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This study investigates the impact of corporate bonds issued by Greek listed firms on employment. Even though external financing and the effects on employment has been studied in the literature, we extend the existing literature by focusing for the first time on the specific role of corporate bonds on employment. We have collected all the relevant papers on this line of the literature and concisely report them in a table format and then use them in analyzing our results. Our empirical analysis is based on a panel dataset from 2001 to 2014 and we examine the effect of corporate bonds in the pre and post period of the Greek economic crisis, in which the banking system is vulnerable and unable to provide financing to the firms. The results suggest that corporate bonds have a positive effect on employment in the pre-crisis sample, denoting that firms hire employees and proceed to investment choices. On the contrary, during the recession, corporate bonds have a negative effect on employment. Firms reduce their costs and try to control their debt obligations by issuing corporate bonds.

For a symmetric matrix B, we determine the class of Q such that Q t BQ is non-negative definite and apply it to panel data estimation and forecasting: the Hausman test for testing the endogeneity of the random effects in panel data models. We show that the test can be performed if the estimated error variances in the fixed and random effects models satisfy a specific inequality. If it fails, we discuss the restrictions under which the test can be performed. We show that estimators satisfying the inequality exist. Furthermore, we discuss an application to a constrained quadratic minimization problem with an indefinite objective function.

Forecasting non-stationary time series, especially when the data generating processes contains a random walk component, is a difficult and sometimes impossible task. In this paper we suggest an intuitive, computationally fast and expedient way of forecasting time series of the above type using distance-based nearest neighbours (NN). We exploit to advantage the path and scale dependence present in a random walk model and so we provide a number of theoretical results (a) on the distances used for selecting the NN, (b) on a number of new forecasting models that use these distances and (c) on the properties of the resulting forecasts. We illustrate the efficacy of our method via a comprehensive empirical application on time series of exchange rates and commodities, where we present the resulting performance enhancements and discuss the importance of such results in a decision-making context, linking our forecasting approach with management mathematics and predictive analytics problems.

The literature on mixed-frequency models is relatively recent and has found applications across economics and finance. The standard application in economics considers the use of (usually) monthly variables (e.g. industrial production) for predicting/fitting quarterly variables (e.g. real GDP). This paper proposes a multivariate singular spectrum analysis (MSSA) based method for mixed-frequency interpolation and forecasting, which can be used for any mixed-frequency combination. The novelty of the proposed approach rests on the grounds of simplicity within the MSSA framework. We present our method using a combination of monthly and quarterly series and apply MSSA decomposition and reconstruction to obtain monthly estimates and forecasts for the quarterly series. Our empirical application shows that the suggested approach works well, as it offers forecasting improvements on a dataset of eleven developed countries over the last 50 years. The implications for mixed-frequency modelling and forecasting, and useful extensions of this method, are also discussed.

We introduce a new forecasting methodology, referred to as adaptive learning forecasting, that allows for both forecast averaging and forecast error learning. We analyze its theoretical properties and demonstrate that it provides a priori MSE improvements under certain conditions. The learning rate based on past forecast errors is shown to be non-linear. This methodology is of wide applicability and can provide MSE improvements even for the simplest benchmark models. We illustrate the method’s application using data on agricultural prices for several agricultural products, as well as on real GDP growth for several of the corresponding countries. The time series of agricultural prices are short and show an irregular cyclicality that can be linked to economic performance and productivity, and we consider a variety of forecasting models, both univariate and bivariate, that are linked to output and productivity. Our results support both the efficacy of the new method and the forecastability of agricultural prices.

A new type of momentum based on the signs of past returns is introduced. This momentum is driven primarily by sign dependence, which is positively related to average return and negatively related to return volatility. An empirical application using a universe of commodity and financial futures offers supporting evidence for the existence of such momentum. Investment strategies based on return signal momentum result in higher returns and Sharpe ratios and lower drawdown relative to time series momentum and other benchmark strategies. Overall, return signal momentum can benefit investors as an effective strategy for speculation and hedging.

New results on volatility modeling and forecasting are presented based on the NoVaS transformation approach. Our main contribution is that we extend the NoVaS methodology to modeling and forecasting conditional correlation, thus allowing NoVaS to work in a multivariate setting as well. We present exact results on the use of univariate transformations and on their combination for joint modeling of the conditional correlations: we show how the NoVaS transformed series can be combined and the likelihood function of the product can be expressed explicitly, thus allowing for optimization and correlation modeling. While this keeps the original &ldquo, model-free&rdquo, spirit of NoVaS it also makes the new multivariate NoVaS approach for correlations &ldquo, semi-parametric&rdquo, which is why we introduce an alternative using cross validation. We also present a number of auxiliary results regarding the empirical implementation of NoVaS based on different criteria for distributional matching. We illustrate our findings using simulated and real-world data, and evaluate our methodology in the context of portfolio management.

Highlights • The study contributes to the stream of literature on superforecasting • The superforecasting hypothesis is challenged under real-life scarcity • Superforecasters are identified even when few hundred of experts is available • Superforecasters are identified even with expedited identification • Additional training on structured analogies improves forecasting performance, Superforecasting has drawn the attention of academics - despite earlier contradictory findings in the literature, arguing that humans can consistently and successfully forecast over long periods. It has also enthused practitioners, due to the major implications for improving forecast-driven decision-making. The evidence in support of the superforecasting hypothesis was provided via a 4-year project led by Tetlock and Mellers, which was based on an exhaustive experiment with more than 5000 experts across the globe, resulting in identifying 260 superforecasters. The result, however, jeopardizes the applicability of the proposition, as exciting as it may be for the academic world; if every company in the world needs to rely on the aforementioned 260 experts, then this will end up an impractical and expensive endeavor. Thus, it would make sense to test the superforecasting hypothesis in real-life conditions: when only a small pool of experts is available, and there is limited time to identify the superforecasters. If under these constrained conditions the hypothesis still holds, then many small and medium-sized organizations could identify fast and consequently utilize their own superforecasters. In this study, we provide supportive empirical evidence from an experiment with an initial (small) pool of 314 experts and an identification phase of (just) 9 months. Furthermore - and corroborating to the superforecasting literature, we also find preliminary evidence that even an additional training of just 20 minutes, can influence positively the number of superforecasters identified.

We test and report on time series modelling and forecasting using several US. Leading economic indicators (LEI) as an input to forecasting real US. GDP and the unemployment rate. These time series have been addressed before, but our results are more statistically significant using more recently developed time series modelling techniques and software. In this replication case study, we apply the Hendry and Doornik automatic time series PC-Give (AutoMetrics) methodology to the well-studied macroeconomic series, US. real GDP and the unemployment rate. The Autometrics system substantially reduces regression sum of squares measures relative to traditional variations on the random walk with drift model. The LEI are a statistically significant input to real GDP. A similar conclusion is found for the impact of the LEI and weekly unemployment claims series leading the unemployment rate series. We tested the forecasting ability of best univariate and best bivariate models over 60- and 120-period rolling windows and report considerable forecast error reductions. The adaptive averaging autoregressive model forecast ADA-AR and the adaptive learning forecast, ADL, produced the smallest root-mean-square errors and lowest mean absolute errors. Our results are greatly supportive of the significance for modeling and forecasting of the suggested input variables and they imply considerable improvements over all traditional benchmarks.

We test and report on time series modelling and forecasting using several U.S. Leading Economic Indicators (LEI) as an input to forecasting real U.S. GDP and the unemployment rate. These time series have been addressed before, but our results are more statistically significant using more recently developed time series modelling techniques and software. Montgomery, Zarnowitz, Tsay, and Tiao (1998) modeled the U.S. unemployment rate as a function of the weekly unemployment claims time series, 1948 – 1992. In this replication case study, we apply the Hendry and Doornik automatic time series PC-Give (AutoMetrics) methodology to the well-studied macroeconomics series, U.S. real GDP and the unemployment rate. The Autometrics system substantially reduces regression sum of squares measures relative to traditional variations on the random walk with drift model. The LEI are a statistically significant input to real GDP. A similar conclusion is found for the impact of the LEI and weekly unemployment claims series leading the unemployment rate series. We tested the forecasting ability of best univariate and best bivariate models over 60- and 120-period rolling windows and report considerable forecast error reductions. The adaptive averaging autoregressive model forecast ADA-AR and the adaptive learning forecast, ADL, produced the smallest root mean square errors and lowest mean absolute errors.

This paper is a critical review of the problems of the Greek tax system. A well‐structured tax system promotes allocative efficiency and supports economic growth, whereas on the contrary current Greek reality translates to nontransparency, complexity, and tax corruption: all of them constitute regulatory failure. Using data, which refer to detailed tax information on certain years, starting in 2006 and ending in 2011, and data on the total back taxes owed to the Greek state, we highlight both the problems but more importantly the need to restructure the Greek tax system to finally promote economic growth and not crony capitalism.

This paper investigates robust model rankings in out‐of‐sample, short‐horizon forecasting. We provide strong evidence that rolling window averaging consistently produces robust model rankings while improving the forecasting performance of both individual models and model averaging. The rolling window averaging outperforms the (ex post) “optimal” window forecasts in more than 50% of the times across all rolling windows.

This paper provides ample empirical evidence, using US equity and bond indices, why daily stop-loss rules can be considered as viable performance enhancers. While a longer-term stop-loss rule can help investors to avoid market crashes by being out of the market, investors may obviously lose on the up-market days too. Furthermore, a shorter-term stop-loss rule may not miss the good market days by allowing investors to stay for a longer time in the market at the obvious expense of increased risk and higher drawdowns. This paper illustrates how daily stop-loss rules can significantly outperform the buy and hold equity and bond benchmarks, their equally weighted portfolio and the trend following strategy, simple moving average, which is driven from those asset classes – for both long and short positions. The results are robust to a variety of variations on the initial theme and it’s shown that performance enhancements can come from a variety of other sources related to a static stop-loss rule.

We show that firms with higher NOA (net operating assets) subsequently experience lower stock returns in at least nine out of sixteen European countries, consistent with the U.S. evidence. This negative relation between NOA and future returns is strongly linked with cross-country variation in factors capturing managerial discretion. However, once we adjust for risk, the effect of NOA on stock returns is substantially attenuated and becomes significant only in three European countries. Overall, our findings suggest that optimal investment by executives in response to discount rate changes could be an underlying source of return predictability attributable to NOA in Europe.

Post-war political consensus about the need for government action to rectify market failure began to unravel in the 1970s, and even the need for prudential control of banking and finance began to be challenged by the start of the 1980s. Regulatory oversight was relaxed in the belief that emerging techniques in financial engineering would render irrelevant fears of sharp periodic downturns that have historically been the consequence of lightly regulated finance. The outcome of this new policy, embracing the idea of unregulated markets to deliver greater prosperity, is disappointing. We find that the average growth rate of UK GDP and output per person employed for three decades from the start of liberalization was no greater than that in the previous three post-war decades. Cyclical fluctuations were deeper. A remarkable feature of the second period is the sharp rise in income inequality in favour of the very top earners. An illusion of greater prosperity for a wider segment may have been created in...

The problem of optimal linear filtering, smoothing and trend extraction for m-period differences of processes with a unit root is studied. Such processes arise naturally in economics and finance, in the form of rates of change (price inflation, economic growth, financial returns) and finding an appropriate smoother is thus of immediate practical interest. The filter and resulting smoother are based on the methodology of singular spectrum analysis (SSA). An explicit representation for the asymptotic decomposition of the covariance matrix is obtained. The structure of the impulse and frequency response functions indicates that the optimal filter has a 'permanent' and a 'transitory component', with the corresponding smoother being the sum of two such components. Moreover, a particular form for the extrapolation coefficients that can be used in out-of-sample prediction is proposed. In addition, an explicit representation for the filtering weights in the context of SSA for an arbitrary covariance matrix is derived. This result allows one to examine the specific effects of smoothing in any situation. The theoretical results are illustrated using different datasets, namely US inflation and real GDP growth.

This paper considers the problem of smoothing a non-stationary time series (having either deterministic and/or stochastic trends) using the discrete cosine transform (DCT). The DCT is a powerful tool which has found fruitful applications in filtering and smoothing as it can closely approximate the optimal Karhunen-Loeve transform (KLT). In fact, it is known that it almost corresponds to the KLT for first-order autoregressive processes with a root close to unity: This is the case with most economic and financial time series. A number of new results are derived in the paper: (a) The explicit form of the linear smoother based on the DCT, which is found to have time-varying weights and that uses all observations; (b) the extrapolation of the DCT-smoothed series; (c) the form of the average frequency response function, which is shown to approximate the frequency response of the ideal low pass filter; (d) the asymptotic distribution of the DCT coefficients under the assumptions of deterministic or stochastic trends; (e) two news method for selecting an appropriate degree of smoothing, in general and under the assumptions in (d). These findings are applied and illustrated using several real world economic and financial time series. The results indicate that the DCT-based smoother that is proposed can find many useful applications in economic and financial time series.

This paper proposes a modified version of the widely used price and moving average cross-over trading strategies. The suggested approach (presented in its ‘long only’ version) is a combination of cross-over ‘buy’ signals and a dynamic threshold value which acts as a dynamic trailing stop. The trading behaviour and performance from this modified strategy are different from the standard approach with results showing that, on average, the proposed modification increases the cumulative return and the Sharpe ratio of the investor while exhibiting smaller maximum drawdown and smaller drawdown duration than the standard strategy.

In this study building on earlier work on the properties and performance of the univariate Theta method for a unit root data-generating process we: (a) derive new theoretical formulations for the application of the method on multivariate time series; (b) investigate the conditions for which the multivariate Theta method is expected to forecast better than the univariate one; (c) evaluate through simulations the bivariate form of the method; and (d) evaluate this latter model in real macroeconomic and financial time series. The study provides sufficient empirical evidence to illustrate the suitability of the method for vector forecasting; furthermore it provides the motivation for further investigation of the multivariate Theta method for higher dimensions

In this chapter, we provide an overview on the interlinked problems of high tax rates, tax evasion, tax havens, global tax competition, the enforcement costs of a tax system, and their relationship with economic growth. Reviewing the related literature, it becomes clear that the current state of high tax evasion and high tax rates are detrimental to both the workings of the tax system and economic growth. There is a clear regulatory failure in attempting to battle tax evasion with either higher tax rates or higher enforcement costs—a conclusion that is supported by empirical data from several OECD countries. In addressing this problem, our review and empirical analysis clearly points toward a solution that is based in providing appropriate motives for taxpayers to pay their taxes while not distorting their plans for economic activity. Optimal allocation of resources in an environment of low economic growth and fiscal restraint can easily be achieved by lowering tax rates—it is then, and only then, that other issues of public policy such as equity and redistribution considerations can be meaningfully discussed.

A new type of momentum based on past return signs is introduced, called Returns Signal Momentum. This is mainly driven by sign dependence, which is positively related to average return and negatively related to return volatility. An empirical application using a universe of commodity and financial futures offers supporting evidence to this momentum factor. Investment strategies based on Returns Signal Momentum result in higher returns and Sharpe ratio and lower drawdown when compared to Time Series Momentum and other benchmark strategies. Overall, Returns Signal Momentum can benefit investors as an effective strategy for speculation and hedging.

The study utilizes a new set of macroeconomic and regulatory data to analyze the evolution of loan loss provisioning practices in the Greek banking system over the period 2005–2015. It explores the determinants of the aggregate loan loss reserves to total loans ratio, which reflects the accumulation of provisions net of write-offs and constitutes an important metric of the credit quality of loan portfolios. The empirical results suggest that domestic credit institutions respond relatively quickly to macroeconomic shocks though the latter’s effects on the provisioning behavior of the domestic banking system show significant persistence. Furthermore, the impact of macroeconomic shocks on the loan loss reserves ratio has become stronger (both in terms of magnitude and statistical significance) following the outbreak of the Greek sovereign debt crisis. From a macro policy perspective, this result indicates that a sustainable stabilization of macroeconomic conditions is a key precondition for safeguarding domestic financial stability. For a regulatory standpoint, it suggests that the possibility of macroeconomic regime-related effects on banks’ provisioning policies should be taken into account when macro prudential stress tests of the banking system are designed and implemented.

We empirically examine the potential relationship between the size of the government in Greece—as approached by indirect taxes and subsidies—and economic growth. Our results suggest that a negative relationship does exist between taxes and growth, confirming a large part of the existing literature. We estimate a variety of models that illustrate how and when policymakers can take action in order to boost growth and end the downward spiral in the Greek economy. Tax-based fiscal action arises as the most efficient strategy, targeting the disposable income of the Greek household by reducing taxation and boosting employment. Most importantly, according to our analysis, the government’s budget constraints are not threatened and fiscal prudence is not to be derailed, a prerequisite under the signed memoranda of understanding and bailout programs.