Alexeev, V & Ignatieva, K 2025, 'Quantifying Extreme Risks in High-Frequency Financial, Energy, and Commodity Markets', Studies in Nonlinear Dynamics & Econometrics.
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Abstract This paper develops a robust framework for tail risk assessment in financial, energy, and commodity markets leveraging Tail Conditional Expectation (TCE) within the versatile family of skewed Generalised Hyperbolic (GH) distributions. The GH distributions effectively capture the asymmetric, heavy-tailed behaviour of high-frequency market data, which, in combination with TCE, addresses the limitations of traditional risk measures like Value-at-Risk (VaR). Focusing on exchange-traded funds (ETFs) – USO (crude oil), GLD (gold), and SPY (S & P 500) – we extend the TCE measure to multivariate portfolios and decompose portfolio-level risk into individual asset contributions. This provides clear insights into each asset’s role in overall tail risk. To capture evolving market dynamics, we implement a rolling-window analysis for time-varying TCEs, validated through rigorous backtesting. The results demonstrate the model’s conservative and reliable performance in extreme risk prediction. By offering a flexible and accurate approach to quantify tail risks, our study empowers market participants to manage volatility, navigate systemic shocks, and design effective risk mitigation strategies across diverse financial environments.
Alfeus, M, Mwampashi, MM, Nikitopoulos, CS & Overbeck, L 2025, 'Stochastic modelling and forecasting of wind capacity utilization with applications to risk management: The Australian case', Pacific-Basin Finance Journal, vol. 91, pp. 102769-102769.
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Bird, R, Gallagher, DR, Khan, A & Yeung, D 2025, 'Do Emotions Influence Investor Behavior?', Journal of Behavioral Finance, vol. 26, no. 2, pp. 229-250.
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Leong, M, Kwok, S & Alexeev, V 2025, 'Managing Bitcoin Risk Exposures in Equity Portfolios: Evidence from High-Frequency Data', Journal of International Financial Markets, Institutions and Money, vol. 99, pp. 102123-102123.
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Liu, Z, Nikitopoulos, CS, Phua, K & Wang, J 2025, 'Data-driven monetary policy: Evidence from the Bank of Japan’s equity purchase program', Pacific-Basin Finance Journal, vol. 90, pp. 102615-102615.
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McEwen, C, Bajada, C, Cotton, D, Wallace, K & Waller, DS 2025, 'Philanthropy and Indigenous Initiatives: Insights From Australian Donors', Australian Journal of Social Issues.
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ABSTRACTThis paper draws on a survey and interview data, collected from a group of 180 donors who made monetary gifts to an Australian higher education institution, to better understand what drives individuals and organisations to donate to Indigenous initiatives. The analysis helped to identify five types of donors: the Advocate and Prescriptive donors, the supporter types who donate to Australian Indigenous initiatives and the Constrained, Reserved and Opposing donors, non‐supporter types who withhold from donating to such initiatives. The results show a wide range of motivations, varied attitudes and multiple perceived barriers to donating to Australian Indigenous initiatives. The study reveals that while some donors do engage and reflect on issues around the role of philanthropy in achieving positive outcomes for Indigenous people, others' motivations and barriers to donating confirm the concerns of scholars who have shown how philanthropy can maintain inequality and colonial practices. This paper also provides practical implications for developing a reflexive approach to philanthropy that supports Indigenous initiatives.
Nguyen, DT, Michayluk, D, Van de Venter, G & Walker, S 2025, 'Improvement in sustainability: Evidence from the mergers and acquisitions market', Australian Journal of Management, vol. 50, no. 2, pp. 484-523.
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One approach to improve a firm’s Environmental, Social, and Governance (ESG) rating is to acquire a target with a higher relative ESG rating. We explore changes in an acquirer’s ESG rating around merger and acquisition (M&A) announcements and provide empirical evidence of a positive relationship between the change in an acquirer’s ESG rating and the target’s relative ESG rating. Of the three components of an ESG rating, an acquirer’s environmental rating displays the largest increase, with social and governance ratings exhibiting a smaller but still significant post-merger increase. This relationship is weaker for cross-border deals or cross-industry deals. However, deals that are both cross-border and same-industry are associated with a larger increase in an acquirer’s ESG rating. In addition to improved ESG ratings, the acquisition of a firm with a superior ESG rating is also associated with higher bid premiums and improved post-merger financial performance which suggests that acquirers act in shareholders’ best interests. JEL Classification: G14, G34
Richards, K-A, Dunsmuir, WTM & Peters, GW 2025, 'Score test for marks in Hawkes processes', International Journal of Data Science and Analytics, vol. 20, no. 3, pp. 3037-3052.
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Abstract A score statistic for detecting the impact of marks in a linear Hawkes self-exciting point process is proposed, with its asymptotic properties, finite sample performance, power properties using simulation and application to real data presented. A major advantage of the proposed inference procedure is that the Hawkes process can be fitted under the null hypothesis that marks do not impact the intensity process. Hence, for a given record of a point process, the intensity process is estimated once only and then assessed against any number of potential marks without refitting the joint likelihood each time. Marks can be multivariate and serially dependent. The score function for any given set of marks is easily constructed as the covariance of functions of future intensities fits the unmarked process with functions of the marks under assessment. The asymptotic distribution of the score statistic is a Chi-squared distribution, with degrees of freedom equal to the number of parameters required to specify the boost function. Model-based or nonparametric estimation of required features of the mark’s marginal moments and serial dependence can be used. Using sample moments of the marks in the test statistic construction does not impact the size and power properties.
Saunders, A, Spina, A, Steffen, S & Streitz, D 2025, 'Corporate Loan Spreads and Economic Activity', The Review of Financial Studies, vol. 38, no. 2, pp. 507-546.
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Abstract We investigate the predictive power of loan spreads for forecasting business cycles, specifically focusing on more constrained, intermediary-reliant firms. We introduce a novel loan-market-based credit spread constructed using secondary corporate loan-market prices over the 1999 to 2023 period. Loan spreads significantly enhance the prediction of macroeconomic outcomes, outperforming other credit-spread indicators. We also explore the underlying mechanisms and differentiate between borrower fundamentals and financial frictions. Evidence suggests that supply-side frictions are a decisive factor in the forecasting ability of loan spreads.
Liu, Z, Nikitopoulos, CS, Phua, K & Wang, J 1970, 'Data-driven monetary policy: Evidence from the Bank of Japan's equity purchase program', Pacific Basin Finance Journal, 30th Annual Conference of Multinational Finance Society, ELSEVIER, Vaasa, Finland.
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Central banks’ monetary policies are increasingly data-driven. To assess the consequences of such monetary policies, we study the equity ETF purchase program of the Bank of Japan (BOJ). We find that BOJ interventions under this program are highly predictable. To exploit this predictability, we design a portfolio strategy that buys (sells) stocks that are more (less) exposed to these interventions. Adjusting for known risk factors, this strategy earns a return of 12.90% per annum. BOJ interventions have a more positive impact on riskier stocks. Thus, the ETF purchase program aligns with the BOJ's goals of reducing equity risk premium by offering downside protection during periods of market uncertainty.
Dahiya, S, Hallak, I & Matthys, T 2025, 'Cash holdings and relationship lending'.
Phua, K, Sang, B, Wei, C & Yu, GY 2025, 'The Economics of Financial Scams: Evidence from Initial Coin Offerings', SSRN.
Phua, K, Wei, C & Yu, G 2025, 'The Digital Economy and Consumer Fraud', SSRN.
