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In line with the COP26 Summit objectives, this paper develops a policy framework to achieve energy transition by considering the social imbalances and regulatory effectiveness. A new energy transition index is proposed. It is an output-side indicator based on the energy ladder hypothesis. This index enables to apprehend the energy transition scenario in any country by capturing (a) the transition to a cleaner energy source, and (b) the transition to more energy efficient sources. Using the two-step System GMM approach and data for 37 OECD countries over the 2000–2019 period, the dynamic and extreme marginal impacts of energy transition drivers with respect to estimates of the model parameters are analyzed. The results show that the social imbalance dampens the positive impacts of energy transition drivers, whereas governance quality helps in augmenting those impacts. The outcomes, drawn from a scenario-based policy design approach, are particularly helpful in advancing potential policy discourse. They have important practical implications for the development of the SDG-oriented policy framework, with special focus on the attainment of the SDG 7 and 13.

This study aims to understand the dynamics of credit and business cycle interactions at the aggregated and disaggregated (sectors and industries) levels in the Indian context. We explore both parametric and non-parametric time-series approach to date the major turning points and calculate the lead-lag measures. We also test for synchronizing credit and business cycles and finding that there is a procyclicality during the crisis period. However, the analysis at sectoral and industry levels exhibit asymmetry as some sectors exhibit countercyclically. The business cycle precedes the credit cycle at the aggregated and disaggregated levels. The repo rate, broad money, real exchange rate, and industrial output significantly explain India's business-credit dynamics.

International audience; We examine the safe haven property of gold for stock and bond markets of G-7 countries. In doing so, we use the novel vector autoregressive for value-at-risk and the cross-quantilogram methods. These quantile-dependence measures help to examine how gold returns react to stock/bond returns when the markets are in a bearish state. The gold market is comparatively less sensitive to bond market innovations and more sensitive to stock market innovations. The tail dependence analysis, through cross-quantilogram, indicates that stock/bond returns significantly and positively spillover to the gold markets when both markets are in a bearish state. Furthermore, the findings of time-varying quantile dependence analysis, obtained by recursive sample estimations, are analogous to the full sample results. Hence, the evidence suggests that gold does not act as a safe haven for the stock and bond markets. Implications of the results are discussed.

Available online 15 January 2019 This paper adopts the Tail-Event driven NETwork (TENET) risk model to assess the systemic risk of Indian banks. Building upon the Value at Risk (VaR), Conditional Value at Risk (CoVaR) and a Single Index Model (SIM) in a generalized quantile regression framework, the results suggest that the Indian banks exhibit high interconnectedness during the crisis period. The results also identify the systemically important banks and explain the banking networks. (C) 2019 Elsevier B.V. All rights reserved. Research and Development, Indian Institute of Technology Kanpur [IITK /HSS /2015086]

First published online: June 2020 This paper evaluates the appropriateness of a Linear Market Model (LMM) which allows for systematic covariance (beta) risk. The performance of LMM will be compared against two extensions, a comparison having yet to be undertaken in the literature. The first extension is the Time-varying Linear Market Model (Tv-LMM) which allows for time-varying systematic covariance risk in the form of a mean reverting state space model via the Kalman filter. The second extension is the multivariate Time-varying Linear Market Model (MTv-LMM) which allows for the time-varying systematic covariance risk of country stock market correlation structure via the multivariate KFMR. The comparison between LMM, Tv-LMM and MTv-LMM, is implemented utilising weekly data collected from several developed and emerging markets for the periods; before and after financial crisis in October 2008, and forecasting 2 years forwards. The empirical findings of that process overwhelmingly support the use of the Multivariate Time-varying Linear Market Model (MTv-LMM) when modelling and forecasting stock market returns, especially for developed stock markets.

This study explores the relationships between changes in the fear index (VIX) and changes in emerging market volatilities i.e., Chinese, Brazilian and the overall emerging volatility index, across their conditional distributions by employing a mixed Quantile regression - Copula methodological approach. Moreover, we analyze whether emerging market volatility indices would respond asymmetrically to positive and negative volatility shocks in the fear index i.e., whether the relationships are asymmetric between the VIX and the emerging market volatilities. Our results confirm that there are strong positive relationships between changes in the VIX and emerging market volatilities, and the linkages tend to be stronger for the upper-parts of the conditional distributions, namely above the median-quantiles up to the extreme-quantiles. In all cases, the nature of the relationship appears to be contemporaneous and on average is three times stronger than their lagged relationship. Further test results reveal that the relationship is highly asymmetric i.e., the effect of a positive shock in the VIX is on average about twice more pronounced than the effect of a negative shock at the extreme-tails of their conditional distributions, a stylized fact that cannot be revealed via conventional estimation methods as OLS. If we compare the effects of positive and negative VIX shocks on emerging market volatilities utilizing QRM, Copulas and OLS, our findings reveal that the effect of a positive shock by the QRM at the 95% quantile is about eight times higher than the one revealed by OLS. An exhaustive robustness analysis is also performed with respect to other volatility measures.

Available online: 09 August 2018 This paper examines the cross-quantile dependence between developed and emerging market stock returns and investigates its time-varying characteristics, using recursive sample estimations. The results based on cross-quantilogram approach reveal a heterogeneous quantile relation for the USA, UK, German, and Japanese stock returns to those of the emerging markets. Systematic risk generally does not explain the cross-country dependence structure, since it remains essentially unchanged when controlling for financial, geopolitical, and economic uncertainties. Moreover, the cross-quantile correlation changes over time, especially in the low and high quantiles, indicating that it is prone to jumps and discontinuities, even in a seemingly stable dependence structure. These results are important for institutional investors and market observers. UAEU UPAR Grant [G00001895] Jan Wallander and Tom Hedelius Foundation

Published online: 09 May 2018 We examine the dependency between the European government bond markets around the recent sovereign debt crisis. A dynamic copula approach is used to model the time-varying dependence structure of those government bond markets, evaluate the nature and strength of their dependencies over time, and gauge the transmission of the crisis shocks. Our results can be summarized as follows: i) the eurozone sovereign bond markets under consideration have a significant and positive dependence with the Greek and the EMU benchmark sovereign bond markets ii) the dynamic-BB7 copula function best describes the dependence structure between these sovereign bond markets and provides evidence of asymmetric tail dependence iii) the conditional probability of crisis transmission from Greece to other eurozone countries is higher than the other way around and iv) Greece is the most vulnerable country when the eurozone entered into the sovereign debt crisis.

We propose an alternative approach to capture the asymmetric risk-return relationship in financial markets using affective cognitive analysis. Implied volatility is employed as a robust gauge of risk perception. Markets exhibit a dramatic increase in fear sentiment when extreme upper-quantile losses hit investors while conditional positive returns fuel exuberance. However, an inverse response is observed in Asian markets due to normative societal phenomena, such as herding. A cognitive paradigm provides with a better interpretation of contagion than classical leverage-feedback theories as risk perception evolves dynamically over time. Overall, the fear of losses is not the flip side of gains' exuberance.

Published online: 17 October 2017 We examine the nonlinear dependence structure and causal nexus between business cycles, stock market returns and asset return volatility for the US economy. We implement two novel methodologies, namely quantile-on-quantile analysis and cross-quantilogram to account for tail dependence and spillovers across quantile ranges. We find evidence of statistically significant spillover effects from extreme equity market returns and their corresponding volatility to specific stages of business cycles. The sensitivity of returns and volatility to business cycle shocks is only evident for extreme quantiles. These findings indicate the importance of modeling the nonlinearity and tail behaviour when analyzing the relationships between equity markets and business cycles. Financial and monetary policy regulators may use the dynamics of spillover predictability and influence between the equity market returns, their volatility and business cycles to exert some degree of control upon business cycle formation and development.

First published online: February 2020 This paper reports the results of a study that investigates the causal interactions among the entities energy consumption, democracy, income, and CO2 emissions in Bangladesh. Bootstrapping causality and time-frequency domain causality methods were adopted to examine the causal co-movements between the variables, using data series for a period of more than four decades. Results show that time-scale behavior plays an important role. Democracy is an important factor for emissions and national income. The nexus of democracy and CO2 emission is bidirectional. The impact of democracy on CO2 is stronger than vice versa. This study provides new insights for policymakers : democratic practices play an important role in implementing climate change policies, at least in the case of Bangladesh.

First published online: January 2020 We examine the major potential drivers of five international housing markets utilizing a quantile regression approach. In particular, we investigate property market dynamics during three variant market environments, namely, under downward (bearish), normal (median), and upward (bullish) trending conditions. Monthly data series for the United States, United Kingdom, Australia, Singapore, and Hong Kong are analysed, in an attempt to quantify uncertainty and detect trading patterns for the largest securitized real estate markets. We find that the stock market volatility, measured by the "pushing factor" VIXS&P500, provides agents with the most reliable and efficient information in terms of predicting market returns during bear market conditions, whereas "pulling factors" such as money supply, treasury yields, and unemployment explain the main stylized facts, incorporating contagion and diverse endogenous and exogenous shocks. Our work provides a richer understanding on comovements in house prices, allowing policy makers to anticipate shocks in global markets in a timely manner.

Several studies have established the predictive power of the yield curve i.e. the difference between long and short-term bond rates and the role of asymmetries in the term structure of bond yields with respect to real economic activity. Using an extensive dataset, comprising 3-month, 1-year, 5-year and 10-year constant maturity Treasury bonds for the Eurozone southern periphery countries – the so-called “PIIGS” – from January 1999 to April 2019, we investigate the links between bond yields of different maturities for the Eurozone southern peripheral countries and we find they co-evolve in line with the predictions of the Expectations Hypothesis theory. We demonstrate the presence of nonlinearities in the term structure, and utilize a multivariate asymmetric two-regime Markov-switching VAR methodology to model them properly. Moreover, we address the economic reasoning behind the introduction of an equilibrium-correction regime-switching approach, hence providing potentially important insights on the behaviour of the entire yield curve. We reveal that the regime shifts are related to the state of the business cycle, particularly in economies in which monetary policy decisions are implemented via changes in short-term rates as a response to deviations of output from equilibrium levels. Our results may have important statistical and economic implications on the behaviour of the term structure of bond yields.

We explore the impact of uncertainty on financial markets in the aftermath of the global financial crisis. In particular, we investigate the temporal dynamics of the dependence structure of stock, ...

Received 4 October 2014, Accepted 20 December 2015, Available online 29 December 2015 We investigate the time-scale relationships between US equity and commodity markets. The empirical evidence from the risk-return profitability analysis based on the wavelet coherence measure shows that equity and commodity markets exhibit time-varying co-movement patterns and behave differently across investment horizons. Moreover, we find evidence of time-frequency causality between the two investigated markets. Our results can have important implications for optimal asset allocation and portfolio diversification.

Available online 8 June 2019 We explore the robustness, efficiency and accuracy of the multi-scale forecasting in crude oil markets. We adopt a novel hybrid wavelet auto-ARMA model to detect the inherent nonlinear dynamics of crude oil returns with an explicitly defined hierarchical structure. Entropic estimation is employed to calculate the optimal level of the decomposition. The wavelet-based forecasting method accounts for the chaotic behavior of oil series, whilst captures drifts, spikes and other non-stationary effects which common frequency-domain methods miss out completely. These results shed new light upon the predictability of crude oil markets in nonstationary settings. (C) 2019 Elsevier B.V. All rights reserved. Jan Wallander Foundation Tom Hedelius Foundation Siamon Foundation

Published online: 27 May 2019 We examine spillover and its determinants among Eurozone sector level credit markets using time and frequency domain spillover approaches. Based on network theory and connectedness analysis, we identify the sectors that are major transmitters and receivers of spillover during normal and crisis periods. The rolling window analysis shows that short-run spillover among credit market sectors intensifies during global and Eurozone crisis periods. Further, using Bayesian model averaging, we find that overall financial conditions and stock market volatility are the main drivers of total and sector-level spillover. Our findings have important implications for policymakers and investors interested in Euro-area credit risk at the sector level.

We examine the interactions between commodity futures returns and five driving factors (financial speculation, exchange rate, stock market dynamics, implied volatility for the US equity market, and economic policy uncertainty). Nonlinear causality tests are implemented after controlling for cointegration and conditional heteroscedasticity in the data over the period May 1990 – April 2014. Our results show strong evidence of unidirectional linear causality from commodity returns to excess speculation for the majority of the considered commodities, in particular for agriculture commodities. This evidence casts doubt on the claim that speculation is driving food prices. We also find unidirectional linear causality from energy futures markets to exchange rates and strong evidence of nonlinear causal dependence between commodity futures returns, on the one hand, and stock market returns and implied volatility, on the other hand. Overall, the new evidence found in this paper can be utilized for policy and investment decision-making.

First published: 08 March 2018 In cross-section studies, if the dependent variable is I(0) but the regressor is I(1), the true slope must be zero in the resulting unbalanced regression. A spuriously significant relationship may be found in large cross-sections, however, if the integrated regressor is related to a stationary variable that enters the DGP but is omitted from the regression. The solution is to search for the related stationary variable, in some cases the first difference of the integrated regressor, in other cases, a categorical variable that can take on limited number of values which depend on the integrated variable. We present an extensive survey, new developments, and applications particularly in finance. EU Horizon 2020 research and innovation programme under the MS-C grant [656136]

Available online: 08 February 2018 This paper incorporates anticipated and unanticipated shocks to bank capital into a DSGE model with a banking sector. We apply this model to study Basel III countercyclical capital requirements and their implications for banking stability and household welfare. We introduce three different countercyclical capital rules. The first countercyclical capital rule responds to credit to output ratio. The second countercyclical rule reacts to deviations of credit to its steady state, and the third rule reacts to credit growth. The second rule proves to be the most effective tool in dampening credit supply, housing demand and household debt as well as in enhancing the banking stability by ensuring that banks have higher bank capital and capital to asset ratio. After conducting a welfare analysis we find that the second rule outranks the other ones followed by the first rule, the baseline and the third rule respectively in terms of welfare accumulation. Jan Wallanders and Tom Hedelius Foundation

We model the downside and upside spillover effects, systemic and tail dependence risks of the DJ World Islamic (DJWI) and DJ World Islamic Financial (DJWIF) indices, and of Islamic equity indices from Japan, USA and the UK. We draw our empirical results and conclusions by implementing a robust modeling framework consisting of Value-at-Risk (VaR), conditional VaR (CoVaR), Delta conditional VaR (ΔCoVaR), canonical vine conditional VaR (c-vine CoVaR), and time-varying and static bivariate and vine copula models. Full sample estimations indicate larger downside spillover effects and systemic risk for the DJ Islamic Financials World and USA Islamic indices, while Islamic indices from Japan and the DJ World financials have greater exposure to upside spillover risk effects. During the financial crisis the USA and UK Islamic indices display higher downside systemic risk; and the strongest negative tail asymmetric dependence occurs between the DJ Islamic Financials World, and the Islamic indices from Japan and the DJ World financials. Implications of the results are discussed.

We examine the spillovers of the US subprime crisis to Asian and European economies and in particular to what extent currency and stock markets have been affected by the crisis. Linear and non-linear dependencies are detected after pairwise and system-wise causality analysis. A new stepwise multivariate filtering approach is implemented after controlling for conditional heteroskedasticity in the raw data and in VAR/VECM residuals using multivariate GARCH models. Significant nonlinear causal linkages persisted even after the application of GARCH-BEKK, CCC-GARCH and DCC-GARCH modelling. This indicates that volatility effects might partly induce nonlinear causality. Perhaps new short-term asset-pricing models could be developed to explain this stylized fact. These results might also have important implications for hedging, trading strategies and financial market regulation. Published version of EUI ECO WP 2011/22

Even though the global contagion effects of the financial crisis have been well documented, the transmission mechanism as well as the nature of the volatility spillovers among the US, the EU and the BRIC markets has not been systematically investigated. To examine the dynamic linear and nonlinear causal linkages a stepwise filtering methodology is introduced, for which vector autoregressions and various multivariate GARCH representations are adopted. The sample covers the after-Euro period and includes the financial crisis and the Eurozone debt crisis. The empirical results show that the BRICs have become more internationally integrated after the US financial crisis and contagion is further substantiated. Moreover, no consistent evidence in support of the “decoupling” view is found. Some nonlinear causal links persist after filtering during the examined period. This indicates that nonlinear causality can, to a large extent, be explained by simple volatility effects, although tail dependency and higher-moments may be significant factors of the remaining interdependencies.

Available online: 20 February 2017 There is evidence to suggest that gold acts as both a hedge and a safe haven for equity markets over recent years, and particularly during crises periods. Our work extends the recent literature on hedging and diversification roles of gold by analyzing its interaction with the stock markets of the leading emerging economies, the BRICS. While they generally exhibit a high growth rate, these economies still experience a pronounced vulnerability to external shocks, particularly to commodity price fluctuations. Using a multi-scale wavelet approach and a GARCH-based copula methodology, we mainly show evidence of: (i) the time-scale co-evolvement patterns between BRICS stock markets and gold market, with some profound regions of concentrated extreme variations; and (ii) a strong time-varying asymmetric dependence structure between those markets. These findings are essential for risk diversification and portfolio hedging strategies among the investigated markets.

Published online: 22 July 2017 This paper aims to investigate herding behavior and its impact on volatility under uncertainty. We apply a cross-sectional absolute deviation approach as well as Quantile Regression methods to capture the herding behavior in daily and monthly frequencies in US markets over several time-periods including the global financial crisis. In a novel attempt we modify the empirical CSAD herding modeling by introducing implied volatility as a measure of agent risk expectations. Our findings indicate that herding tends to be intense under extreme market conditions, as depicted in the upper high quantile range of the conditional distribution of returns. During crisis periods herding is observed at the beginning of the crisis and becomes insignificant towards the end. The US market herding behavior exhibits time-varying dynamic trading patterns that can be attributed e.g., to overconfidence or excessive “flight to quality” features, mostly observed in the aftermath of the global financial crisis. Moreover, implied volatility reveals asymmetric patterns and plays a key role in enforcing irrational behavior.

We examine the relationship between money supply growth and inflation in 3 Asian Economies which are India, Malaysia and Japan using a time-frequency approach. The application of a unified multi-scale analysis allows us to provide a continuous assessment of the link between money supply growth and inflation, unlike most of the existing literature studying this relationship. We also employ a bivariate frequency-domain causality test to determine the nature and direction of interdependence between money supply growth and inflation dynamics. Our findings provide a better understanding of their lead-lag linkages and causal relationship in the selected countries of the Asia-Pacific region.

We emphasize the role of news-based economic policy and equity market uncertainty indices as robust drivers of oil price fluctuations. In that, we utilize a new hybrid nonparametric quantile causality methodology in order to investigate whether EPU and EMU uncertainty measures incorporate critical predictability for oil market returns and volatility. Based on an updated daily database spanning January 1986 to December 2014, we find that both measures present strong predictability over the entire distribution of oil around the median, yet more importantly for volatility forecastability covers the entire distribution except minor divergences in the tails. Therefore, an inherent heterogeneity is observed and an asymmetric pattern over the distribution of oil returns and its volatility exists with respect to uncertainty predictability.

The present study addresses the learning mechanism of boundedly rational agents in the dynamic and noisy environment of financial markets. The main objective is the development of a system that “decodes” the knowledge-acquisition strategy and the decision-making process of technical analysts called “chartists”. It advances the literature on heterogeneous learning in speculative markets by introducing a trading system wherein market environment and agent beliefs are represented by fuzzy inference rules. The resulting functionality leads to the derivation of the parameters of the fuzzy rules by means of adaptive training. In technical terms, it expands the literature that has utilized Actor–Critic reinforcement learning and fuzzy systems in agent-based applications, by presenting an adaptive fuzzy reinforcement learning approach that provides with accurate and prompt identification of market turning points and thus higher predictability. The purpose of this paper is to illustrate this concretely through a comparative investigation against other well-established models. The results indicate that with the inclusion of transaction costs, the profitability of the novel system in case of NASDAQ Composite, FTSE100 and NIKKEI255 indices is consistently superior to that of a Recurrent Neural Network, a Markov-switching model and a Buy and Hold strategy. Overall, the proposed system via the reinforcement learning mechanism, the fuzzy rule-based state space modeling and the adaptive action selection policy, leads to superior predictions upon the direction-of-change of the market.

There is substantial evidence that inflation rates are characterized by long memory and nonlinearities. In this paper, we introduce a long-memory Smooth Transition AutoRegressive Fractionally Integrated Moving Average-Markov Switching Multifractal specification [ STARFIMA ( p , d , q ) - MSM ( k ) ] for modeling and forecasting inflation uncertainty. We first provide the statistical properties of the process and investigate the finite sample properties of the maximum likelihood estimators through simulation. Second, we evaluate the out-of-sample forecast performance of the model in forecasting inflation uncertainty in the G7 countries. Our empirical analysis demonstrates the superiority of the new model over the alternative STARFIMA ( p , d , q ) - GARCH -type models in forecasting inflation uncertainty.

In this paper we study the dependence structure of extreme realization of returns between seven Asia-Pacific stock markets and the U.S. Methodologically we apply the multivariate extreme value theory that best suits to the problem under investigation. The evidence we obtain indicates that extreme correlations are not substantially different from the unconditional ones or from those obtained from multivariate GARCH models. A clustering analysis shows that the Asia-Pacific countries do not belong to a distinct block of countries on the basis of the extreme correlations we have estimated. The policy implications of our study are that the benefits from portfolio diversification with assets from the Asia-Pacific stock markets are not eroded during crisis periods, in the sense that no correlation breakdown has been observed.

The present study investigates the long-term linear and nonlinear causal linkages among six currencies, namely EUR/USD, GBP/USD, USD/JPY, USD/CHF, AUD/USD and USD/CAD. The prime motivation for choosing these exchange rates comes from the fact that they are the most liquid and widely traded, covering about 90% of total FX trading worldwide. The data spans two periods (PI: 3/20/1991 \u2013 3/20/1997, PII: 3/20/2003 \u2013 3/20/2007) before and after the structural break of the Asian financial crisis, which set a platform for departure for causality testing. We apply a new nonparametric test for Granger non-causality by Diks and Panchenko (2005, 2006) as well as the conventional linear Granger test on the return time series. To ensure that any causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of pairwise VAR filtered residuals as well as in a six-variate formulation. We find remaining significant bi- and uni-directional causal nonlinear relationships in the return series. Finally, we investigate the hypothesis of nonlinear non-causality after controlling for conditional heteroskedasticity in the data using a GARCH-BEKK model. Our approach allows the entire variance-covariance structure of the currency interrelationship to be incorporated in order to explicitly capture the volatility spillover mechanism. Whilst the nonparametric test statistics are smaller in some cases, significant nonlinear causal linkages persisted even after GARCH filtering during both the pre- and post-Asian crisis period. This indicates that currency returns may exhibit asymmetries and statistically significant higher-order moments.

This paper uses a k-th order nonparametric Granger causality test to analyze whether firm-level, economic policy and macroeconomic uncertainty indicators predict movements in real stock returns and their volatility. Linear Granger causality tests show that whilst economic policy and macroeconomic uncertainty indices can predict stock returns, firm-level uncertainty measures possess no predictability. However, given the existence of structural breaks and inherent nonlinearities in the series, we employ a nonparametric causality methodology, as linear modeling leads to misspecifications thus the results cannot be considered reliable. The nonparametric test reveals that in fact no predictability can be observed for the various measures of uncertainty i.e., firm-level, macroeconomic and economic policy uncertainty, vis-a-vis real stock returns. In turn, a profound causal predictability is demonstrated for the volatility series, with the exception of firm-level uncertainty. Overall our results not only emphasize the role of economic and firm-level uncertainty measures in predicting the volatility of stock returns, but also presage against using linear models which are likely to suffer from misspecification in the presence of parameter instability and nonlinear spillover effects.

This paper assesses the presence of linear and nonlinear dynamic causal relations among the stock markets of the Eurozone peripheral countries, namely Portugal, Ireland, Italy, Greece and Spain (PIIGS countries). In addition to the pairwise analysis, this paper tests causality in a multivariate setting to take into account the effects of all variables by using a three-step multivariate filtering procedure. The paper also investigates the existence of nonlinear causal linkages of VAR/VECM filtered residuals to verify whether the observed causalities are strictly nonlinear and controls for conditional heteroskedasticity using a multivariate Dynamic Conditional Correlation (DCC) GARCH model to test the hypothesis of nonlinear noncausality. Most of the nonlinear causal linkages were purged after multivariate GARCH filtering, a fact which indicates that volatility effects primarily induce nonlinear causality. Yet in some cases that nonlinear links pertained, possibly peripheral stock returns may exhibit statistically significant higher-order moments. Our results may be useful in explaining a significant part of the (non)predictability of peripheral stock markets but more importantly in quantifying the process of financial markets and banking integration in the Eurozone.

Published online: 05 Jan 2016 The popular sentiment-based investor index SBW introduced by Baker and Wurgler (2006, 2007) is shown to have no predictive ability for stock returns. However, Huang et al. (2015) developed a new investor sentiment index, SPLS, which can predict monthly stock returns based on a linear framework. However, the linear model may lead to misspecification and lack of robustness. We provide statistical evidence that the relationship between stock returns, SBW and SPLS is characterized by structural instability and inherent nonlinearity. Given this, using a nonparametric causality approach, we show that neither SBW nor SPLS predicts stock market returns or even its volatility, as opposed to previous empirical evidence.

Until recently economists focused on structural models that were constrained by a lack of high-frequency data and theoretical deficiencies. Little academic research has been invested in actually trying to build successful real-time trading models for the high-frequency foreign exchange market, which is characterized by inherent complexity and heterogeneity. The present work opens new directions for inference on market efficiency in an attempt to account for the use of technical analysis by practitioners over many years now. This paper presents a heuristic model that efficiently emulates the dynamic learning of intraday traders. The proposed setup incorporates agent beliefs, preferences and expectations while it integrates the calibration of technical rules by means of adaptive training. The study focuses on EUR/USD which is the most liquid and widely traded currency pair. The data consist of a very large tick-by-tick sample of bid and ask prices covering many trading periods to enhance robustness in the results. The efficiency of a technical trading strategy based on the proposed model is investigated in terms of directional predictability. The heuristic learning system is compared against many non-linear models, a random walk and a buy & hold strategy. Based on statistical testing it is shown that, with the inclusion of transaction costs, the profitability of the new model is consistently superior. These findings provide evidence of technical predictability under incomplete information and can be justified by invoking the existence of heterogeneity caused by many factors affecting market microstructure. Overall, the results suggest that the proposed model can be used to improve upon traditional technical analysis approaches.

The introduction of Euro currency was a game-changing event intended to induce convergence of Eurozone business cycles on the basis of greater monetary and fiscal integration. The benefit of participating into a common currency area exceeds the cost of losing autonomy in national monetary policy only in case of cycle co-movement. However, synchronization was put back mainly due to country-specific differences and asymmetries in terms of trade and fiscal policies that became profound at the outset of the global financial crisis. As opposed to previous studies that are mostly based on linear correlation or causality modeling, we utilize the cross-wavelet coherence measure to detect and identify the scale-dependent time-varying (de)synchronization effects amongst Eurozone and the broad Euro area business cycles before and after the financial crisis. Our results suggest that the enforcement of an active monetary policy by the ECB during crisis periods could provide an effective stabilization instrument for the entire Euro area. However, as dynamic patterns in the lead-lag relationships of the European economies are revealed, (de)synchronization varies across different frequency bands and time horizons.

We focus on the interaction of frictions both at the firm level and in the banking sector in order to examine the transmission mechanism of the shocks and to reflect on the response of the monetary policy to increases in interest rate spreads, using DSGE models with financial frictions. However, VAR models are linear and the solutions of DSGEs are often linear approximations; hence they do not consider time variation in parameters that could account for inherent nonlinearities and capture the adaptive underlying structure of the economy, especially in crisis periods. A novel method for time-varying VAR models is introduced. As an extension to the standard homoskedastic TVP-VAR, we employ a Markov-switching heteroskedastic error structure. Overall, we conduct a comparative empirical analysis of the out-of-sample performance of simple and hybrid DSGE models against standard VARs, BVARs, FAVARs, and TVP-VARs, using data sets from the U.S. economy. We apply advanced Bayesian and quasi-optimal filtering techniques in estimating and forecasting the models. European Commission - Seventh Framework Programme (FP7) "Dote ricercatori": FSE, Regione Lombardia

Information on economic policy uncertainty (EPU) does matter in predicting oil returns especially when accounting for omitted nonlinearities in the relationship between these two variables via a time-varying coe¢ cient approach. In this work, we compare the forecastability of standard, Bayesian and TVP-VAR models against the random-walk and benchmark AR models. Our results indicate that over the period 1900:1-2014:2 the time-varying VAR model with stochastic volatility outranks all alternative models.

First published online: 3 August 2015 This study proposes an integrated framework to model and estimate relatively large dependence matrices using pair vine copulas and minimum risk optimal portfolios with respect to five risk measures within the context of the global financial crisis. We apply this methodology to two 20-asset mining (gold and iron ore-nickel) sector portfolios from the Australian Securities Exchange. The pair vine copulas prove to be powerful tools for the modeling of changing dependence risk under three different period scenarios combined with the optimization of portfolios that have complex patterns of dependence. The portfolio optimization results converge, on average, in some stocks.

Recent empirical evidence based on a linear framework tends to suggest that a Markov-switching version of the consumption-aggregate wealth ratio ( cay M S ), developed to account for structural breaks, is a better predictor of stock returns than the conventional measure (cay)—a finding we confirm as well. Using quarterly data over 1952:Q1–2013:Q3, we however provide statistical evidence that the relationship between stock returns and cay or cay M S is in fact nonlinear. Then, given this evidence of nonlinearity, using a nonparametric Granger causality test, we show that it is in fact cay and not cay M S which is a stronger predictor of not only stock returns, but also volatility.

Although policymakers and practitioners are particularly interested in dynamic stochastic general equilibrium (DSGE) models, these are typically too stylized to be applied directly to the data and often yield weak prediction results. Very recently, hybrid DSGE models have become popular for dealing with some of the model misspecifications. Major advances in estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. In this study we introduce a Bayesian approach to estimate a novel factor augmented DSGE model that extends the model of Consolo et al. [Consolo, A., Favero, C.A., and Paccagnini, A., 2009. On the Statistical Identification of DSGE Models. Journal of Econometrics, 150, 99–115]. We perform a comparative predictive evaluation of point and density forecasts for many different specifications of estimated DSGE models and various classes of VAR models, using datasets from the US economy including real-time data. Simple and hybrid DSGE models are implemented, such as DSGE-VAR and tested against standard, Bayesian and factor augmented VARs. The results can be useful for macro-forecasting and monetary policy analysis. European Commission - Seventh Framework Programme (FP7) Dote ricercatori

The present study builds upon the seminal work of Engel and West (2005) and in particular on the relationship between exchange rates and fundamentals. The paper discusses the well-known puzzle that fundamental variables such as money supply and interest rate, provide help in predicting changes in floating rates. It also tests the theoretical result of Engel and West (2005) that in a rational expectations present-value model, the asset price manifests near-random walk behaviour if the fundamentals are I (1) and the factor for discounting future fundamentals is near one. The study explores the direction and nature of causal interdependencies among the most widely traded currencies in the world, their country-specific fundamentals and their US-differentials. A new VAR/VECM-GARCH multivariate filtering approach is implemented, whilst linear and nonlinear non-causality is tested and validated on simulated and empirical data. The evidence implies that there is no indication of a prevailing causal behaviour and when nonlinear effects are accounted for, the pattern of leads and lags changes over time. The variations in this linkage can be attributed to parameter instability in structural models, which may have little effect on FX forecastability over short horizons, yet it is significant for long-run cointegrating relationships. While it is difficult to reconcile these findings with the Rational Expectations hypothesis, the theory of Consistent Expectations or Bounded Rationality matches simulation and empirical findings more efficiently. Overall, fundamentals may be important determinants of FX rates, however there may be some other unobservable variables driving the currency rates that current asset-pricing models have not yet captured.

Standard VAR and Bayesian VAR models are proven to be reliable tools for modeling and forecasting, yet they are still linear and they do not consider time-variation in parameters. VAR modeling is subject to the Lucas critique and fails to take into account the inherent nonlinearities of the economy, while it can only be utilized in the analysis of stationary series and in many cases stationarity assumptions are too restrictive. A novel time-varying multivariate state-space estimation method for vector autoregression models is introduced. For the time-varying parameter model (TVP-VAR), the parameters are estimated using a multivariate specification of the standard Kalman filter (Harvey, 1990) combined with a suitable extension of the univariate methodology framework of Kim and Nelson (1999). The TVP-VAR model as well as standard VARs and Bayesian VARs, are used in a comparative investigation of their predicting performance for the monthly IP, CPI and Euribor rate of the EU economy. The total period covers 1999:1–2011:2 with an out-of-sample testing period of 2007:2 to 2011:2, which included the US sub-prime and the EU debt crisis sub-periods. The results varied across the investigated time series and indicated that the TVP-VAR model consistently outperforms the other models in case of the EU monthly CPI, while different specifications of the VAR and BVAR models for the IP and Euribor series provide with better forecasting performance. Interestingly, the robustness analysis showed that the TVP-VAR model provided with enhanced predictability in particular during “crisis times”.

Over the last few years, there has been a growing interest in DSGE modelling for predicting macroeconomic fluctuations and conducting quantitative policy analysis. Hybrid DSGE models have become popular for dealing with some of the DSGE misspecifications as they are able to solve the trade-off between theoretical coherence and empirical fit. However, these models are still linear and they do not consider time variation for parameters. The time-varying properties in VAR or DSGE models capture the inherent nonlinearities and the adaptive underlying structure of the economy in a robust manner. In this article, we present a state-space time-varying parameter VAR model. Moreover, we focus on the DSGE–VAR that combines a microfounded DSGE model with the flexibility of a VAR framework. All the aforementioned models as well simple DSGEs and Bayesian VARs are used in a comparative investigation of their out-of-sample predictive performance regarding the US economy. The results indicate that while in general the classical VAR and BVARs provide with good forecasting results, in many cases the TVP–VAR and the DSGE–VAR outperform the other models. European Commission - Seventh Framework Programme (FP7) Marie Curie Intra European Fellowship 'Dote ricercatoriî: FSE, Regione Lombardia'

Opponents of the efficient markets hypothesis argue that predictability reflects the psychological factors and “fads” of irrational investors in a speculative market. In that, conventional time series analysis often fails to give an accurate forecast for financial processes due to inherent noise patterns, fat tails, and nonlinear components. A recent stream of literature on behavioral finance has revealed that boundedly rational agents using simple rules of thumb for their decisions under uncertainty provides a more realistic description of human behavior than perfect rationality with optimal decision rules. Consequently, the application of technical analysis in trading could produce high returns. Machine learning techniques have been employed in economic systems in modeling nonlinearities and simulating human behavior. In this study, we expand the literature that evaluates return sign forecasting ability by introducing a recurrent neural network approach that combines heuristic learning and short-term memory emulation, thus mimicking the decision-making process of boundedly rational agents. We investigate the relative direction-of-change predictability of the neural network structure implied by the Lee–White–Granger test as well as compare it to other well-established models for the DJIA index. Moreover, we examine the relationship between stock return volatility and returns. Overall, the proposed model presents high profitability, in particular during “bear” market periods.

Published revised version of EUI Working paper ECO; 2011/23. This paper relies on wavelet multiresolution analysis to investigate the dependence structure and predictability of currency markets across different timescales. It explores the nature and direction of causality among the exchange rates with respect to the US dollar of the most widely traded currencies, namely Euro, Great Britain Pound and Japanese Yen. The timescale analysis involves the estimation of linear, nonlinear and spectral causal relationships of wavelet components and aggregate series as well as the investigation of their out-of-sample predictability. Moreover, this study attempts to probe into the micro-foundations of across-scale causal heterogeneity on the basis of trader behavior with different time horizons. The examined period starts from the introduction of the Euro and covers the dot-com bubble, the financial crisis of 2007–2010 and the Eurozone debt crisis. Technically, this paper presents an invariant discrete wavelet transform that deals efficiently with phase shifts, dyadic-length and boundary effects. It also proposes a new entropy-based methodology for the determination of the optimal decomposition level and a wavelet-based forecasting approach. Overall, there is no indication of a global causal behavior that dominates at all timescales. In the out-of-sample analysis wavelets clearly outperform the random walk for the volatility series. Moreover, the synergistic application of wavelet decomposition and artificial neural networks provided with an enhanced predictability in many forecast horizons for the returns. These results may have important implications for market efficiency and predictability.

Finding a precise estimate for the smoothness parameter of LSTAR models is notoriously difficult. This paper introduces a robust estimation method for the transition and autoregressive parameters of STAR models, comprising gradient descent and singular value decomposition to account for heteroscedastic noise.

This paper conducts a comparative evaluation of the predictive performance of various Value-at- Risk (VaR) models. Special emphasis is paid on two methodologies related to the Extreme Value Theory (EVT): the Peaks over Threshold (POT) and the Blocks Maxima (BM). Both estimation techniques are based on limit results for the excess distribution over high thresholds and block maxima, respectively. They are applied on, USD-denominated, daily returns of the Dow Jones Industrial Average (DJIA) and the Cyprus Stock Exchange (CSE) indices with the intension to compare the performance of the various estimation techniques on markets with different capitalization and trading practices. The sample extends over the period November 21, 1997 to April 19, 2002 while the sub-period April 12, 2001 to April 19, 2002 has been reserved for backtesting purposes. The results we report reinforce previous ones according to which at very high confidence levels the EVT-based methodology produces the most accurate forecasts of extreme losses.