Dimos joined the 我要吃瓜 in September 2012 as a Lecturer in Finance. Before joining the 我要吃瓜, Dimos worked for the University of Edinburgh Business School and Durham Business School. He obtained his PhD from the University of St. Andrews School of Management and holds an MSc in Finance and Investment from Durham University, an MSc in Computer Based Information Systems and a BA (Hons) in Business Administration from the University of Sunderland. Dimos is a Fellow of the Higher Education Academy.
Dimo’s research looks at modelling and forecasting the volatility of stock markets in both developed and emerging markets, and with applications to risk management. Taking into account a number of different parameters such as the model, in and out of sample periods, trading volume and the volatility index; the main aim is on improving the accuracy of the volatility forecasts. Since high volatility is associated with uncertainty market disruption, accurately modelling and forecasting volatility helps manage and control risk.
Article
Khasawneh M, McMillan D & Kambouroudis D (2024) Left-Tail Risk and UK Stock Return Predictability: Underreaction, Overreaction, and Arbitrage Difficulties. International Review of Financial Analysis, 95 (A), Art. No.: 103333. https://doi.org/10.1016/j.irfa.2024.103333
Article
Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets
Korkusuz B, Kambouroudis D & McMillan DG (2023) Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets. Finance Research Letters. https://doi.org/10.1016/j.frl.2023.103992
Article
Do Artificial Neural Networks Provide Improved Volatility Forecasts: Evidence from Asian Markets
McMillan D, Kambouroudis D & Sahiner M (2023) Do Artificial Neural Networks Provide Improved Volatility Forecasts: Evidence from Asian Markets. Journal of Economics and Finance.
Article
Expected Profitability, the 52-Week High and the Idiosyncratic Volatility Puzzle
McMillan D, Kambouroudis D & Khasawneh M (2022) Expected Profitability, the 52-Week High and the Idiosyncratic Volatility Puzzle. European Journal of Finance. https://doi.org/10.1080/1351847X.2022.2144401
Article
Lottery Stocks in the UK: Evidence, Characteristics and Cause
McMillan D, Kambouroudis D & Khasawneh M (2022) Lottery Stocks in the UK: Evidence, Characteristics and Cause. International Journal of Banking, Accounting and Finance.
Article
Korkusuz B, McMillan D & Kambouroudis D (2022) Complex Network Analysis of Volatility Spillovers between Global Financial Indicators and G20 Stock Markets. Empirical Economics. https://doi.org/10.1007/s00181-022-02290-w
Article
Kambouroudis D, McMillan D & Tsakou K (2021) Forecasting realized volatility: The role of implied volatility, leverage effect, overnight returns, and volatility of realized volatility. Journal of Futures Markets, 41 (10), pp. 1618-1639. https://doi.org/10.1002/fut.22241
Article
Forecasting Realised Volatility: Does the LASSO approach outperform HAR?
Ding Y, Kambouroudis D & McMillan D (2021) Forecasting Realised Volatility: Does the LASSO approach outperform HAR?. Journal of International Financial Markets, Institutions and Money, 74, Art. No.: 101386. https://doi.org/10.1016/j.intfin.2021.101386
Article
Cross-border exchanges and volatility forecasting
Goyal A, Kallinterakis V, Kambouroudis DS & Laws J (2018) Cross-border exchanges and volatility forecasting. Quantitative Finance, 18 (5), pp. 789-799. https://doi.org/10.1080/14697688.2017.1414512
Article
Volatility forecasting across tanker freight rates: the role of oil price shocks
Gavriilidis K, Kambouroudis DS, Tsakou K & Tsouknidis DA (2018) Volatility forecasting across tanker freight rates: the role of oil price shocks. Transportation Research Part E: Logistics and Transportation Review, 118, pp. 376-391. https://doi.org/10.1016/j.tre.2018.08.012
Article
Does VIX or Volume Improve GARCH Volatility Forecasts?
Kambouroudis DS & McMillan D (2016) Does VIX or Volume Improve GARCH Volatility Forecasts?. Applied Economics, 48 (13), pp. 1210-1228. https://doi.org/10.1080/00036846.2015.1096004
Article
Kambouroudis DS, McMillan D & Tsakou K (2016) Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models. Journal of Futures Markets, 36 (12), pp. 1127-1163. https://doi.org/10.1002/fut.21783
Book Chapter
Kambouroudis DS (2016) Modeling and Forecasting Stock Market Volatility in Frontier Markets: Evidence from four European and Four African Frontier Markets. In: Andrikopoulos P, Gregoriou N & Kallinterakis V (eds.) Handbook of Frontier Markets: The African, European and Asian Evidence. Amsterdam and New York: Elsevier, pp. 39-54. http://store.elsevier.com/Handbook-of-Frontier-Markets/isbn-9780128037768/
Article
Is there an ideal in-sample length for forecasting volatility?
Kambouroudis DS & McMillan DG (2015) Is there an ideal in-sample length for forecasting volatility?. Journal of International Financial Markets, Institutions and Money, 37, pp. 114-137. https://doi.org/10.1016/j.intfin.2015.02.006
Article
Are RiskMetrics forecasts good enough? Evidence from 31 stock markets
McMillan D & Kambouroudis DS (2009) Are RiskMetrics forecasts good enough? Evidence from 31 stock markets. International Review of Financial Analysis, 18 (3), pp. 117-124. https://doi.org/10.1016/j.irfa.2009.03.006