ISSN: 2056-3736 (Online Version) | 2056-3728 (Print Version)

Asymmetric Effects of Exchange Rate Volatility on Taiwan-China Trade: A Non-Linear ARDL Analysis of 20 Industries

Ilia Tetin, Elizaveta Antonenko and Guych Nuryyev

Correspondence: Guych Nuryyev,

International College, I-Shou University, Kaohsiung, Taiwan

pdf (675.22 Kb) | doi:


This study investigates the long-run and short-run effects of exchange rate volatility on Taiwan's exports to and imports from China across 20 industries, employing a non-linear autoregressive distributed lag (NARDL) approach. The analysis covers the period from January 2004 to December 2022 and highlights industry-specific sensitivities and asymmetric impacts of exchange rate fluctuations. Our findings reveal the critical role of exchange rate volatility in shaping export and import performance across industries, with both positive and negative shocks exerting significant short-run and long-run effects. Asymmetric impacts of exchange rate fluctuations affect 87.96% of Taiwan's total exports to China in the long run and 72.11% in the short run. In contrast, the asymmetric impacts on imports influence 77.12% of Taiwan's total imports from China in the long run and 13.21% in the short run, demonstrating varying sensitivity across industries. These findings accentuate the necessity for industry-tailored trade policies and strategic considerations to better manage the risks and opportunities presented by exchange rate volatility in cross-strait trade.


  Exchange rate volatility, Taiwan-China trade, Non-linear ARDL, Asymmetric effects.


Akaike, Hirotugu. 1974. “A New Look at the Statistical Model Identification.” In Selected Papers of Hirotugu Akaike, edited by Emanuel Parzen, Kunio Tanabe, and Genshiro Kitagawa, 215–222. Springer Series in Statistics. New York, NY: Springer New York. doi:10.1007/978-1-4612-1694-0_16.

Arize, Augustine C., Thomas Osang, and Daniel J. Slottje. 2000. “Exchange-Rate Volatility and Foreign Trade: Evidence from Thirteen LDC’s.” Journal of Business & Economic Statistics 18 (1): 10. doi:10.2307/1392132.

Baek, Jungho, and Soojoong Nam. 2021. “The South Korea–China Trade and the Bilateral Real Exchange Rate: Asymmetric Evidence from 33 Industries.” Economic Analysis and Policy 71 (September): 463–475. doi:10.1016/j.eap.2021.06.007.

Bahmani-Oskooee, Mohsen, Parveen Akhtar, Sana Ullah, and Muhammad Tariq Majeed. 2020. “Exchange Rate Risk and Uncertainty and Trade Flows: Asymmetric Evidence from Asia.” Journal of Risk and Financial Management 13 (6): 128. doi:10.3390/jrfm13060128.

Bahmani‐Oskooee, Mohsen, and Scott W. Hegerty. 2007. “Exchange Rate Volatility and Trade Flows: A Review Article.” Journal of Economic Studies 34 (3): 211–255. doi:10.1108/01443580710772777.

Bahmani‐Oskooee, Mohsen, and Scott W. Hegerty. 2009. “The Effects of Exchange‐Rate Volatility on Commodity Trade between the United States and Mexico.” Southern Economic Journal 75 (4): 1019–1044. doi:10.1002/j.2325-8012.2009.tb00945.x.

Bahmani-Oskooee, Mohsen, Scott W. Hegerty, and Jia Xu. 2012. “Exchange-Rate Volatility and Industry Trade Between Japan and China.” Global Economy Journal 12 (3): 1850268. doi:10.1515/1524-5861.1855.

Bauwens, Luc, Sébastien Laurent, and Jeroen V. K. Rombouts. 2006. “Multivariate GARCH Models: A Survey.” Journal of Applied Econometrics 21 (1): 79–109. doi:10.1002/jae.842.

Bollerslev, Tim. 1986. “Generalized Autoregressive Conditional Heteroskedasticity.” Journal of Econometrics 31 (3): 307–327. doi:10.1016/0304-4076(86)90063-1.

Chen, Hui-Chuan. 2001. “Taiwan’s Exports and Trade Imbalance against US and Japan: An Empirical Analysis.” Applied Economics 33 (10): 1283–1287. doi:10.1080/00036840122642.

Chen, Hui-Chuan. 2002. “Taiwan’s Exports and Trade Imbalance against US and Japan: An Empirical Investigation Based on Error Correction Model.” Applied Economics 34 (18): 2303–2309. doi:10.1080/00036840210141695.

Chen, Shyh-Wei. 2008. “Long-Run Aggregate Import Demand Function in Taiwan: An ARDL Bounds Testing Approach.” Applied Economics Letters 15 (9): 731–735. doi:10.1080/13504850600749032.

Cheung, Francis K., Maw Lin Lee, and Yi-Chen Wu. 1997. “Endogenous Export Prices and the Taiwan-US Trade Imbalance.” Applied Economics 29 (1): 23–31. doi:10.1080/000368497327362.

Chien, Mei-Se, Nur Setyowati, and Chih-Yang Cheng. 2020. “Asymmetric Effects of Exchange Rate Volatility on Bilateral Trade between Taiwan and Indonesia.” The Singapore Economic Review 65 (04): 857–888. doi:10.1142/S021759082050006X.

Clark, Peter B. 1973. “Uncertainty, Exchange Risk, and the Level of International Trade.” Economic Inquiry 11 (3): 302–313.

Engle, Robert F. 1982. “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation.” Econometrica 50 (4): 987. doi:10.2307/1912773.

Fang, WenShwo, YiHao Lai, and Stephen M. Miller. 2009. “Does Exchange Rate Risk Affect Exports Asymmetrically? Asian Evidence.” Journal of International Money and Finance 28 (2): 215–239. doi:10.1016/j.jimonfin.2008.11.002.

Fung, Loretta. 2008. “Large Real Exchange Rate Movements, Firm Dynamics, and Productivity Growth.” Canadian Journal of Economics/Revue Canadienne d’économique 41 (2): 391–424. doi:10.1111/j.1365-2966.2008.00468.x.

Fung, Loretta, and Jin-Tan Liu. 2009. “The Impact of Real Exchange Rate Movements on Firm Performance: A Case Study of Taiwanese Manufacturing Firms.” Japan and the World Economy 21 (1): 85–96. doi:10.1016/j.japwor.2007.11.002.

Grauwe, Paul De. 1988. “Exchange Rate Variability and the Slowdown in Growth of International Trade.” Staff Papers - International Monetary Fund 35 (1): 63. doi:10.2307/3867277.

Hansen, Peter R., and Asger Lunde. 2005. “A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?” Journal of Applied Econometrics 20 (7): 873–889. doi:10.1002/jae.800.

Hsing, Han-Min, and Andreas Savvides. 1996. “Does a J-Curve Exist for Korea and Taiwan?” Open Economies Review 7 (2): 127–145. doi:10.1007/BF01891900.

Klein, Michael W. 1990. “Sectoral Effects of Exchange Rate Volatility on United States Exports.” Journal of International Money and Finance 9 (3): 299–308. doi:10.1016/0261-5606(90)90011-N.

McKenzie, Michael D. 1999. “The Impact of Exchange Rate Volatility on International Trade Flows.” Journal of Economic Surveys 13 (1): 71–106. doi:10.1111/1467-6419.00075.

Moreno, Ramon. 1989. “Exchange Rates and Trade Adjustment in Taiwan and Korea.” Economic Review - Federal Reserve Bank of San Francisco, no. 2: 30.

Péridy, Nicolas. 2003. “Exchange Rate Volatility, Sectoral Trade, and the Aggregation Bias.” Review of World Economics 139 (3): 389–418. doi:10.1007/BF02659668.

Pesaran, M. Hashem, Yongcheol Shin, and Richard J. Smith. 2001. “Bounds Testing Approaches to the Analysis of Level Relationships.” Journal of Applied Econometrics 16 (3): 289–326. doi:10.1002/jae.616.

Shin, Yongcheol, Byungchul Yu, and Matthew Greenwood-Nimmo. 2014. “Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework.” In Festschrift in Honor of Peter Schmidt, edited by Robin C. Sickles and William C. Horrace, 281–314. New York, NY: Springer New York. doi:10.1007/978-1-4899-8008-3_9.

Stokman, A. C. J. 1995. “Effect of Exchange Rate Risk on Intra-EC Trade.” De Economist 143 (1): 41–54. doi:10.1007/BF01388354.

Sun, Chia-Hung, and Yi-Bin Chiu. 2010. “Taiwan’s Trade Imbalance and Exchange Rate Revisited.” Applied Economics 42 (7): 917–922. doi:10.1080/00036840701720937.

Tang, Hsiao Chink. 2014. “Exchange Rate Volatility and Intra-Asia Trade: Evidence by Type of Goods.” The World Economy 37 (2): 335–352. doi:10.1111/twec.12095.

Truong, Loc Dong, Ha Hoang Ngoc Le, and Dut Van Vo. 2022. “The Asymmetric Effects of Exchange Rate Volatility on International Trade in a Transition Economy: The Case of Vietnam.” Buletin Ekonomi Moneter Dan Perbankan 25 (2): 203–214. doi:10.21098/bemp.v25i2.1636.

Wang, Kai-Li, and Christopher B. Barrett. 2007. “Estimating the Effects of Exchange Rate Volatility on Export Volumes.” Journal of Agricultural and Resource Economics 32 (2). Western Agricultural Economics Association: 225–255.

Impact of Supply Risk Sharing on Supply Chains: Multiplicative vs. Additive Risks

Junhyun Bae and Ji-Hung (Ryan) Choi

Correspondence: Junhyun Bae,

School of Business Administration, Oakland University, Rochester MI 48309, USA

pdf (675.22 Kb) | doi:


We consider a supply chain in which a retailer orders from a manufacturer(s) who face(s) a stochastic supply risk (random yield) under single or dual-sourcing cases. In specific, we look into this problem in two different yield risks: multiplicative and additive. One might intuit that if the retailer shares a manufacturer’s random yield risk with the manufacturer, the manufacturer will be better off. Interestingly, we confirm that this intuition is only valid in the additive yield risk but not necessarily in the multiplicative yield risk. Moreover, under dual sourcing, both manufacturers fiercely compete on their prices (i.e., Bertrand-like competition) to become the sole source in the additive yield risk, but the manufacturers do not compete as much in the multiplicative yield risk. Lastly, this paper shows that the supply chain inefficiency may decrease (increase) as risk-sharing increases in the additive risk model under dual sourcing (single sourcing) while it does not change in the multiplicative risk model.


  Supply risk, random yield, dual sourcing, game theory.


An, Jaehyung, Soo-Haeng Cho, Christopher S. Tang. 2015. Aggregating smallholder farmers in emerging economies. Production and Operations Management 24(9) 1414–1429.

Dalzell, Stephanie, Jack Snape, Tara De Landgrafft. 2020. Tonnes of Australian lobsters stuck in Chinese airports amid trade tensions. Australian Broadcasting Corporation News. URL

Jung, Seung Hwan. 2020. Offshore versus onshore sourcing: Quick response, random yield, and competition. Production and Operations Management 29(3) 750–766.

Keren, Baruch. 2009. The single-period inventory problem: Extension to random yield from the perspective of the supply chain. Omega 37(4) 801–810. Role of Flexibility in Supply Chain Design and Modeling.

Kouvelis, Panos, Guang Xiao, Nan Yang. 2021. Role of risk aversion in price postponement under supply random yield. Management Science 67(8) 4826–4844.

Li, Xiang, Yongjian Li, Xiaoqiang Cai. 2013. Double marginalization and coordination in the supply chain with uncertain supply. European Journal of Operational Research 226(2) 228–236.

Niu, Baozhuang, Jiawei Li, Jie Zhang, Hsing Kenneth Cheng, Yinliang (Ricky) Tan. 2019. Strategic analysis of dual sourcing and dual channel with an unreliable alternative supplier. Production and Operations Management 28(3) 570–587.

Smith, Elliot. 2023. Covid caused huge shortages in the jobs market. it may be easing — but there’s another problem ahead. CNBC. URL shortages-in-the-jobs-market-it-may-be-easing.html.

Tang, Sammi Yu, Panos Kouvelis. 2011. Supplier diversification strategies in the presence of yield uncertainty and buyer competition. Manufacturing & Service Operations Management 13(4) 439–451.

Wang, Daqin, Ou Tang, Lihua Zhang. 2014. A periodic review lot sizing problem with random yields, disruptions and inventory capacity. International Journal of Production Economics 155 330–339. Celebrating a century of the economic order quantity model.

Wang, Yimin, Wendell Gilland, Brian Tomlin. 2010. Mitigating supply risk: Dual sourcing or process improvement? Manufacturing & Service Operations Management 12(3) 489–510.

Xiao, Li, Ce Wang. 2023. Multi-location newsvendor problem with random yield: Centralization versus decentralization. Omega 116 102795.

Xie, Lei, Junhai Ma, Mark Goh. 2021. Supply chain coordination in the presence of uncertain yield and demand. International Journal of Production Research 59(14) 4342–4358.

Yano, Candace Arai, Hau L. Lee. 1995. Lot sizing with random yields: a review. Operations Research 43(2) 311–334.

Yu, Haisheng, Amy Z. Zeng, Lindu Zhao. 2009. Single or dual sourcing: decision-making in the presence of supply chain disruption risks. Omega 37(4) 788–800.

Does Institutional Quality matter to the Inflation Targeting-Financial Stability Nexus?

Adel Bogari

Correspondence: Adel Bogari,

Associate Professor, College of Business Administration, Al-Baha University, KSA

pdf (675.22 Kb) | doi:


This study examines the quality institutions role played in the inflation targeting- financial stability nexus. A sample of 65 developed and developing countries, including 33 inflation-targeting countries (10 developed and 23 developing), and 32 non-inflation-targeting countries (12 developed and 20 developing), during the 1996 - 2020 period. Using Two Step GMM estimation, results show that inflation targeting stimulates financial stability. This positive relationship between inflation targeting and financial stability is proved, regardless of the inflation targeting regime in place; Soft or Full-Fledged. Results from institutional quality variables prove that inflation-targeting countries with poor institutional quality are financially vulnerable, and that for good institutional quality are able to promote financial stability.


  Inflation targeting, Financial stability, Developed and developing countries, Quality of institutions, Tow Step GMM System.


Aaron, M., & James.Y. (2018). Are Inflation Targets Credible? A Novel Test, Economics Letters. Vol. 167, pp. 67–70.

Alpanda, S., & Honig, A. (2014). The Impact of Central Bank Independence on the Performance of Inflation Targeting Regimes, Journal of International Money and Finance, Vol.44, pp.118–135.

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.

Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51.

Balke, N. & Fomby, T. (1997). Threshold Cointegration.  International Economic Review, Vol.38, N :3, pp. 627–645.

Bean, C. (2009). The Meaning of Internal Balance thirty years on, Economic Journal, Vol.119, pp. 442–460.

Berger, W., & Kißmer, F. (2013). Central Bank Independence and Financial Stability: A Tale of Perfect Harmony?, European Journal of Political Economy, Vol.31, pp.109–118.

Bernanke, B. & Gertler, M. (2000). Monetary Policy and Asset Price Volatility, Working paper N: 7559. National Bureau of Economic Research.

Bernanke, B. S., Gertler, M., & Gilchrist, S. (1999). The financial accelerator in a quantitative business cycle framework. In Taylor, J. B. and Woodford, M., editors, Handbook of Macroeconomics, volume 1 of Handbook of Macroeconomics, chapter 21, pages 1341–1393. Elsevier

Blanchard, O.& Galí, J. (2007). Real Wage Rigidities and The New Keynesian Model, Journal of Money, Credit and Banking, Vol.39, pp. 35–65.

Borio, C. & Zhu, H. (2008). Capital Regulation, Risk-Taking and Monetary Policy: Amissing Link in The Transmission Mechanism?. BIS Working Papers, 8, 236-251.

Borio, C., English, W. & Filardo, A. (2003). A Tale of Two Perspectives: Old or New Challenges for Monetary Policy?, In BIS Papers Chapters. pp. 1–59. Bank for International Settlements.

Dell’ariccia, G., Laeven, L. & Suarez, G. A. (2017). Bank Leverage and Monetary Policy’s Risk-Taking Channel: Evidence from The United States, The Journal of Finance, Vol.72, pp .613– 654.

Diamond, D. & Dybvig, P. (1983). Bank Runs, Deposit Insurance, and Liquidity, The Journal of Political Economy, Vol.91, pp. 401-419.

Elsayed,A.H., Naifer, N. & Nasreen,S. (2022). Financial Stability and Monetary Policy Reaction: Evidence from the GCC Countries, Quarterly Review of Economics and Finance, pp.1-10.

Fazio, D. M., Silva, T. C., Tabak, B. M.& Cajueiro, D. O. (2018). Inflation Targeting and Financial Stability: Does the Quality of Institutions Matter?, Economic Modelling, N : 71, pp. 1–15.

Fazio, D. M., Tabak, B. M. & Cajueiro, D. O. (2015). Inflation Targeting: Is IT to Blame for Banking System Instability? , Journal of Banking and Finance, Vol.59, pp.76–97.

Fisher, I. (1933). The Debt-Deflation Theory of Great Depressions, Journal of the Econometric Society,Vol. 1, pp.337-357.

Fouejieu, A. A. (2017). Inflation Targeting and Financial Stability in Emerging Markets, Economic Modelling, N: 60, pp. 51–70.

Frappa, S. & Mésonnier, J.-S. (2010). The Housing Price Boom of The Late 1990s: Did Inflation Targeting Matter?, Journal of Financial Stability, Vol.6, N : 4, pp .243–254.

Hadhri,S. (2021). The Nexus, Downside Risk and Asset Allocation between Oil and Islamic Stock Markets: A Cross-Country Analysis, Energy Economics, Vol.101, Article 105448.

Hove, S., Tchana, F. & Touna Mama, A. (2017). Do Monetary, Fiscal and Financial  Institutions Really Matter for Inflation Targeting in Emerging Market Economies ? , Research in  International Business and Finance, N : 39, pp.128–149.

Issing, O. (2009). In Search of Monetary Stability: The Evolution of Monetary Policy. BIS Working Papers No 273

Jiménez, G., Ongena, S., Peydró, J.-L., & Saurina, J. (2014). Hazardous Times for Monetary Policy: What do Twenty-Three Million Bank Loans Say about the Effects of Monetary Policy on Credit Risk-Taking?, Econometrica, Vol.82, pp.463–505.

Kapetanios, G., Pesaran, H. & Yamagata, T. (2006). Panels with Non Stationary Multifactor Error Structures, Journal of Econometrics, Vol.160. (2), pp. 326-348.

Kaufmann D, Kraay A, Mastruzzi M (2011). The worldwide governance indicators: methodology and analytical issues. Hague J Rule Law 3(02):220–246. S1876404511200046

Kim, S.& Mehrotra, A. (2017). Managing Price and Financial Stability Objectives in Inflation Targeting Economies in Asia and the Pacific , Journal of Financial Stability, N : 29, pp. 106–116.

Kindleberger, C.P. (1978). Manias, Panics and Crashes, A History of Financial Crises,  Basic Books, New York.

Martin, C. & Milas, C. (2013).  Financial Crises and Monetary Policy: Evidence from the UK, Journal of Financial Stability,Vol. 9, N :4, pp.654–661.

Minea,A., Tapsoba,R. & Villieu,P.(2020). Inflation Targeting Adoption and Institutional Quality: Evidence from Developing Countries, The World Economy-Wiley, pp.2107-2127.

Minsky, H. (1982). Can” it” Happen Again? : Essays on Instability and Finance , ME Sharpe Armonk, New York.

Mishkin, F. (2000). InflationTargeting in Emerging-Market Countries, American Economic   Review, Vol. 90, N :2, pp.105-109.

Mishkin, F. S. (1991). Asymmetric Information and Financial Crises: A Historical Perspective, Financial Markets and Financial Crises, Hubbard R G, University Of Chicago Press.

Owoundi, J.P.F., Mbassi, M. C. & Ownoudi.F. (2021). Does Inflation Targeting Weaken  Financial Stability ? Assessing The Role of Institutional Quality, The Quarterly Review of Economics and Finance, Vol.80, pp.376-378.

Ozan. E., Neslihan, K.b., & Umit, O. (2017). A Comparison of Optimal Policy Rules Prior to and During Inflation Targeting: Empirical Evidence from Bank of Canada, Applied Economics. Vol. 49, N: 39, pp.3899–3911.

Schwartz, A. J. (1995). Why Financial Stability Depends on Price Stability, Economic Affairs, Vol.15, pp. 21–25.

Shin, Y., Yu, B. & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework, Festschrift in honor of Peter Schmidt. New York, NY: Springer, pp.281–314.

Svensson, L. E. O. (2010). Inflation Targeting. In Handbook of Monetary Economics, Elsevier, pp. 1237–1302.

Svensson, L.E.O. (1997). Inflation Forecast Targeting: Implementing and Monitoring Inflation Targets, European Economic Review, Vol. 41, pp.1111-1146.

Svensson,L.E.O.(1999). Price Stability as a Target for Monetary Policy: Defining and Maintaining Price Stability, Bureau national de la recherche économique, Document de travail, N : 7276, Cambridge.

Tas, B., & Peker, M. (2017). Inflation Target Credibility: Do the Financial Markets Find the Targets Believable ? , Oxford Bulletin of Economics and Statistics, Vol.79, N : 6, pp.1125–1147.

Taylor, M. P. & Davradakis, E. (2006). Interest Rate Setting and Inflation Targeting: Evidence of a Nonlinear Taylor Rule for The United Kingdom, Studies in Nonlinear Dynamics & Econometrics,Vol. 10,N : 4.

Umar,M. & Wen, J. (2020).  Does Inflation Targeting Cause Financial Instability? An Empirical Test of Paradox of Credibility Hypothesis, North American Journal of Economics and Finance, Vol.52, pp.101-164.

White, W. R. (2006). Is Price Stability Enough?, BIS Working Papers. Bank for International Settlements, N: 205.

Forecasting TAIEX and FITX with Affirmative and Doubtful Investor Sentiments

Tzu-Pu Chang, Yu-Wei Chan and Ping-Huang Wang

Correspondence: Tzu-Pu Chang,

Department of Finance, National Yunlin University of Science and Technology, Taiwan.

pdf (675.22 Kb) | doi:


The existing literature have used media messages as a proxy variable for investor sentiment, but they mainly classify sentiment into positive or negative categories based on the words used in news articles, without much attention to the degree of affirmative or doubtful conveyed by the words used. Thus, in addition to classifying news content into positive or negative sentiment, this study also measures the degree of affirmative or doubtful expressed in the news articles in order to achieve more accurate predictive results. The study converts qualitative text to two quantitative scores (sentiment ratio and affirmative ratio) and investigates the predictive ability of these two variables on stock index returns and volatility in Taiwan’s case. The empirical findings indicate that only affirmative ratio exhibits a significant and negative impact on the one-day ahead returns of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the Taiwan Stock Exchange Index Futures (FITX). Additionally, the volatility of returns in both future and spot markets is significantly influenced by both sentiment ratio and affirmative ratio.


  Investor sentiment, Text-mining, TAIEX, FITX, Affirmative ratio.


Audrino, F., Sigrist, F. and Ballinari, D. (2020). The Impact of Sentiment and Attention Measures on Stock Market Volatility. International Journal of Forecasting, 36, 334-357.

Baker, M. and  Stein, J.C. (2004).  Market Liquidity as a Sentiment Indicator. Journal of Financial Markets, 7, 271-299.

Baker, M. and Wurgler, J.  (2000). The Equity Share in New Issues and Aggregate Stock Returns. Journal of Finance, 55, 2219-2257.

Baker, M. and Wurgler, J. (2006). Investor Sentiment and the Cross‐section of Stock Returns. Journal of Finance, 61, 1645-1680.

Brown, G.W. and Cliff, M.T. (2004). Investor Sentiment and the Near-term Stock Market. Journal of Empirical Finance, 11, 1-27.

Brown, G.W. and Cliff, M.T. (2005). Investor Sentiment and Asset Valuation.  Journal of Business, 78, 405-440.

Chen, K.C., Lin, C.I. and Chen, H.M. (2021). Relationship between News Sentiment Indicator and the Taiwan Weighted Stock Index. Journal of Social Sciences and Philosophy, 33, 383-423. (in Chinese)

Fisher, K.L. and Statman, M. (2000). Investor Sentiment and Stock Returns. Financial Analysts Journal, 56, 16-23.

Garcia, D. (2013). Sentiment during Recessions. Journal of Finance, 68, 1267-1300.

Gillam, L., Ahmad, K., Ahmad, S., Casey, M., Cheng, D., Taskaya, T., de Oliveira, P.C.F. and Manomaisupat, P. (2002). Economic News and Stock Market Correlation: A Study of the UK Market. Workshop at the International Conference on Terminology and Knowledge Engineering.

Kumari, J. and Mahakud, J.  (2015). Does Investor Sentiment Predict the Asset Vvolatility? Evidence from Emerging Stock Market India. Journal of Behavioral and Experimental Finance, 8, 25-39.

Li, F. (2010). The Information Content of Forward‐looking Statements in Corporate Filings - A Naïve Bayesian Machine Learning Approach. Journal of Accounting Research, 48, 1049-1102.

Liu, P., Smith, S.D. and Syed, A.A. (1990). Stock Price Reactions to the Wall Street Journal’s Securities Recommendations. Journal of Financial and Quantitative Analysis, 25, 399-410.

Loughran, T. and McDonald, B. (2011). When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10‐Ks. Journal of Finance, 66, 35-65.

Tetlock, P.C. (2007). Giving Content to Investor Sentiment: The Role of Media in the Stock Market. Journal of Finance, 62, 1139-1168.

The Covid-19 Pandemic Impact on the Saudi Arabia Tourism Sector: An Accountancy Approach

Seraj Bahrawe, Mohammed Abulkhair and Sami Mensi

Correspondence: Sami Mensi,

ESC-Business School (ESCT). Univ. of Manouba & ECSTRA Laboratory, Tunisia.

pdf (675.22 Kb) | doi:


The article aims to determine the impact of the COVID-19 outbreak on the tourism industry in the world and in the Kingdom of Saudi Arabia. It adopts the financial data of listed companies in Saudi Arabia and uses the synthetic index compilation method to compile an accounting index that captures the period before and during the COVID-19 outbreak and measures the impact of the COVID-19 on the tourism sector. From this article, we recommend the appropriate policies to re-launch some tourism activities within the after COVID-19 period. It will be crucial to restore all types of travel, and domestic and international flights, improve direct and indirect employment and the recovery of many related business as travel agencies, hotels, and airline companies, which allow for economic and social benefits.


  COVID-19 Pandemic; Tourism Sector; Accountancy Approach, KSA.


AlDawood A. (2019). Arabian Travel and Tourism Industry. Understanding inbound, outbound, and domestic travel trends. Seera Group.Riyadh, Saudi Arabia

Apergis, E., & N. Apergis (2020). Can the COVID-19 pandemic and oil prices drive the US partisan conflict index? Energy Research Letters 1 (1):13144. doi:10.46557/001c.13144.

Ataguba, J. E. (2020). COVID -19 pandemic, a war to be won: Understanding its economic implications for Africa. Applied Health Economics and Health Policy 10227. doi:10.1007/s40258-020-00580-x.

Ben Youssef, A., Zeqir A. & Belaid, F. (2020). The impact of COVID-19 on the tourism sector in MENA, ERF Forum 2020. file:///C:/Users/Admin/Downloads/TheimpactofCovid-19onthetourismsectorinMENA-ERF.pdf

Chen, G. Z. (2017). Research on the construction of the prosperity index system of listed companies. Shanghai Journal of Economics 12:47–56. doi:10.19626/j.cnki.cn31-1163/f.2017.12.005.

Choi, K. (2010). The effect of economic crisis on the value relevance of accounting information. Korean Accounting Journal Vol.19, No. 3, pp. 83–110.

Cristiana, P. C., Brînză, G., Alexandru A.A. & Butnaru, G.I. (2020). COVID -19 pandemic and its effects on the tourism sector. CES Working Papers – Volume XII, Issue 2.

Das, S. (2022). Chapter 9 - Impact of COVID-19 on industries, Editor(s): Deepak Rawtani, Chaudhery Mustansar Hussain, Nitasha Khatri, COVID-19 in the Environment, Elsevier, Pp 191-200, ISBN 9780323902724,

Deghi, A. & Dulani, S, T. (2021). Corporate Funding and the COVID-19 Crisis. IMF WP/21/86

Feng, S., Zihan, Y, Xingjian, R., & Sajia, Y. (2022). Advances in Economics, Business and Management Research, volume 215 Proceedings of the 2022 7th International Conference on Social Sciences and Economic Development (ICSSED 2022)

Foo, L., Chin, M., Tan, Kim, L., & Phuah, K.T. (2020). The impact of COVID -19 on tourism industry in Malaysia. Current Issues in Tourism,

Haque, S.M., & Varghese, R.. (2021). The COVID-19 Impact on Corporate Leverage and Financial Fragility, IMF WP/21/265.

He, P., Niu, H., Sun, Z & Tao L. (2020). Accounting Index of COVID -19 Impact on Chinese Industries: A Case Study Using Big Data Portrait Analysis. Emerging Markets Finance and Trade, Vol. 56, No. 10, pp.2332–2349

Krueger, D., Uhlig, H. & Xie, T. (2020). Macroeconomic Dynamics and Reallocation in an Epidemic, Working Paper 27047. National Bureau of Economic Research, Cambridge available on. visited 19 August 2020.

Kucharski, A.J., Russell, T.W., Diamond, C., Liu, Y., Edmunds, J., Funk, S. & Flasche, S. (2020). Early dynamics of transmission and control of COVID -19: a mathematical modelling study. Lancet Infect. Dis. (20) 301444.

Kuo, H.I., Chen, C.C., Tseng, W.C., Ju, L.F. & Huang, B.W. (2008). Assessing impacts of SARS and Avian Flu on international tourism demand to Asia. Tour. Manag. 29 (5), 917–928.

Lucia, S., Kramarova., K & Chabadova, D. (2022). Impact of the COVID-19 Pandemic on the Business Environment in Slovakia. Economies Vol. 10, No. 10, pp. 244-266,

Luvsannyam, D. (2018). Panel Structural VARs and the PSVAR Add-In accessed January 2020. html.

Mandel, A. & Veetil, V. (2020). The economic cost of COVID lockdowns: an out-of-equilibrium analysis. Econ. Disasters Climate Change. /s41885-020-00066-z.

Mao, C.K., Ding, C.G. & Lee, H.Y. (2010). Post-SARS tourist arrival recovery patterns: an analysis based on a catastrophe theory. Tour. Manag. 31 (6), 855–861. https://doi. org/10.1016/j.tourman.2009.09.003.

Marron, D. (2020). Macroeconomic Policy in the Time of COVID -19. Federal Budget and Economy, TaxVox, 17 March 2020, available on. https://www.taxpolicycenter. org/taxvox/macroeconomic-policy-time-COVID-19. visited 15 March 2020.

McKee, M. & Stuckler, D. (2020). If the world fails to protect the economy, COVID-19 will damage health not just now but also in the future. Nat. Med. 26 (May 2020), 640–642 available on. visited 19 August 2020.

McKercher, B. (2004). The impact of SARS on Hong Kong’s tourism industry. Int. J. Contemp. Hosp. Manag. 16 (2), 139–143. 520034. ScienceAlert. (2020). January, accessed at

Mulder, N. (2020). The impact of the COVID -19 pandemic on the tourism sector in Latin America and the Caribbean, and options for a sustainable and resilient recovery, International Trade series, No. 157 (LC/TS.2020/147), Santiago, Economic Commission for Latin America and the Caribbean (ECLAC).

OECD, (2020). Tourism policy responses to coronavirus (COVID-19),

Pedroni, P. (2013). Structural panel VARs. Econometrics 1 (2), 180–206. 10.3390/econometrics1020180. Pine, R.,

Tarkom, A. & Huang, X. (2023). "Readjusting the speed of leverage adjustment during the COVID-19 pandemic?", China Accounting and Finance Review, Vol. ahead-of-print No. ahead-of-print.

UNCTD. (2020). COVID-19 and Tourism Assessing the Economic Consequences

UNWTO. (2020). Impact assessment of the COVID -19 outbreak on international tourism, (retrieved from

Labor Demand Forecasting: The Case of Cambodia

KY Sereyvuth

Correspondence: KY Sereyvuth,

Department of Economics and Management, Graduate School of Humanities and Social Sciences, Saitama University, Japan.

pdf (675.22 Kb) | doi:


Labor demand forecasting is crucial for Cambodia’s economic prosperity. This is because it enables the country to make well-informed decisions and implement effective policies that align with the changing dynamics of its labor market to promote sustainable economic progress. This study utilizes a demand-driven model; specifically, the autoregressive integrated moving average (ARIMA) model with a top-down approach to forecast Cambodia’s labor demand from 2020 to 2025. By capturing current and future labor market trends, we can identify skill requirements and ensure high employment rates for sustainable development. With labor demand forecasting, Cambodia can proactively address skill gaps, optimize workforce planning, and foster an environment conducive to economic growth and stability.


  Labor demand, Employment forecasting, ARIMA, Top-down forecasting.


National Institute of Statistics (NIS) 2020 General Population Census of the Kingdom of Cambodia 2019. NIS: Phnom Penh, Cambodia.

National Institute of Statistics (1993‒2019) Cambodia Socio-Economic Survey (CSES). For various years (1993, 1996, 1997, 1999, 2004, 2007–2019). NIS: Phnom Penh, Cambodia.

Ministry of Economic and Finance (2021) Annual Macroeconomic and Fiscal Policy Framework 2021 (working paper), MEF: Phnom Penh, Cambodia

National Employment Agency (2018) Skills Shortages and Skills Gaps in the Cambodian Labour Market: Evidence from Employer Survey 2017. NEA: Phnom Penh, Cambodia

James, M. W., Albert P.C., & Chiang, Y. H. (2004) A critical review of forecasting models to predict manpower demand. The Australian Journal of Construction Economics and Building, 4(2), 51.

Meagher, G. A., Adams, P. D., and Horridge, J.M.  (2000) Applied General Equilibrium Modelling and Labour Market Forecasting. Centre of Policy Studies/IMPACT Centre Working Papers, ip-76. Victoria University, Centre of Policy Studies/IMPACT Centre.

Cedefop (European Centre for the Development of Vocational Training). (2012b) Skill supply and demand in Europe: Methodological framework. Luxembourg: European Centre for the Development of Vocational Training.

Oxinos, G. et al. (2005) Country contribution: Cyprus Feasibility workshop on European skill needs forecasting: information inputs by Member States, Pafos, Cyprus, 20 and 21 October 2005.

Hughes, G. and Fox, R. (2005) Country contribution: Ireland. Feasibility workshop on European skill needs forecasting: information inputs by Member States, Pafos, Cyprus, 20–21

Cörvers, F. and Dupuy, A. (2006) Explaining the occupational structure of Dutch sectors of industry, 1988-2003. Maastricht: Research Centre for Education and the Labour Market (ROA-W-2006/7E).

Dubra, E., & Gulbe, M. (2008). Forecasting the labour force demand and supply in Latvia. Technological and economic development of economy, 14(3), 279-299.

Giesecke, J. A., Tran, N. H., Meagher, G. A., & Pang, F. (2015). A decomposition approach to labour market forecasting. Journal of the Asia Pacific Economy, 20(2), 243-270.

Cedefop. (2008a). Future skill needs in Europe Medium-term forecast. Luxembourg: European Centre for the Development of Vocational Training.

Cedefop. (2008b). Systems for anticipation of skill needs in the EU Member States. Luxembourg: European Centre of the Vocational Training.

Cedefop. (2009). Future skill needs in Europe: medium-term forecast Background technical report. Luxembourg: European Centre of the Vocational Training.

Cedefop. (2012a). Future skills supply and demand in Europe. Luxembourg: European Centre for the Development of Vocational Training.

Maier, T., Mönnig, A., & Zika, G. (2015). Labour demand in Germany by industrial sector, occupational field and qualification until 2025–model calculations using the IAB/INFORGE model. Economic Systems Research, 27(1), 19-42.

Jeong, H. (2014) Legacy of Khmer Rouge on Skill Formation in Cambodia. Journal of International and Area Studies, 21(1), 1–19,

Wong, J. M., Chan, A. P., & Chiang, Y. H. (2005). Time series forecasts of the construction labour market in Hong Kong: the Box‐Jenkins approach. Construction Management and Economics, 23(9), 979-991.

Alyahya, M. and Hadwan, M. (2022) Applying ARIMA Model to Predict Future Jobs in the Saudi Labor Market. International Research Journal of Innovations in Engineering and Technology (IRJIET), 6(4), 1–8,

Explaining the first effects of Covid-19 on Greek banks’ profitability

Barkas Panagiotis, Kounadeas Theodoros and Spatharakis Nikolaos Dimitrios

Correspondence: Kounadeas Theodoros,

Department of Business Administration, National and Kapodistrian University of Athens.

pdf (675.22 Kb) | doi:


The present paper studies the profitability dynamics of systemic Greek banks. By deploying an econometric methodology based on multiple linear regression analysis, we empirically investigate the drivers of banks’ return on assets between 2008 and 2020. We also shed light on the first effects of Covid-19 on banks. Examining the effects various macroeconomic, regulatory and financial factors, we find that public debt developments, including Greek debt restructuring, and banks’ provisions for credit losses had a negative effect on banks profitability. Besides, we testify that banks' capital adequacy and the size of liabilities of financial institutions towards their customers strengthened chances of increased bank profitability. We discuss the implications of our empirical findings in light of macroeconomic, regulatory and financial developments in Greece and the EU.


  Systemic Banks, Profitability, Greece, ROA, Debt Crisis, Covid-19, Financial Analysis, Financial Ratios.


Albertazzi, U., Ropele, T., Sene, G. & Signoretti, F.M. (2014). the impact of the sovereign debt crisis on    the activity of Italian banks. Journal of Banking & Finance, 46, pp. 387-402. doi:10.1016/j.jbankfin.2014.05.005

Aldasoro, I., Fender, I., Hardy, B, & Tarashev, N. (2020). Effects of Covid-19 on the banking sector: the market's assessment. Bank for International Settlements, BIS Bulletins 12. Retrieved February 12, 2022, from

Alexakis, P., Thomadakis, S. & Xanthakis, M. (1995) Bank Liberalization and Profitability: Evidence from Greek Commercial Banks, Journal of International Financial Markets, Institutions and Money, 5, pp. 181-192.

Alexiou, C. & Voyazas, S. (2009). Determinants of bank profitability: Evidence from the Greek banking sector. Economic Annals, 54 (182), pp. 93-188. doi: 10.2298/EKA0A0982093A

Ari, A., Chen, S. & Ratnovski, L. (2021). the dynamics of non-performing loans during banking crises: a new database with post-COVID-19 implications. Journal of Banking & Finance, 133. doi: 10.1016/j.jbankfin.2021.106140

Athanassoglou, P., Brissimis, S. & Delis, M. (2008). bank-specific, industry-specific and macroeconomic determinants of bank profitability, Journal of International Financial Markets, Institutions and Money, 18 (2), pp. 121-136. doi: 10.1016/j.intfin.2006.07.001

Barkas, P., Kounadeas, T., Spatharakis, N. D. (2022). Financial and Macroeconomic Drivers of Bank Profitability: Evidence from Greek Systemic Banks During 2009-2019. International Journal of Corporate Finance and Accounting (IJCFA), 9(1), 1-22.

Basdekis C., Christopoulos A., Kasampoxakis I., Lyras A. (2020), Profitability & Optimal Debt Ratio of the Automobiles & Parts Sector in the Euro Area, Journal of Capital Market Studies, 4 (2), pp. 113-127, DOI 10.1108/JCMS-08-2020-0031

Berger, A.N., Demirgüç-Kunt, A. (2021). banking research in the time of COVID-19. Journal of Financial Stability, 57. doi: 10.1016/j.jfs.2021.100939

Bitar, M., Tarazi, A. (2020). A Note on Regulatory Responses to COVID-19 Pandemic: Balancing Banks' Solvency and Contribution to Recovery. HAL open science, hal-02964598. Retrieved February 8, 2022, from

Bongini, P., Cucinelli, D., Di Battista, M.L. & Nieri, L. (2019). Profitability shocks and recovery in time of crisis evidence from European banks. Finance Research Letters, 30, pp. 233-239. doi: 10.1016/

Borri, N. & Di Giorgio, G. (2021). Systemic risk and the COVID challenge in the european banking sector. Journal of Banking & Finance. doi: 10.1016/j.jbankfin.2021.106073

Cheng, G. & Mevis, D. (2019). What happened to profitability? Shocks, challenges and perspectives for euro area banks, The European Journal of Finance, 25 (1), pp. 54-78. doi: 10.1080/1351847X.2018.1470994

Duan, Y., El Ghoul, S., Guedhami, O., Li, H. & Li X. (2021). Bank systemic risk around COVID-19: A cross-country analysis. Journal of Banking & Finance, 133. doi: 10.1016/j.jbankfin.2021.106299.

Elhanass, M., Quang Trinh, V. & Li, T. (2021). Global banking stability in the shadow of Covid-19 outbreak. Journal of International Financial Markets, Institutions and Money, 72. doi: 10.1016/j.intfin.2021.101322

Eriotis, N., Kollias K., Kounadeas, Th. (2021). Has the Composition of the Greek Banking Sector Investment Portfolio Contributed to the Greek Economy Financial Crisis. International Journal of Corporate Finance and Accounting. 8. 1-11. 10.4018/IJCFA.2021070101.

Foglia, M., Addi, A. & Angelini, E. (2022). The Eurozone banking sector in the time of COVID-19: Measuring volatility connectedness. Global Finance Journal, 51. doi:10.1016/j.gfj.2021.100677.

Halkos, G. & Salamouris, D. (2004). Efficiency measurement of the Greek commercial banks with the use of financial ratios: A data envelopment analysis approach. Management Accounting Research, 15 (2), pp. 201-224. Doi: 10.1016/j.mar.2004.02.001

Kanas, A., Vasiliou, D. & Eriotis, N. (2012). Revisiting bank profitability: A semi-parametric approach. Journal of International Financial Markets, Institutions and Money, 22 (4), pp. 990-1005. doi: 10.1016/j.intfin.2011.10.003

Katsampoxakis, I. (2021), ECB's unconventional monetary policy and spillover effects between sovereign and bank credit risk, EuroMed Journal of Business, EMJB-09-2020-0103,

Katsampoxakis I., Basdekis C., Anathreptakis K., (2022), How the Greek Crisis Determined Firm Profitability and Optimal Debt Ratio, Research Anthology on Business Continuity and Navigating Times of Crisis, IGI Global, DOI: 10.4018/978-1-6684-4503-7.ch055

Katsimi, M. (2010). EMU and the Greek crisis: the political-economy perspective, European Journal of Political Economy, 26 (4), pp. 568-576.

Kosmidou, K. (2008). The determinants of banks' profits in Greece during the period of EU financial integration. Managerial Finance, 34 (2), pp. 146-159. Doi:10.1108/03074350810848036

Kosmidou, K., Kousenidis, D. & Negakis, C. (2015). The impact of the EU/ECB/IMF bailout programs on the financial and real sectors of the ASE during the Greek sovereign crisis. Journal of Banking & Finance, 50 (C), pp. 440-454.Doi: 10.1016/j.jbankfin.2014.03.008

Kosmidou, K. & Zopounidis, C. (2008). Measurement of Bank Performance in Greece. South-Eastern Europe Journal of Economics, 6 (1), pp. 79-95. Retrieved December 19, 2020, from

Kotios, A. & Roukanas, S. (2013). The Greek Crisis and the Crisis in Eurozone's Governance. in P. Sklias & N. Tzifakis (Eds.), Greece's Horizons, Reflecting on the Country's Assets and Capabilities, (pp. 91-105) Berlin, Heidelberg: Springer. doi: 10.1007/978-3-642-34534-0_8

Kouretas, G. & Vlamis, P. (2010). The Greek Crisis: Causes and Implications, Panoeconomicus, 57 (4), pp. 391-404. doi: 10.2298/PAN1004391K

Kutter, A. (2014). A catalytic moment: The Greek crisis in the German financial press, Discourse & Society, 25 (4), pp. 446-466. doi: 10.1177/0957926514536958

Lapavitsas, C. (2019). Political Economy of the Greek Crisis, Review of Radical Political Economics, 51 (1), pp.31-51. doi: 10.1177/0486613417730363

Li, X., Feng, H., Zhao, S. & Carter, D.A. (2021). the effect of revenue diversification on bank profitability and risk during the COVID-19 pandemic. Finance Research Letters, 43. doi: 10.1016/

Marcu, M.R. (2021). the impact of the COVID-19 pandemic on the banking sector. Management Dynamics in the Knowledge Economy, 9 (2), pp. 205-223. doi: 10.2478/mdke-2021-0013

Matos, P., Costa, A. & Da Silva, C. (2021). on the risk-based contagion of G7 banking system and the COVID-19 pandemic. Global Business Review. doi: 10.1177/09721509211026813

Menicucci, E. & Paolucci, G. (2016). The determinants of bank profitability: Empirical evidence from European banking sector. Journal of Financial Reporting and Accounting, 14 (1), pp. 86-115. doi: 10.1108/JFRA-05-2015-0060

Miller, S.M. & Noulas, A.G. (1997). Portfolio mix and large-bank profitability in the U.S., Applied Economics, 29 (4), pp. 505-512, Doi: 10.1080/000368497326994

Pagoulatos, G. (2020). EMU and the Greek crisis: Testing the extreme limits of an asymmetric union, Journal of European Integration, 42 (3), pp. 363-379. doi: 10.1080/07036337.2020.1730352

Pagoulatos, G. & Quaglia, L. (2013). Turning the crisis on its head: sovereign debt crisis as banking crisis in Italy and Greece. in I. Hardie & D. Howarth (Eds.), Market-Based Banking & the International Financial Crisis (pp. 179-200). Oxford: Oxford University Press. doi: 10.1093/acprof:oso/9780199662289.003.0008

Pasiouras, F. & Kosmidou, K. (2007). Factors influencing the profitability of domestic and foreign commercial banks in the European Union. Research in International Business and Finance, 21 (2), pp. 222-237. Doi: 10.1016/j.ribaf.2006.03.007

Provopoulos, G. (2014) The Greek Economy and Banking System: Recent Developments and the Way Forward, Journal of Macroeconomics, 39 (B), pp. 240-249. doi:10.1016/j.jmacro.2013.09.016

Schiniotakis, N. (2012). Profitability factors and efficiency of Greek banks, EuroMed Journal of Business, 7 (2), pp. 185-200. doi: 10.1108/14502191211245606

Schularick, Μ., Steffen, S. & Troger, T.H. (2020). Bank Capital and the European Recovery from the COVID-19 Crisis. Centre for Economic Policy Research, Discussion Paper No DP14927. Retrieved February 5, 2022, from

Staikouras, C. & Steliaros, M. (1999). Determinants Factors of Profitability of the Greek Banking System, Journal of the Banking Association of Greece, 19/20, pp. 61-66.

Stournaras, Y. (2018). Lessons from the financial crisis and challenges for the Greek banking sector. In International Center for Monetary and Banking Studies (ICMB) (Ed.), Lessons from the financial crisis and challenges for the Greek banking sector, 13 November 2018 (pp. 1-22). Geneva: International Center for Monetary and Banking Studies (ICMB). Retrieved December 15, 2020, from

Stournaras, Y. (2019). Lessons from the Greek Crisis: past, present, future. Atlantic Economic Journal, 47 (2), pp. 127-135. doi: 10.1007/s11293-019-09615-8

Van Dooren, M. (2017). Estimating the Determinants of Bank Profitability in the European Union from 1998-2013. The Park Place Economist, 25 (1). Retrieved February 23, 2022, from

Vousinas, G. (2015). Recapitalization of the Greek Banking System & the Fallacy of PSI: An Empirical Analysis with Future Prospects, International Case Studies Journal, 4 (1), pp. 47-60. doi: 10.2139/ssrn.2546526

Twitter‘s happiness sentiment index impacts on financial markets: an integrated overview of empirical findings

Νikolaos A. Kyriazis

Correspondence: Νikolaos A. Kyriazis,

Department of Economics, University of Thessaly, 28th October 78 Street, PC:38333, Volos, Greece.

pdf (675.22 Kb) | doi:


This survey paper investigates the empirical findings of academic work that explores the nexus between the highly innovative Twitter happiness sentiment index and a range of financial assets. An integrated overview of econometric outcomes and the relevant investment policy implications are provided. It is revealed that investor happiness reinforces the safe haven abilities of gold. Moreover, major stock indices are highly influenced by the happiness index especially at higher quantiles. Reverse causality between the happiness index and stock indices is also detected but in a weaker level. This survey contributes to better understanding investment decisions based on behavioural finance and provides evidence about the nexus of investor sentiment estimation with the financial sector nowadays.


  Investor happiness, Investor sentiment, Twitter, Survey, Gold, Stock prices.


Barberis, N., & Thaler, R. (2003). A survey of behavioral finance. Handbook of the Economics of Finance,      1, 1053-1128.Baur, D. G., & McDermott, T. K. (2010). Is gold a safe haven? International evidence. Journal of Banking   & Finance, 34(8), 1886-1898.Beckmann, J., Berger, T., & Czudaj, R. (2015). Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. Economic Modelling, 48, 16-24.Bonato, M., Gkillas, K., Gupta, R., & Pierdzioch, C. (2020). A Note on InvestorHappiness and the Predictability of Realized Volatility of Gold. Finance Research Letters, 101614.Bonato, M., Gkillas, K., Gupta, R., & Pierdzioch, C. (2020b). Investor happiness and predictability of the realized volatility of oil price. Sustainability, 12(10), 4309Bredin, D., Conlon, T., & Potì, V. (2015). Does gold glitter in the long-run? Gold as a hedge and safe haven across time and investment horizon. International Review of Financial Analysis, 41, 320-328.Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of empirical finance, 11(1), 1-27.Byström, H. (2020). Happiness and Gold Prices. Finance Research Letters, 101599. Chuang, C. C., Kuan, C. M., & Lin, H. Y. (2009). Causality in quantiles and dynamic stock return–volume relations. Journal of Banking & Finance, 33(7), 1351-1360.Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199.Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.De Bondt, W. F., Muradoglu, Y. G., Shefrin, H., & Staikouras, S. K. (2008). Behavioral finance: Quo vadis?. Journal of Applied Finance (Formerly Financial Practice and Education), 18(2).Diks, C., & Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control, 30(9-10), 1647-1669.Dutta, A., Das, D., Jana, R. K., & Vo, X. V. (2020). COVID-19 and oil market crash: Revisiting the safe haven property of gold and Bitcoin. Resources Policy, 69, 101816.Fama, E. F. (1998). Market efficiency, long-term returns, and behavioral finance. Journal of financial economics, 49(3), 283-306.Fisch, C., & Block, J. (2018). Six tips for your (systematic) literature review in business and management research. Management Review Quarterly, 68(2), 103-106.Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, 424-438.Granger, C. W. (1988). Some recent development in a concept of causality. Journal of econometrics, 39(1-2), 199-211.Hirshleifer, D. (2015). Behavioral finance. Annual Review of Financial Economics, 7, 133-159.Hood, M., & Malik, F. (2013). Is gold the best hedge and a safe haven under changing stock market volatility?. Review of Financial Economics, 22(2), 47-52.Huang, B. N., Hwang, M. J., & Yang, C. W. (2008). Causal relationship between energy consumption and GDP growth revisited: a dynamic panel data approach. Ecological economics, 67(1), 41-54.Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica: journal of the Econometric Society, 33-50.Koenker, R., & Machado, J. A. (1999). GMM inference when the number of moment conditions is large. Journal of Econometrics, 93(2), 327-344.Königstorfer, F., & Thalmann, S. (2020). Applications of Artificial Intelligence in commercial banks–A research agenda for behavioral finance. Journal of Behavioral and Experimental Finance, 27, 100352.Kyriazis, N. A. (2019a). A survey on efficiency and profitable trading opportunities in cryptocurrency markets. Journal of Risk and Financial Management, 12(2), 67.Kyriazis, N. A. (2019b). A survey on empirical findings about spillovers in cryptocurrency markets. Journal of Risk and Financial Management, 12(4), 170.Kyriazis, N. A. (2020a). Herding behaviour in digital currency markets: An integrated survey and empirical estimation. Heliyon, 6(8), e04752.Kyriazis, N. A. (2020b). Is Bitcoin similar to gold? An integrated overview of empirical findings. Journal of Risk and Financial Management, 13(5), 88.Kyriazis, N. A. (2021a). The Nexus of Sophisticated Digital Assets with Economic Policy Uncertainty: A Survey of Empirical Findings and an Empirical Investigation. Sustainability, 13(10), 5383.Kyriazis, N. A. (2021b). Trade Policy Uncertainty Effects on Macro Economy and Financial Markets: An Integrated Survey and Empirical Investigation. Journal of Risk and Financial Management, 14(1), 41.Kyriazis, Ν. A. (2021c). The effects of geopolitical uncertainty on cryptocurrencies and other financial assets. SN Business & Economics, 1(1), 1-14.Lee, C. C., & Chen, M. P. (2020). Happiness sentiments and the prediction of cross-border country exchange-traded fund returns. The North American Journal of Economics and Finance, 54, 101254.Lee, W. Y., Jiang, C. X., & Indro, D. C. (2002). Stock market volatility, excess returns, and the role of investor sentiment. Journal of banking & Finance, 26(12), 2277-2299.Li, X., Shen, D., Xue, M., & Zhang, W. (2017). Daily happiness and stock returns: The case of Chinese company listed in the United States. Economic Modelling, 64, 496-501.Merkle, C., Egan, D. P., & Davies, G. B. (2015). Investor happiness. Journal of Economic Psychology, 49, 167-186.Mian, G. M., & Sankaraguruswamy, S. (2012). Investor sentiment and stock market response to earnings news. The Accounting Review, 87(4), 1357-1384.Naeem, M. A., Farid, S., Faruk, B., & Shahzad, S. J. H. (2020). Can happiness predict future volatility in stock markets?. Research in International Business and Finance, 54, 101298.Pan, W. F. (2020). Does Investor Sentiment Drive Stock Market Bubbles? Beware of Excessive Optimism!. Journal of Behavioral Finance, 21(1), 27-41.Papadamou, S., Kyriazis, Ν. A., & Tzeremes, P. G. (2019). Unconventional monetary policy effects on output and inflation: A meta-analysis. International Review of Financial Analysis, 61, 295-305.Papadamou, S., Siriopoulos, C., & Kyriazis, N. A. (2020). A survey of empirical findings on unconventional central bank policies. Journal of Economic Studies, 47(7), 1533-1577.Shiller, R. J. (2003). From efficient markets theory to behavioral finance. Journal of economic perspectives, 17(1), 83-104.Stracca, L. (2004). Behavioral finance and asset prices: Where do we stand?. Journal of economic psychology, 25(3), 373-405.Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of finance, 62(3), 1139-1168.Thaler, R. H. (1999). The end of behavioral finance. Financial Analysts Journal, 55(6), 12-17.Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British journal of management, 14(3), 207-222.Wen, X., & Cheng, H. (2018). Which is the safe haven for emerging stock markets, gold or the US dollar?. Emerging Markets Review, 35, 69-90.Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93-112.You, W., Guo, Y., & Peng, C. (2017). Twitter's daily happiness sentiment and the predictability of stock returns. Finance Research Letters, 23, 58-64.Zhao, R. (2019). Quantifying the correlation and prediction of daily happiness sentiment and stock return: The Case of Singapore. Physica A: Statistical Mechanics and its Applications, 533, 122020.Zhao, R. (2020a). Quantifying the cross sectional relation of daily happiness sentiment and return skewness: Evidence from US industries. Journal of Behavioral and Experimental Finance, 27, 100369.Zhao, R. (2020b). Quantifying the cross sectional relation of daily happiness sentiment and stock return: Evidence from US. Physic a A: Statistical Mechanics and its Applications, 538, 12262.

Co-movement and global factors in sovereign bond yields

Ioannis A. Venetis and Avgoustinos Ladas

Correspondence: Ioannis A. Venetis,

University of Patras, School of Economics and Business, Department of Economics, University Campus, Rio 26504.

pdf (675.22 Kb) | doi:


We study the co-movement in international zero-coupon government bond yields using a recently proposed methodology by Choi et al. (2018) and Choi et al. (2021) for the estimation of multilevel factor models. We employ a readily available non-proprietary dataset coupled with open-source code which facilitates reproduction of the results but also comparability with the existing bibliography. The ten countries dataset is cross-sectionally expanded to eleven countries with newly constructed data series on the term structure of Greek constant-maturity, government zero-coupon bond rates. We find that the country pair US-Germany is most suitable as an initial candidate for global factor estimation. We confirm that three global factors account for most of the variation in zero-coupon bond yields leaving a small proportion to be (contemporaneously) explained by local factors. Global inflation and global real activity are related to the global level and slope factors. The third global factor, “curvature,” is strongly related to economic/financial uncertainty linked to systemic risk stemming from the US financial markets.


  Sovereign bonds; Yield curve; Term structure; Multilevel factor model; Global factors; Local factors.


Abbritti, M., Dell’Erba, S., Moreno, A., & Sola, S. 2018. Global factors in the term structure of interest rates. International Journal of Central Banking, 14(2), 301 - 340.Afonso, A., & Martins, M.M.F. 2012. Level, slope, curvature of the sovereign yield curve, and fiscal behaviour. Journal of Banking &amp Finance, 36(6), 1789 - 1807.Ahn, Seung C., & Horenstein, Alex R. 2013. Eigenvalue Ratio Test for the Number of Factors. Econometrica, 81(3), 1203 - 1227.Andreou, E., Gagliardini, P., Ghysels, E., & Rubin, M. 2019. Inference in Group Factor Models with an Application to Mixed-Frequency Data. Econometrica, 87(4), 1267 - 1305.Bae, Byung Yoon, & Kim, Dong Heon. 2011. Global and Regional Yield Curve Dynamics and Interactions: The Case of Some Asian Countries. International Economic Journal, 25(4), 717 - 738.Bai, J., & Wang, P. 2015. Identification and estimation of dynamic factor models. Journal of Business & Economic Statistics, 33(2), 221 - 240.Bai, Jushan, & Ng, Serena. 2002. Determining the Number of Factors in Approximate Factor Models. Econometrica, 70(1), 191 - 221.Baker, S., Bloom, N., Davis, S., & Kost, K. 2019. Policy News and Stock Market Volatility. NBER Working Paper 25720, mar.Baker, S.R., Bloom, N., & Davis, S.J. 2016. Measuring Economic Policy Uncertainty. The Quarterly Journal of Economics, 131(4), 1593 - 1636.Bhatt, Vipul, Kishor, N Kundan, & Ma, Jun. 2017. The impact of EMU on bond yield convergence: Evidence from a time-varying dynamic factor model. Journal of Economic Dynamics and Control, 82(Sep), 206 - 222.Byrne, J.P., Cao, S., & Korobilis, D. 2019. Decomposing global yield curve co- movement. Journal of Banking & Finance, 106(Sep), 500 - 513.Chen, P. 2012. Common Factors and Specific Factors. Unpublished Manuscript, MPRA Paper No. 36114.Chin, M., De Graeve, F., Filippeli, T., & Theodoridis, K. 2022. Understanding International Long-term Interest Rate Comovement. in Dolado, J.J., Gambetti, L. and Matthes, C. (Ed.) Essays in Honour of Fabio Canova (Advances in Econometrics, Vol. 44B), Emerald Publishing Limited, Bingley, Sep, 147 - 189.Choi, I., Kim, D., Kim, Y.J., & Kwark, H-S. 2018. A multilevel factor model: Identification, asymptotic theory and applications. Journal of Applied Econometrics, 33(3), 355 - 377.Choi, I., Lin, R., & Shin, Y. 2021. Canonical correlation-based model selection for the multilevel factors. Journal of Econometrics, oct.Chuhan, Punam, Claessens, Stijn, & Mamingi, Nlandu. 1998. Equity and bond flows to Latin America and Asia: the role of global and country factors. Journal of Development Economics, 55(2), 439 - 463.Coroneo, L., Garrett, I., & Sanhueza, J. 2018. Dynamic Linkages Across Country Yield Curves: The Effects of Global and Local Yield Curve Factors on US, UK and German Yields. Mili M., Samaniego Medina R., di Pietro F. (eds) New Methods in Fixed Income Modeling. Contributions to Management Science. Springer, Cham, 205 - 222.Dahlquist, Magnus, & Hasseltoft, Henrik. 2013. International Bond Risk Premia. Journal of International Economics, 90(1), 17 - 32.Diebold, F.X., & Li, C. 2006. Forecasting the term structure of government bond yields. Journal of Econometrics, 130(2), 337 - 364.Diebold, F.X., Rudebusch, G.D., & Aruoba, S.B. 2006. The macroeconomy and the yield curve: a dynamic latent factor approach. Journal of Econometrics, 131(1 - 2), 309 - 338.Diebold, F.X., Li, C., & Yue, V.Z. 2008. Global yield curve dynamics and interactions: A dynamic Nelson-Siegel approach. Journal of Econometrics, 146(2), 351 - 363.Han, Xu. 2021. Shrinkage Estimation of Factor Models With Global and Group- Specific Factors. Journal of Business & Economic Statistics, 39(1), 1 - 17.Jotikasthira, Chotibhak, Le, Anh, & Lundblad, Christian. 2015. Why do term structures in different currencies co-move? Journal of Financial Economics, 115(1), 58 - 83.Kaminska, I., Meldrum, A., & Smith, J. 2013. A global model of international yield curves: no-arbitrage term structure approach. International Journal of Finance & Economics, 18(4), 352 - 374.Kim, Dukpa, Kim, Yunjung, & Bak, Yuhyeon. 2017. Multilevel factor analysis of bond risk premia. Studies in Nonlinear Dynamics & Econometrics, 21(5).Kobayashi, T. 2020. Global Bond Market Interaction: An Arbitrage-free Dynamic Nelson Siegel Modeling Approach. Working paper. Availble at: , Apr., 1 - 22.Litterman, R.B., & Scheinkman, J. 1991. Common Factors Affecting Bond Returns. The Journal of Fixed Income, 1(1), 54 - 61.Modugno, M., & Nikolaou, K. 2009. The forecasting power of international yield curve linkages. European Central Bank, Working Paper Series, Apr., No. 1044.Moench, Emanuel. 2010. Term structure surprises: the predictive content of curvature, level, and slope. Journal of Applied Econometrics, 27(4), 574 - 602.Nelson, Charles R., & Siegel, Andrew F. 1987. Parsimonious Modeling of Yield Curves. The Journal of Business, 60(4), 473.Onatski, Alexei. 2010. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Review of Economics and Statistics, 92(4), 1004 - 1016.Perignon, Christophe, Smith, Daniel R., & Villa, Christophe. 2007. Why common factors in international bond returns are not so common. Journal of International Money and Finance, 26(2), 284 - 304.Puttmann, L. 2018. Patterns of Panic: Financial Crisis Language in Historical Newspapers. SSRN Electronic Journal.Rudebusch, G.D. 2010. Macro-finance models of interest rates and the economy. The Manchester School, 78(aug), 25 - 52.Stagnol, L. 2019. Extracting global factors from local yield curves. Journal of Asset Management, 20(5), 341 - 350.Stock, J.H., & Watson, M.W. 2011. Dynamic factor models. In: Clements, M.J., Hendry, D.F. (Eds.), Oxford Handbook on Economic Forecasting. Oxford University Press, Oxford, 35 - 39.Stolyarov, D., & Tesar, L.L. 2021. Interest rate trends in a global context. Economic Modelling, 101(Aug), 105532.Svensson, Lars E O. 1994. Estimating and interpreting forward interest rates: Sweden 1992-1994. NBER Working Paper No. 4871, sep, 1 - 47.Wright, J.H. 2011. Term Premia and Inflation Uncertainty: Empirical Evidence from an International Panel Dataset. American Economic Review, 101(4), 1514 - 1534.

A VAR model for Fiscal Multipliers and the Future of Fiscal Policy in European Monetary Union

Theodore Chatziapostolou and Nikolina Kosteletou

Correspondence: Theodore Chatziapostolou,

National and Kapodistrian University of Athens, Greece

pdf (675.22 Kb) | doi:


Fiscal multipliers have been a core issue for the effectiveness of fiscal policy. During the financial economic crisis of 2007–8 there has been a revival of interest in re-estimating the size of the multipliers. Empirical literature showed that fiscal multipliers are dependent either on structural characteristics of the economy (exchange rate regime, openness, etc.), or on business cycles or on fiscal characteristics (level of debt, the choice between expenditures and taxes, etc.) of the economies. The aim of this paper is to contribute to this discussion by developing a VAR model to compute the effects of fiscal policy to output for the 19 member states of EMU for the period 2002-2019. Controlling for size of the countries, level of Debt to GDP ratio and openness. Based on these findings we will discuss the difficulties of fiscal consolidation in EMU economies. We argue that EMU is facing a deadlock, the necessity of fiscal consolidation on the one hand and the unavoidable risk of uneven results of fiscal contraction in the member states due to different size of multipliers on the other hand. The only alternative for EMU is to take a step forward towards a fiscal union. In this case fiscal policy should be balance different political priorities and preferences and at the same time be timely and effective.


  Fiscal policy, European Monetary Union, debt, Fiscal cooperation, Fiscal multipliers


Afonso, A., & Leal, F. S. (2019). Fiscal multipliers in the Eurozone: an SVAR analysis. Applied Economics, 51(51), 5577-5593.

Alcalá, F., & Ciccone, A. (2004). Trade and productivity. The Quarterly journal of economics, 119(2), 613-646.

Amato G., Bassanini F., Messori M., Tosato G. (2022), “The new European fiscal framework: how to harmonise rules and discretion", ASTRID Paper, n. 81.

Auerbach, A. J., & Gorodnichenko, Y. (2012). Measuring the output responses to fiscal policy. American Economic Journal: Economic Policy, 4(2), 1-27.

Barrell, R., Holland, D., & Hurst, I. (2012). Fiscal consolidation: Part 2. Fiscal multipliers and fiscal consolidations.

Barro, R. J. (1989). The Ricardian approach to budget deficits. Journal of Economic perspectives, 3(2), 37-54.

Batini, N., Callegari, G., & Melina, G. (2012). Successful austerity in the United States, Europe and Japan.

Beetsma, R. M., Cimadomo, J., & Van Spronsen, J. (2022). One scheme fits all: a central fiscal capacity for the EMU targeting eurozone, national and regional shocks.

Bentour, E. M. (2022). The effects of public debt accumulation and business cycle on government spending multipliers. Applied Economics, 54(19), 2231-2256.

Blanchard, O., & Perotti, R. (2002). An empirical characterization of the dynamic effects of changes in government spending and taxes on output. the Quarterly Journal of economics, 117(4), 1329-1368.

Blanchard, O. J., Cottarelli, C., Spilimbergo, A., & Symansky, S. (2009). Fiscal policy for the crisis.

Blanchard, O. J., & Leigh, D. (2013). Growth forecast errors and fiscal multipliers. American Economic Review, 103(3), 117-20.

Blanchard, O. J., & Leigh, D. (2014). Learning about fiscal multipliers from growth forecast errors. IMF Economic Review, 62(2), 179-212.

Born, B., Juessen, F., & Müller, G. J. (2013). Exchange rate regimes and fiscal multipliers. Journal of Economic Dynamics and Control, 37(2), 446-465.

Burriel, P., Castro, F. D., Garrote, D., Gordo Mora, E., Paredes, J., & Pérez, J. J. (2010). Fiscal Multipliers in the Euro Area. Bank of Italy Occasional Paper.

Chatziapostolou T. (2022b). European Monetary Union Towards a Multi-speed ‘Fiscal Adjustment’ Europe, International Journal of Business and Economics Research. 11(4), 257-263.

Chian Koh, W. (2017). Fiscal multipliers: new evidence from a large panel of countries. Oxford Economic Papers, 69(3), 569-590.

Christiano, L., Eichenbaum, M., & Rebelo, S. (2011). When is the government spending multiplier large?. Journal of Political Economy, 119(1), 78-121.

Collingro, F., & Frenkel, M. (2020). Fiscal multipliers in the euro area: A comparative study⋆. The Quarterly Review of Economics and Finance.

Corsetti, G., & Müller, G. J. (2013). Multilateral economic cooperation and the international transmission of fiscal policy. In Globalization in an Age of Crisis: Multilateral Economic Cooperation in the Twenty-First Century (pp. 257-297). University of Chicago Press.

Cwik, T., & Wieland, V. (2011). Keynesian government spending multipliers and spillovers in the euro area. Economic Policy, 26(67), 493-549.

Deleidi, M., Iafrate, F., & Levrero, E. S. (2021). Government investment fiscal multipliers: evidence from Euro-area countries. Macroeconomic Dynamics, 1-19.

Eggertsson, G. B. (2011). What fiscal policy is effective at zero interest rates?. NBER Macroeconomics Annual, 25(1), 59-112.

Eggertson, G., & Krugman, P. (2012). Debt, Deleveraging, and The Liquidity Trap‖. Quarterly Journal of Economics, 1469-1513.

Galí, J., López-Salido, J. D., & Vallés, J. (2007). Understanding the effects of government spending on consumption. Journal of the european economic association, 5(1), 227-270.

Giavazzi, F., & Pagano, M. (1990). Can severe fiscal contractions be expansionary? Tales of two small European countries. NBER macroeconomics annual, 5, 75-111.

Horvath, R., Kaszab, L., Marsal, A., & Rabitsch, K. (2020). Determinants of fiscal multipliers revisited. Journal of Macroeconomics, 63, 103162.

Hory, M. P. (2016). Fiscal multipliers in Emerging Market Economies: can we learn something from Advanced Economies?. International Economics, 146, 59-84.

Ianc, N. B., & Turcu, C. (2020). So alike, yet so different: Comparing fiscal multipliers across EU members and candidates. Economic Modelling, 93, 278-298.

Ilzetzki, E., Mendoza, E. G., & Végh, C. A. (2013). How big (small?) are fiscal multipliers?. Journal of monetary economics, 60(2), 239-254.

Laxton, D., & Pesenti, P. (2003). Monetary rules for small, open, emerging economies. Journal of Monetary Economics, 50(5), 1109-1146.

Leeper, E. M., Walker, T. B., & Yang, S. C. S. (2008). Fiscal foresight: analytics and econometrics (No. w14028). National Bureau of Economic Research.

Leigh, D., Devries, P., Freedman, C., Guajardo, J., Laxton, D., & Pescatori, A. (2010). Will it hurt? Macroeconomic effects of fiscal consolidation. World Economic Outlook, 93, 124.

Li, K., Morck, R., Yang, F., & Yeung, B. (2004). Firm-specific variation and openness in emerging markets. Review of Economics and Statistics, 86(3), 658-669.

Ramey, V. A., & Zubairy, S. (2018). Government spending multipliers in good times and in bad: evidence from US historical data. Journal of Political Economy, 126(2), 850-901.

Ratto, M., Roeger, W., & in't Veld, J. (2009). QUEST III: An estimated open-economy DSGE model of the euro area with fiscal and monetary policy. economic Modelling, 26(1), 222-233.

Riera-Crichton, D., Vegh, C. A., & Vuletin, G. (2015). Procyclical and countercyclical fiscal multipliers: Evidence from OECD countries. Journal of International Money and Finance, 52, 15-31.

Reinhart, C. M., & Rogoff, K. S. (2010). Growth in a Time of Debt. American economic review, 100(2), 573-578.

Silva, R., Carvalho, V. M., & Ribeiro, A. P. (2013). How large are fiscal multipliers? A panel-data VAR approach for the Euro area. FEP Working Papers no. 500, August, Porto, Portugal: Faculty of Economics University of Porto,.

Smets, F., & Wouters, R. (2003). An estimated dynamic stochastic general equilibrium model of the euro area. Journal of the European economic association, 1(5), 1123-1175.

Squalli, J., & Wilson, K. (2011). A new measure of trade openness. The World Economy, 34(10), 1745-1770.

Tang, K. K. (2011). Correcting the size bias in trade openness and globalization measures. Global Economy Journal, 11(3), 1850235.

Turrini, A., Röger, W., & Székely, I. P. (2012). Banking crises, output loss, and fiscal policy. CESifo Economic Studies, 58(1), 181-219.

Woodford, M. (2011). Simple analytics of the government expenditure multiplier. American Economic Journal: Macroeconomics, 3(1), 1-35.