The findings in this study suggest a significant time-varying nature of the correlation between FDI and exports. The promotion effect of FDI to exports is proved by the rolling window approach; it thus supports the KK model that divides FDI into lateral FDI and vertical FDI and proves that the constitution of FDI is critical to the relationship between FDI and exports.

China has been facing adjustment of its economic structure in recent years, and in this situation, increasing the proportion of FDI that can bring advanced production function is critical for the industrial structural adjustment.

This paper uses the bootstrap rolling window causality test to investigate the time-varying nature of the causality between FDI and exports, considering structural changes for the first time. The authors further deepen the previous research and draw a more realistic conclusion.

This paper aims to examine the relationship between trade and economic growth using data of UAE economy for the period of 1974-2011.

The bounds testing is applied for testing the cointegration relationship between the variables. The rolling window approach has been used to analyze the stability of long run coefficients.

The empirical analysis shows the presence of cointegration between trade and economic growth. Furthermore, exports have positive, but imports have negative effect on economic growth. The rolling window approach confirms the stability of long-run estimates.

This paper provides new insights for policymakers to use trade as economic tool for sustainable economic development.

This paper makes a unique contribution to the literature with reference to UAE, being a pioneering attempt to investigate the relationship between trade and economic growth by using long time series data and applying more rigorous techniques like time varying rolling window analysis.

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error. We then discuss the Corradi and Swanson (2002) (CS) test of (non)linear out-of-sample Granger causality. Thereafter, we carry out a series of Monte Carlo experiments examining the properties of the CS and a variety of other related predictive accuracy and model selection type tests. Finally, we present the results of an empirical investigation of the marginal predictive content of money for income, in the spirit of Stock and Watson (1989), Swanson (1998) and Amato and Swanson (2001).

We examine the role of structural breaks in forecasting stock return volatility. We begin by testing for structural breaks in the unconditional variance of daily returns for the S&P 500 market index and ten sectoral stock indices for 9/12/1989–1/19/2006 using an iterative cumulative sum of squares procedure. We find evidence of multiple variance breaks in almost all of the return series, indicating that structural breaks are an empirically relevant feature of return volatility. We then undertake an out-of-sample forecasting exercise to analyze how instabilities in unconditional variance affect the forecasting performance of asymmetric volatility models, focusing on procedures that employ a variety of estimation window sizes designed to accommodate potential structural breaks. The exercise demonstrates that structural breaks present important challenges to forecasting stock return volatility. We find that averaging across volatility forecasts generated by individual forecasting models estimated using different window sizes performs well in many cases and appears to offer a useful approach to forecasting stock return volatility in the presence of structural breaks.

This paper aims to ask whether a range of stock market factors contain information that is useful to investors by generating a trading rule based on one-step-ahead forecasts from rolling and recursive regressions.

Using USA data across 3,256 firms, the authors estimate stock returns on a range of factors using both fixed-effects panel and individual regressions. The authors use rolling and recursive approaches to generate time-varying coefficients. Subsequently, the authors generate one-step-ahead forecasts for expected returns, simulate a trading strategy and compare its performance with realised returns.

Results from the panel and individual firm regressions show that an extended Fama-French five-factor model that includes momentum, reversal and quality factors outperform other models. Moreover, rolling based regressions outperform recursive ones in forecasting returns.

The results support notable time-variation in the coefficients on each factor, whilst suggesting that more distant observations, inherent in recursive regressions, do not improve predictive power over more recent observations. Results support the ability of market factors to improve forecast performance over a buy-and-hold strategy.

The results presented here will be of interest to both academics in understanding the dynamics of expected stock returns and investors who seek to improve portfolio performance through highlighting which factors determine stock return movement.

The authors investigate the ability of risk factors to provide accurate forecasts and thus have economic value to investors. The authors conducted a series of moving and expanding window regressions to trace the dynamic movements of the stock returns average response to explanatory factors. The authors use the time-varying parameters to generate one-step-ahead forecasts of expected returns and simulate a trading strategy.

The purpose of this paper is to examine the day-of-the-week effects of Bitcoin (BTC) markets on the exchange level from January 2014 to September 2018.

The in-depth study on the day-of-the-week effects is conducted by using data consisting of Bitcoin prices denominated in 20 fiat currencies from 23 Bitcoin trading exchanges through the method of rolling sample for calendar effect proposed by Zhang et al. (2017).

It is shown by the empirical results that different patterns of the day-of-the-week effects are observed on Bitcoin denominated in various fiat currencies by referring to the price data collected from exchanges. Furthermore, the patterns of the day-of-the-week effects are also available after adjusting Bitcoin prices denominated in domestic currencies into USD.

Because of the discontinuity of data for some daily return series, estimation with dynamic variance is not applicable. It is assumed that the error item follows normal distribution with constant variance.

The day-of-the-week effects are wide-spread in Bitcoin markets, and they are not mainly caused by movements of foreign exchange rates. Actually, empirical findings in this study provide evidence for inefficiency of Bitcoin markets.

The role of trend inflation shocks for the U.S. macroeconomic dynamics is investigated by estimating two DSGE models of the business cycle. Policymakers are assumed to be concerned with a time-varying inflation target, which is modeled as a persistent and stochastic process. The identification of trend inflation shocks (as opposed to a number of alternative innovations) is achieved by exploiting the measure of trend inflation recently proposed by Aruoba and Schorfheide (2011). Our main findings point to a substantial contribution of trend inflation shocks for the volatility of inflation and the policy rate. Such contribution is found to be time dependent and highest during the mid-1970s to mid-1980s.

The purpose of this paper is to report on a sequential three-stage analysis of inflation persistence using monthly data from 11 inflation targeting (IT) countries and, for comparison, the USA, a non-IT country with a history of credible monetary policy.

First, the authors estimate inflation persistence in a rolling-window fractional-integration setting using the semiparametric estimator suggested by Phillips (2007). Second, the authors use tests for unknown structural breaks as a means to identify effects of the regime switch and the global financial crisis on inflation persistence. The authors use the sequences of estimated persistence measures from the first stage as dependent variables in the Bai and Perron (2003) structural break tests. Finally, the authors reapply the Phillips (2007) estimator to the subsamples defined by the breaks.

Four countries (Canada, Iceland, Mexico, and South Korea) experience a structural break in inflation persistence that coincide with the implementation of the IT regime, and three IT countries (Sweden, Switzerland, and the UK), as well as the USA experience a structural break in inflation persistence that coincides with the global financial crisis.

The authors find that in most cases the estimates of inflation persistence switch from mean-reversion nonstationarity to mean-reversion stationarity.

Monetary policy implications differ between pre- and post-global financial crisis.

Global financial crisis affected the persistence of inflation rates.

First paper to consider the effect of the global financial crisis on inflation persistence.

Under the background of the overall increase of China's economic policy uncertainty and the urgent need for the transformation and upgrading of the substantial economy, this paper studies the time-varying causality between China's economic policy uncertainty and the growth of the substantial economy through bootstrap rolling window causality test, further refines economic policies and studies the causal differences between different types of economic policies and substantial economic growth, refining the conclusions of previous studies.

This paper first studies the causal relationship between China's economic policy uncertainty and substantial economic growth in the full sample period through bootstrap Granger causality test. Then, the paper tests the short-term and long-term stability of the parameters of the VAR model, and it is found that the model parameters are unstable in both the short and long term, so the results of the Granger causality test of the full sample are not credible. Finally, we conduct a dynamic test of the causal relationship between China's economic policy uncertainty and substantial economic growth by means of rolling window, so as to comprehensively analyze the dynamic characteristics and sudden changes of the relationship between them.

The research shows that economic policy uncertainty in China has a significant inhibiting effect on the growth of substantial economy. Growth in the substantial economy will drive up economic policy uncertainty before 2016 and restrain it after that. In addition, this paper further subdivides economic policy uncertainty to explore the causal differences between different types of economic policy uncertainty and substantial economic growth. The test results show that the relationship between them has obvious policy heterogeneity. The fiscal policy uncertainty and the monetary policy uncertainty, as the main policy means in China, has a significant impact on the growth rate of substantial economy in multiple ranges, but the effect time is short. Although trade policy uncertainty has a significant impact on the growth rate of substantial economy only during the financial crisis, the effect lasts for a long time. The impact of exchange rate and capital account policy uncertainty on the growth rate of substantial economy is mainly reflected after 2020.

The values of this paper are as follows: First, the economic policy uncertainty is combined with the growth of substantial economy, which makes up the gap of previous studies. Second, the economic policy uncertainty is further subdivided. The paper explores the causal differences between different types of economic policy uncertainties and the growth of substantial economy, so as to make the research more detailed. Finally, different from the previous static analysis, this paper uses dynamic model to examine the relationship between China's economic policy uncertainty and the growth of substantial economy from a dynamic perspective, with richer research conclusions.

The purpose of this paper is to present a discrete quantitative trading strategy to directly control a portfolio's maximum percentage of drawdown losses while trying to maximize the portfolio's long‐term growth rate.

The loss control target is defined through a Rolling Economic Drawdown (REDD) with a constant look‐back time window. The authors specify risk aversion in the power‐law portfolio wealth utility function as the complement of maximum percentage loss limit and assume long‐term stable Sharpe ratios for asset class indexes while updating volatility estimation in dynamic asset allocation implementation.

Over a test period of the past 20 years (1992‐2011), a risk‐based out‐of‐sample dynamic asset allocation among three broad based indexes (equity, fixed income and commodities) and a risk free asset, is robust against variations in capital market expectation inputs, and out‐performs the in‐the‐sample calibrated model and traditional asset allocation significantly.

The current proposal can lead to a new mathematical framework for portfolio selection. Besides investors' liquidity and behavioural constraints, macroeconomic and market cycle, and the potential of central bank interventions following a market crash, could be additionally considered for a more rigorous dynamic asset allocation model.

Besides the benefit of a clear mandate to construct suitable client portfolios, the portfolio approach can be applied to design invest‐able securities, such as principal‐guaranteed investment products, target risk asset allocation ETFs, and target‐date mutual funds with a glide path, etc. The formulation can also be implemented as a managed futures hedge fund portfolio.

The paper introduces the Rolling Economic Drawdown (REDD) concept and specifies risk aversion as the floor of maximum percentage loss tolerance. Dynamic asset allocation is implemented through updating estimation of asset class volatilities.