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Quantitative Research in Financial Economics
A research group of the Business School.
Our group focuses on all aspects of quantitative and empirical financial modeling and draws expertise from areas as diverse as applied stochastic modeling, financial econometrics, banking, microstructure and asset pricing. The work of the group is underpinned by state-of-the-art data facilities that drive the financial risk measures contained in The Durham Finance Laboratory (coming soon).
Quantitative modeling, backed up by appropriate empirical research in relation to financial decision-making, is a topic of ongoing importance to policy-makers, financial practitioners, academics and the public at large. The mechanisms that set the interest rates on our mortgages and loans, the prices of everyday goods and services, the value of our pensions and the security of our economy relies to a greater or lesser extent on the ability to model correctly the distribution of future outcomes and hence price the degree of risk inherent within our economic system.
The Future of Quantitative Research
The various interconnected financial crises that have occurred between 2007 and 2012 have brought about a substantive change in the public understanding of the financial system. Part of our research agenda is to push forward into new areas of research relating to financial markets.
Traditional asset pricing approaches to financial markets, commonly taught in most finance courses, have focused, directly or indirectly, on the completeness argument. In a complete financial market the universe of global assets is priced relative to a finite number of states-of-the-world that have a random chance of occurring. If there are more assets than states we can use linear combinations of the states to construct prices for these assets. From this simple set-up all of our current asset pricing models have evolved, including famous models such as the capital asset pricing model and the Black and Scholes option-pricing model. The complete markets set-up is mathematically very elegant and with simple extensions makes very precise predictions on the fluctuations of financial markets.
Unfortunately, our experience during the 2007 to 2012 era has indicated that the effectiveness of these models in pricing stocks, bonds, derivatives and commodities has been very poor. Even with the introduction of more complex non-linear adjustments to the complete market approach we have not been able to capture fully the realities of the financial risks we face or the persistence of the positive and negative bubbles we have observed in various markets.
In the period leading up to 2007, financial services embraced financial engineering founded in the principles of complete markets and no-arbitrage, eschewing many of the ideas around bounded rationality and market failure that had risen to prominence in the economics literature in the prior thirty years. In the post-crisis era we are now beginning to see a re-emergence of the need to treat the agents within a financial market as strategic players with differentiated information, and this provides the backdrop for the research ideas that permeate through our group.
Part of our research agenda focuses on addressing this problem and our approach is multi-faceted. For instance, most traded underlying assets, such as stocks and commodities, have an associated derivatives market (a contract whose value is directly related to the value of the underlying asset). These markets are used to both hedge risk and leverage bets on future valuations of the underlying asset. The traditional framework provides a specific rule for the valuation of these derivatives and part of our team is involved in using the discrepancies in the predicted and actual valuations to gain insight into why our traditional asset pricing models are breaking down.
Another approach from the group focuses on the actual trading mechanism within financial markets and if the relatively new highly automated trading systems are leading to systematic inefficiencies and frictions in how we value stocks, commodities, bonds, currencies, futures, options and other financial assets. This research looks at the minutiae of trading, observing how each and every quote and who is making the quote affects the aggregate direction of asset prices. It also now appears that market participants with technological superiority in computational speed and high levels of influence appear to have systematic advantages over other participants in the market. The combination of these inefficiencies also appears to be important in explaining the inadequacies of our traditional approaches.
Faculty, Durham University Business School, Mill Hill Lane, Durham DH1 3LB
- Dr Majid Al Sadoon
- Dr Anurag Narayan Banerjee
- Dr Frankie Chau
- Tahani Coolen-Maturi (in the Department of Mathematical Sciences)
- Dr Damian Damianov
- Dr Ekaterina Damianova
- Dr Rataporn Deesomsak
- Professor Muhammed-Shahid Ebrahim
- Dr Ahmed Elsayed
- Dr Chulwoo Han
- Mr Terry Harris
- Dr Arzé Karam
- Dr Anamaria Nicolae
- Dr Nikos Paltalidis
- Professor Dennis Philip
- Prof Abderrahim Taamouti
- Professor Julian Williams
- Dr Yeqin Zeng
- Professor Qi Zhang
Publications by staff in this group
- Song, X. & Taamouti, A. (2018). Measuring Nonlinear Granger Causality in Mean. Journal of Business and Economics Statistics 36(2): 321-333.
- Ramos, S.B., Taamouti, A., Veiga, H. & Wang, C.-W. (2017). Do investors price industry risk? Evidence from the cross-section of the oil industry. Journal of Energy Markets 10(1): 79-108.
- Han, C. & Taamouti, A. (2017). Partial Structural Break Identification. Oxford Bulletin of Economics and Statistics 79(2): 145-164.
- Belalia, M., Bouezmarni, T., Lemyre, F.C. & Taamouti, A. (2017). Testing Independence Based on Bernstein Empirical Copula and Copula Density. Journal of Nonparametric Statistics 29(2): 346-380.
- Bogoev, D. & Karam, A. (2017). Detection of algorithmic trading. Physica A: Statistical Mechanics and its Applications 484: 168-181.
- Karam, A. (2017). The effects of intraday news flow on market liquidity, price volatility and trading activity. Economics Bulletin 37(4): 2354-2363.
- Buckle, M., Chen, J. & Williams, J. (2016). Realised higher moments theory and practice. European journal of finance 22(13): 1272-1291.
- Gomes, P. & Taamouti, A. (2016). In search of the determinants of European asset market comovements. International Review of Economics and Finance 44: 103-117.
- Buckland, R., Williams, J. & Beecher, J. (2015). Risk and regulation in water utilities a cross-country comparison of evidence from the CAPM. Journal of regulatory economics 47(2): 117-145.
- Harris, T. (2015). Credit scoring using the clustered support vector machine. Expert Systems with Applications 42(2): 741-750.
- Taamouti, A. (2015). Finite-Sample Sign-Based Inference in Linear and Nonlinear Regression Models with Applications in Finance. L'actualité économique: revue d'analyse économique 91(1-2): 89-113.
- Taamouti, A. (2015). Stock Market's Reaction to Money Supply: Nonparametric Analysis. Studies in Nonlinear Dynamics and Econometrics 19(5): 669-689.
- Banerjee, A. & Banik, N. (2014). Is India Shining?. Review of Development Economics 18(1): 59-72.
- Chau, F., Deesomsak, R. & Wang, J. (2014). Political Uncertainty and Stock Market Volatility in the Middle East and North African (MENA) Countries. Journal of International Financial Markets, Institutions and Money 28: 1-19.
- Buckle, M., Chen, J. & Williams, J. (2014). How predictable are equity covariance matrices? Evidence from high frequency data for four markets. Journal of Forecasting 33(7): 542-557.
- Luque, J. & Taamouti, A. (2014). Did the Euro Change the Effect of Fundamentals on Economic Uncertainty?. The B.E. Journal of Macroeconomics 14(1): 625-660.
- Taamouti, A., Bouezmarni, T. & El Gouch, A. (2014). Nonparametric estimation and inference for conditional density based Granger causality measures. Journal of Econometrics 180(2): 251-264.
- Bouezmarni, T. & Taamouti, A. (2014). Nonparametric Tests for Conditional Independence Using Conditional Distribution. Journal of Nonparametric Statistics 26(4): 697-719.
- Feunou, B., Fontaine, J.S., Taamouti, A. & Tédongap, R. (2014). Risk Premium, Variance Premium, and the Maturity Structure of Uncertainty. Review of Finance 18(1): 219-269.
- Afonso, A., Gomes, P. & Taamouti, A. (2014). Sovereign Credit Ratings and Financial Markets Volatility. Computational Statistics and Data Analysis 76: 20-33.
- Hung, C.D. & Banerjee, A. (2013). Active Momentum Trading versus Passive "1/N Naive Diversification. Quantitative Finance 13(5): 655-663.
- Banerjee, A. (2013). Sensitivity of detrended long-memory processes. Communications in Statistics: Theory and Methods 42(20): 3770-3780.
- Zhang, Z., Chau, F. & Li, X. (2013). Accumulation of Large Foreign Reserves in China: A Behavioural Perspective. Economic Change and Restructuring 46(1): 85-108.
- Zhang, Z., Chau, F. & Zhang, W. (2013). Exchange Rate Determination and Dynamics in China: A Market Microstructure Analysis. International Review of Financial Analysis 29: 303-316.
- Chau, F., Dosmukhambetova, G. & Kallinterakis, V. (2013). International Financial Reporting Standards and Noise Trading: Evidence from Central and Eastern European Countries. Journal of Applied Accounting Research 14(1): 37-53.
- Damianov, D.S. & Pagan, J.A. (2013). Health Insurance Coverage, Income Distribution and Healthcare Quality in Local Healthcare Markets. Health Economics 22(8): 987-1002.
- Calice, G., Chen, J. & Williams, J. (2013). Are there benefits to being naked? The returns and diversification impact of capital structure arbitrage. The European Journal of Finance 19(9): 815-840.
- Calice, G., Chen, J. & Williams, J. (2013). Liquidity spillovers in sovereign bond and CDS markets: An analysis of the Eurozone sovereign debt crisis. Journal of Economic Behavior & Organization 85: 122-143.
- Harris, T. (2013). Quantitative credit risk assessment using support vector machines: broad versus narrow default definitions. Expert Systems with Applications 40(11): 4404-4413.
- Bouezmarni, T., El Gouch, A. & Taamouti, A. (2013). Bernstein estimator for unbounded copula densities. Statistics & Risk Modeling 30(4): 343-360.
- Bouaddi, M. & Taamouti, A. (2013). Portfolio Selection in a Data-Rich Environment. Journal of Economic Dynamics and Control 37(12): 2943-2962.
- Banerjee, A. (2012). Discriminating short and long memory in finite samples using sensitivity analysis: an application to growth convergence. Bulletin of economic research 64(s1): 168-192.
- Suvankulov, F., Lau, M. & Chau, F. (2012). Job search on the Internet and its outcome. Internet Research 22(3): 298-317.
- Lau, M., Suvankulov, F., Su, Y. & Chau, F. (2012). Some Cautions on the Use of Nonlinear Panel Unit Root Tests: Evidence from a Modified Series-specific Non-linear Panel Unit-root Test. Economic Modelling 29(3): 810–816.
- Damianov, D.S. (2012). Seller Competition by Mechanism Design. Economic Theory 51(1): 105-137.
- Damianov, D.S. & Sanders, S. (2012). Why Don't You Two Get a Room? A Puzzle and Pricing Model of Extra Services in Hotels. Journal of Industrial Organization Education 6(1): 1035.
- McMillan, D.G. & Philip, D. (2012). Short-sale constraints and efficiency of the spot-futures dynamics. International Review of Financial Analysis 24: 129-136.
- Calice, G., Ionnadis, C. & Williams, J. (2012). Credit Derivatives and the Default Risk of Large Complex Financial Institutions. Journal of Financial Services Research 42(1-2): 85-107.
- Ioannidis, C., Pym, D. & Williams, J. (2012). Information Security Trade-offs and Optimal Patching Policies. European Journal of Operational Research 216(2): 434-444.
- Bouezmarni, T., Rombouts, J.V.K. & Taamouti, A. (2012). A Nonparametric Copula Based Test for Conditional Independence with Applications to Granger Causality. Journal of Business & Economic Statistics 30(2): 275-287.
- Taamouti, A., Dufour, J-M. & Garcia, R. (2012). Measuring High-Frequency Causality Between Returns, Realized Volatility and Implied Volatility. Journal of Financial Econometrics 10(1): 124-163.
- Taamouti, A. (2012). Moments of Multivariate Regime Switching with Application to Risk-Return Trade-Off. Journal of Empirical Finance 19(2): 292-308.
- Taamouti, A. & Bouaddi, M. (2012). Portfolio Risk Management in a Data-Rich Environment. Financial Markets and Portfolio Management 26(4): 469-494.
- Banerjee, A. & Hung, C.-H. (2011). Informed Momentum Trading versus Uninformed "Naive" Investors Strategies. Journal of Banking and Finance 35(11): 3077-3089.
- Chau, F., Deesomsak, R. & Lau, M. (2011). Investor Sentiment and Feedback Trading: Evidence from the Exchange-Traded Fund Markets. International Review of Financial Analysis 20(5): 292-305.
- Damianov, D.S. (2011). A Classroom Experiment on Status Goods and Consumer Choice. Journal of Economics and Finance Education 10(1): 1-13.
- Damianov, D.S. & Sanders, S. (2011). Status Spending Races, Cooperative Consumption, and Voluntary Public Income Disclosure: A Classroom Experiment. International Review of Economic Education 10(1): 29-53
- Calafiore, P. & Damianov, D.S. (2011). The Effect of Time Spent Online on Student Achievement in Economics and Finance Online Courses. Journal of Economic Education 42(3): 209-223.
- Williams, J. & Ioannidis, C. (2011). Multivariate Asset Price Dynamics with Stochastic Covariation. Quantitative Finance 11(1): 125-134.
- Chen, J., Buckland, R. & Williams, J. (2011). Regulatory Changes, Market Integration and Spillover Effects in the Chinese A, B and Hong Kong Equity Markets. Pacific-Basin Finance Journal 19(4): 351-373.
- Taamouti, A., Tsafack, G. & Amira, K. (2011). What Drives International Equity Correlations? Volatility or Market Direction? Journal of International Money and Finance 30(6): 1234-1263.
- Damianov, D.S. & Becker, J.G. (2010). Auctions with Variable Supply: Uniform Price versus Discriminatory. European Economic Review 54(4): 571-593
- Damianov, D.S., Oechssler, J. & Becker, J.G. (2010). Uniform vs. Discriminatory Auctions with Variable Supply—Experimental Evidence. Games and Economic Behavior 68(1): 60-76.
- Bouezmarn, T., Rombouts, Jeroen V.K. & Taamouti, A. (2010). Asymptotic properties of the Bernstein density copula estimator for α-mixing data. Journal of Multivariate Analysis 101(1): 1-10.
- Taamouti, A. & Dufour, J-M. (2010). Exact Optimal and Adaptive Inference in Linear and Nonlinear Models under Heteroskedasticity and Non-Normality of Unknown Forms. Journal of Computational Statistics and Data Analysis 54(11): 2532-2553.
- Taamouti, A. & Dufour, J-M. (2010). Short and Long Run Causality Measures: Theory and Inference. Journal of Econometrics 154(1): 42-58.
- Taamouti, A. , Roy, R. & Bouezmarni, T. (2010). Asymptotic and Small Sample Properties of Conditional-Distribution-based Tests for Conditional. Proceedings of the Business and Economic Statistics Section of the American Statistical Association 1436-1447.
- Taamouti, A. (2009). Analytical Value-at-Risk and Expected Shortfall under Regime Switching. Finance Research Letters 6(3): 138-151.
- Taamouti, A. & Dufour, J-M. (2006). Nonparametric Short and Long Run Causality Measures. Proceedings of the Business and Economic Statistics Section of the American Statistical Association 3986-3992.
Chapter in book
- Chau, F., Lau, M. & Su, Y. (2013). Commodity Futures and Strategic Asset Allocation. In Alternative Investments: Instruments, Performance, Benchmarks, and Strategies. John Wiley & Sons. 399-418.
- Ioannidis, C., Pym, D. & Williams, J. (2013). Fixed Costs, Investment Rigidities, and Risk Aversion in Information Security: A Utility-theoretic Approach. In Economics of Information Security and Privacy III. Schneier, B. New York: Springer Verlag. III: 171-192.
- Calice, G., Chen, J. & Williams, J. (2013). Liquidity Spillovers in Credit Markets During the Eurozone Crisis. In Financial Crisis Containment and Government Guarantees. LaBrosse, J.R., Olivares-Caminal, R. & Singh, D. Edward Elgar Publishing.
- Zhang, Z., Chau, F. & Shi, N. (2012). A curious Partnership in Global Imbalances: China’s Continual Accumulation of US Treasuries. In China's Role in Global Economic Recovery. Fu, X Routledge. 18-40.
- Philip, D. (2012). Modelling volatility and correlations in financial time series. In Introductory Econometrics – A Practical Approach. Seddighi, H.R. Routledge.