About Me
I am an Associate Professor in Econometrics and Business Statistics at Melbourne Business School, University of Melbourne.
I have previously worked at Deakin University, Monash University and the University of Tasmania since I obtained my PhD degree from the Department of Econometrics and Business Statistics, Monash University in December 2013.
My research covers time series econometrics in general, with a particular focus on the applications of innovative econometric and statistical tools to macroeconomic and financial market data. I have worked on a wide range of research projects in high frequency financial econometrics, multivariate time series modelling, empirical finance, macro-econometrics, macro-financial linkages, and interdisciplinary projects on forecasting. My research has been published in top field journals in econometrics, including Journal of Econometrics, Journal of Business & Economic Statistics, Journal of Applied Econometrics, among other outlets.
You can find out more about my research here:
- MBS Faculty Profile: https://mbs.edu/faculty-and-research/faculty/wenying-yao
- Google Scholar: https://scholar.google.com.au/citations?user=vb1gpjsAAAAJ&hl=en
- SSRN: https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=2598966
- RePEc: https://ideas.repec.org/f/pya365.html
- The University of Melbourne: https://findanexpert.unimelb.edu.au/profile/852821-wenying-yao
- LinkedIn: https://au.linkedin.com/in/wenying-yao-b2588516
News and updates
| Nov.2023 | I have been promoted to Associate Professor at Melbourne Business School from November 2023. |
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| Feb.2022 | I will be joining Melbourne Business School as an Assistant Professor in Econometrics and Business Statistics from March 2022. |
| Dec.2021 | My project "High-frequency Estimation of Term Structure Models at the Zero Lower Bound" has been funded by the Australian Research Council Discovery Project. I will be working with Dr. Bonsoo Koo (Monash University) and Dr. Lars Winkelmann (Freie Universität Berlin) on this project in 2022--2025. |
| Nov.2021 | My promotion application to Associate Professor is successful, effective from January 2022. |
| Nov.2020 | My project "New Insights on Modelling Time Trends with Panel Data: Theory and Practice" has been funded by the Australian Research Council Discovery Project. I will be working with Dr. Bin Peng (Monash University) and Prof. Joakim Westerlund (Deakin University and Lund University) on this project in 2021--2023. |
| Sep.2018 | My promotion application to Senior Lecturer is successful, effective from January 2019. |
| Aug.2016 | I joined Deakin University as a Lecturer in Economics. |
| Apr.2016 | I've started working at Monash University as a Research Fellow until July 2016. |
| Dec.2015 | I will be attending the ASSA/AEA meeting in San Francisco during 3-5 January 2016. |
| Nov.2015 | My application to the "Australia-Germany Joint Research Co-operation Scheme" has been successful. I will be working with Dr Lars Winkelmann (Free University of Berlin) on the project "Identifying Financial Market Contagion in Real Time" in 2016-2017. |
| Apr.2015 | My paper "The role of intra-day volatility pattern in jump detection" has been accepted by the 11th World Congress of the Econometric Society, to be held in August 2015 in Montreal, Canada. |
| Dec.2013 | I attended the graduation ceremony on 18th December 2013 at Monash University and obtained my doctoral degree in Econometrics. |
| Apr.2013 | I started to work as a post-doctoral research fellow at School of Economics and Finance, University of Tasmania, Australia. |
| Feb.2013 | I submitted my PhD dissertation titled "Vector ARMA Model and Macroeconomic Modeling: Some New Methodology and Algorithms". |
| Dec.2012 | I presented my paper "Forecasting with Error Correction VARMA Models" in the seminar series at the School of Economics and Finance, University of Tasmania. |
| Apr.2010 | I won Econometric Game 2010 held in Amsterdam with other 4 teammates representing Monash University. (Monash news link) |
Curriculum Vitae
Positions Held
| Nov.2023---Now | Associate Professor, Melbourne Business School, University of Melbourne |
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| Mar.2022---Nov.2023 | Assistant Professor, Melbourne Business School, University of Melbourne |
| Aug.2016---Feb.2022 | Associate Professor/Senior Lecturer/Lecturer, Department of Economics, Deakin University |
| Apr.2016---Jul.2016 | Research Fellow, Department of Econometrics and Business Statistics, Monash University |
| Apr.2013---Mar.2016 | Research Fellow, Tasmanian School of Business and Economics, University of Tasmania |
Education
| Dec.2013 | PhD in Econometrics, Department of Econometrics and Business Statistics, Monash University |
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| Jun.2008 | Bachelor of Economics, Bachelor of Science (Mathematics), Double-degree program, School of Economics, Renmin University of China |
Research Interests
| Financial econometrics |
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| Macroeconometrics |
| Multivariate time series |
Publications
- Tail connectedness: Measuring the volatility connectedness network of equity markets during crises (with Tingting Cheng, Fei Liu and Junli Liu), Pacific-Basin Finance Journal, 2024.
- Tests for jumps in yield spreads (with Lars Winkelmann), Journal of Business & Economic Statistics, 2024.
- Identifying changes in the distribution of income from higher-order moments with an application to Australia (with Vance Martin, Jialu Shi and Yang Song), Australian & New Zealand Journal of Statistics, 2024.
- The Impact of forward guidance and large-scale asset purchase programs on commodity markets (with Pedro Gomis-Porqueras and Shuddha Rafiq), Studies in Nonlinear Dynamics & Econometrics, 2023.
- An examination of herding behavior of the Chinese mutual funds: A time-varying perspective (with Tingting Cheng and Shuo Xing), Pacific-Basin Finance Journal, 2022.
- Characterizing financial crises through the spectrum of high frequency data (with Mardi Dungey, Jet Holloway and Abdullah Yalaman), Quantitative Finance, 2022.
- The impact of COVID-19 pandemic on the volatility connectedness network of global stock market (with Tingting Cheng, Junli Liu and Albert Bo Zhao), Pacific-Basin Finance Journal, 2022.
- Forecasting the volatility of asset returns: The informational gain from option prices (with Vance Martin and Chrismin Tang), International Journal of Forecasting, 2021.
- Modelling financial contagion using high frequency data (with Mardi Dungey and Vitali Alexeev), Economic Record, 2020.
- Jump risk in the US financial sector (with Dinesh Gajurel, Mardi Dungey and Nagaratnam Jeyasreedharan), Economic Record, 2020.
- High-dimensional predictive regression in the presence of cointegration (with Heather Anderson, Bonsoo Koo and Myung Hwan Seo), Journal of Econometrics, 2020.
- Asymmetric jump beta estimation with implications for portfolio risk management (with Vitali Alexeev and Giovanni Urga), International Review of Economics and Finance, 2019.
- News and expected returns in east Asian equity markets: The RV-GARCHM model (with Vance Martin and Chrismin Tang), Journal of Asian Economics, 2018.
- High frequency characterization of Indian banking stocks (with Mohammad Sayeed and Mardi Dungey), Journal of Emerging Markets Finance, 2018.
- Vector autoregressions and macroeconomic modelling: An error taxonomy (with Don Poskitt), Journal of Business & Economic Statistics, 2017.
- On weak identification in structural VAR(MA) models (with Timothy Kam and Farshid Vahid), Economics Letters (lead article), 2017.
- Time-varying continuous and jump betas: The role of firm characteristics and periods of stress (with Vitali Alexeev and Mardi Dungey), Journal of Empirical Finance (lead article), 2017.
- Determination of long-run and short-run dynamics in EC-VARMA models via canonical correlations (with George Athanasopoulos, Don Poskitt and Farshid Vahid), Journal of Applied Econometrics, 2016.
- Continuous and jump betas: Implications for portfolio diversification (with Vitali Alexeev and Mardi Dungey), Econometrics, 2016.
Conference Proceedings
- Perceptions and Use of Building Price Data in Australia (with Anthony Mills, Argaw Gurmu, Citra Ongkowijoyo, Alexia Nalewaik, Anthony Leiberman), Association for the Advancement of Cost Engineering International Conference, Chicago. USA, 2023.
- The value of price data for quantity surveying professionals in Australia (with Anthony Mills, Alexia Nalewaik, Argaw Gurmu, Citra Ongkowijoyo and Anthony Lieberman), PAQS 2022: Disruption and Transformation in the Built Environment: Proceedings of the Pacific Association of Quantity Surveyors 2022 congress, Singapore, 2022.
Working Papers
- A constrained dynamic Nelson-Siegel model for monetary policy analysis (with Jamie Cross, Aubrey Poon, and Dan Zhu).
- Multiple testing for the topology of financial networks (with Richard Luger and Matthew Greenwood-Nimmo).
- Optimal bandwidth selection for forecasting under parameter instability (with Yu Bai, Bin Peng and Shuping Shi), under review.
- ycevo: An R package for nonparametric yield curve estimation, analyses and prediction (with Fin Yang, Bonsoo Koo, and Nico Purnomo).
- Cojump anchoring (with Lars Winkelmann).
- Do market-wide circuit breakers calm markets or panic them? Evidence from COVID-19 pandemic (with Xiaoyang Li), revise & resubmit.
- Construction price data -- Perceptions, utilization, and modeling (with Anthony Mills, Argaw Gurmu, Citra Ongkowijoyo, Alexia Nalewaik, and Anthony Leiberman).
Research Grants
- Australian Research Council Discovery Project ($312,355), 2022--2025
- Australian Research Council Discovery Project ($313,000), 2021--2024
- Australian Institute of Quantity Surveyors funding ($18,181), 2021--2022
- Female-led Research Study Seed Funding, Deakin Business School, 2019
- Faculty Research Grant, Deakin Business School, Deakin University, 2018
- Start-up Research Grant, Department of Economics, Deakin University, 2017
- Start-up Research Grant, Department of Economics, Deakin University, 2016
- Australia-Germany Joint Research Co-operation Scheme ($20,800), 2016--2017
- Econometric Society World Congress 2015 Travel Grant, 2015
Invited Seminars
| Oct.2024 | School of Economics, University of Queensland |
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| Aug.2024 | School of Economics, University of Sydney |
| Aug.2024 | Department of Actuarial Studies and Business Analytics, Macquarie University |
| May.2023 | Research School of Economics, Australian National University |
| Mar.2022 | Department of Economics, University of Melbourne |
| Aug.2021 | Department of Econometrics and Business Statistics, Monash University |
| Sep.2020 | Melbourne Online Seminar, University of Melbourne/Monash University |
| Jul.2019 | Department of Economics, University of Hamburg |
| Apr.2019 | Institute of Economics, Academia Sinica |
| Mar.2019 | School of Economics, University of Adelaide |
| May.2017 | School of Economics, University of Queensland |
| Nov.2016 | School of Business and Economics, Free University of Berlin |
| Sep.2016 | Tasmanian School of Business and Economics, University of Tasmania |
| Apr.2016 | Department of Economics, University of Melbourne |
| Oct.2015 | Department of Economics, Macquarie University |
| Sep.2015 | Research School of Finance, Actuarial Studies and Applied Statistics, Australian National University |
| Jul.2015 | Hanqing Advanced Institute of Economics and Finance, Renmin (People's) University of China |
| Jun.2015 | School of Business and Economics, Free University of Berlin |
| May.2015 | Department of Econometrics and Business Statistics, Monash University |
| Sep.2014 | QUT Business School, Queensland University of Technology |
| May.2014 | School of Accounting, Economics and Finance, Deakin University |
| Apr.2014 | Reserve Bank of New Zealand |
| Dec.2012 | School of Economics and Finance, University of Tasmania |
Conference/Workshop Presentations
| Dec.2024 | Econometric Society Australasian Meeting, Melbourne Australia |
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| Jul.2024 | International Symposium on Forecasting, Dijon France |
| Jun.2024 | Society for Financial Econometrics Annual Conference, Rio de Janeiro Brazil |
| Nov.2023 | Workshop in honour of Professors Donald Poskitt and Gael Martin, Melbourne Australia |
| Jul.2023 | Asia Meeting of the Econometric Society, Beijing China |
| Jun.2023 | Society of Financial Econometrics Annual Conference, Seoul Korea |
| Dec.2022 | European Winter Meeting of the Econometric Society, Berlin Germany |
| Nov.2022 | FIRN Annual Conference, Hamilton Island Australia |
| Nov.2022 | Continuing Education in Macroeconometrics Workshop, Melbourne Australia |
| Jul.2022 | International Symposium on Econometric Theory and Applications, Online |
| Jul.2021 | Econometric Society Australasian Meeting, Online |
| Jun.2021 | International Conference on Econometrics and Statistics, Online |
| Feb.2020 | Australia New Zealand Econometrics Study Group Conference, Melbourne Australia |
| Dec.2019 | International Conference on Computational and Financial Econometrics, London UK |
| Dec.2019 | Workshop of the Australasian Macroeconomic Society, Hobart Australia |
| Nov.2019 | Time Series and Forecasting Symposium, Sydney Australia |
| Jun.2019 | International Symposium on Forecasting, Thessaloniki Greece |
| Jun.2019 | International Symposium on Econometric Theory and Applications, Osaka Japan |
| Feb.2019 | Deakin Business School Centre for Banking and Financial Stability Workshop, Melbourne Australia |
| Dec.2018 | International Conference on Computational and Financial Econometrics, Pisa Italy |
| Dec.2018 | (EC)^2 Conference on Big Data Econometrics with Applications, Rome Italy |
| Jun.2017 | Society of Financial Econometrics Annual Conference, New York USA |
| Mar.2017 | Midwest Finance Association Annual Meeting, Chicago USA |
| Dec.2016 | International Conference on Computational and Financial Econometrics, Seville Spain |
| Jul.2016 | Conference on Financial Econometrics & Empirical Asset Pricing, Lancaster UK |
| Jun.2016 | Conference of the International Association of Applied Econometrics, Milan Italy |
| Nov.2015 | SIRCA Young Researcher Workshop, Sydney Australia |
| Aug.2015 | World Congress of the Econometric Society, Montreal Canada |
| Jun.2015 | Society of Financial Econometrics Annual Conference, Aarhus Denmark |
| Jun.2015 | International Workshop on Financial Markets and Nonlinear Dynamics, Paris France |
| Feb.2015 | New Zealand Econometrics Study Group Meeting, Brisbane Australia |
| Jan.2015 | Western Economic Association International Conference, Wellington New Zealand |
| Jul.2014 | Econometric Society Australasian Meeting, Hobart Australia |
| Jun.2014 | China Meeting of Econometric Society, Xiamen China |
| Dec.2013 | Auckland Finance Meeting, Auckland New Zealand |
| Nov.2013 | FIRN Annual Conference, Sydney Australia |
| Nov.2013 | SIRCA Young Researcher Workshop, Sydney Australia |
| Oct.2013 | Annual Meeting of the Midwest Econometrics Group, Bloomington Indiana USA |
| Feb.2013 | Young Statistician Conference, Melbourne Australia |
| Jul.2012 | Econometric Society Australasian Meeting, Melbourne Australia |
| Jun.2012 | International Symposium on Forecasting, Boston USA |
| May.2012 | International Symposium on Econometric Theory and Applications, Shanghai China |
| Jul.2011 | Workshop on Macroeconomic Dynamics, Brisbane Australia |
| Jul.2011 | Monash Macro Workshop, Melbourne Australia |
| Jul.2011 | Australian Conference of Economists, Canberra Australia |
| Jul.2011 | Econometric Society Australasian Meeting, Adelaide Australia |
Awards
- Research Paper Award, the 28th SFM Conference, 2020
- Department of Economics Research Award, Deakin University, 2020
- Department of Economics Ambassador Award, Deakin University, 2020
- Department of Economics Research Award, Deakin University, 2019
- Department of Economics Ambassador Award, Deakin University, 2019
- Career Development Scholarship, University of Tasmania, 2015
- Small Research Grant, Tasmanian School of Business and Economics, University of Tasmania, 2014
- Postgraduate Publication Award, Monash Institute of Graduate Research, Monash University, 2013
- Winner of Econometric Game 2010 (representing Monash University), Amsterdam, 2010
- Faculty of Business and Economics Postgraduate Research Scholarship, Monash University, 2010--2013
Computer Skills
- Matlab
- R
- GAUSS
- Eviews
Publications
Tail connectedness: Measuring the volatility connectedness network of equity markets during crises (with Tingting Cheng, Fei Liu and Junli Liu), Pacific-Basin Finance Journal, 2024.
Abstract: This paper studies the global volatility connectedness network among 16 stock markets under different market conditions. We construct measures of tail connectedness following Ando et al. (2022) by introducing quantile regression into the classic Diebold–Yilmaz network model. We demonstrate the advantages of using tail connectedness for measuring extreme systemic risk, and examine the dynamic evolution of volatility connectedness from 2005 to 2021 at different quantiles. Our empirical results suggest that when the market is calm, the strength of volatility connectedness is determined by the closeness of economic and trade ties. Although (North) American and European stock markets tend to act as net risk providers during crises, Asian markets have become increasingly influential in the past two decades. We also find that the spillover of extreme risks is predominantly unidirectional, with either the U.S. or China sitting at the center of the spillover network and transmitting risks to the regional centers and peripheral markets.
Tests for jumps in yield spreads (with Lars Winkelmann), Journal of Business & Economic Statistics, 2024.
Abstract: This paper studies high-frequency econometric methods to test for a jump in the spread of bond yields. We propose a coherent inference procedure that detects a jump in the yield spread only if at least one of the two underlying bonds displays a jump. Ignoring this inherent connection by basing inference only on a univariate jump test applied to the spread tends to overestimate the number of jumps in yield spreads and puts the coherence of test results at risk. We formalize the statistical approach in the context of an intersection union test in multiple testing. We document the relevance of coherent tests and their practicability via simulations and real data examples.
Identifying changes in the distribution of income from higher-order moments with an application to Australia (with Vance Martin, Jialu Shi and Yang Song), Australian & New Zealand Journal of Statistics, 2024.
Abstract: Changes in the distribution of income over time are identified based on an adjusted two-sample version of the Neyman smooth test developed in Bera, Ghosh and Xiao (2013) by using subsampling methods to approximate the sampling distribution of the test statistic when samples are not independent of each other. A range of Monte Carlo experiments show that the approach corrects for size distortions arising from dependent samples as well as generating monotonic power functions. Applying the approach to studying the distribution of income in Australia over the business cycle and the Global Financial Crisis, the empirical results highlight the importance of higher-order moments and demonstrate that business cycles are not all alike as the relative strengths of higher-order moments vary over phases of the cycle.
The Impact of Forward Guidance and Large-scale Asset Purchase Programs on Commodity Markets (with Pedro Gomis-Porqueras and Shuddha Rafiq), Studies in Nonlinear Dynamics & Econometrics, 2022.
Abstract: This paper investigates how different commodity prices are affected by unconventional monetary policies (UMP) implemented by the Federal Reserve of the United States as a response to the Global Financial Crisis. We analyze impulse responses using local projections and identify UMP shocks through high-frequency identification strategy. We show that forward guidance (FG) and large-scale asset purchase (LSAP) shocks lead to distinct responses when analyzing commodity prices. We find that asset-like commodities, such as gold and silver, respond to these UMP shocks most aggressively. While an easing FG shock leads to increases in their prices, an easing LSAP shock has the opposite effect. This differential response suggests that these asset-like commodities are being used as inflation and exchange rate hedges. In contrast, production-like and agricultural commodities respond to UMP shocks in the same way as conventional monetary policy shocks. Consistent with previous literature, we find that easing LSAP shocks, to some extent, signal a negative economic outlook. Policymakers can exploit the responses from the various commodity classes examined in this paper when evaluating the effectiveness of monetary policy in different sectors of the economy.
An examination of herding behavior of the Chinese mutual funds: A time-varying perspective (with Tingting Cheng and Shuo Xing), Pacific-Basin Finance Journal, 2022.
Abstract: This paper investigates the existence of herding behaviour in the Chinese mutual fund market from a time-varying perspective. We examine the relationship between the dispersion of fund returns and the fund market returns using a Markov regime switching approach, and explore the driving factors of herding under different regimes. Our results suggest that herding behaviour is time-varying and heterogeneous across different fund types, investment styles, fund sizes, and industrial groups. In addition, the observed herding behaviours are mainly driven by non-fundamentals. We also find that herding behaviour is more pronounced during the up market and becomes insignificant during the down market.
Characterizing financial crises through the spectrum of high frequency data (with Mardi Dungey, Jet Holloway and Abdullah Yalaman), Quantitative Finance, 2022.
Abstract: Recent advances in high frequency financial econometrics enable us to characterize which components of the data generating processes change in crisis, and which do not. This paper introduces a new statistic which captures large discontinuities in the composition of a given price series. Monte Carlo simulations suggest that this statistic is useful in characterizing the tail behaviour across different sample periods. An application to US Treasury market provides evidence consistent with identifying periods of stress via flight-to-cash behaviour which results in increased abrupt price falls at the short end of the term structure, and decreased negative price jumps at the long end.
The impact of COVID-19 pandemic on the volatility connectedness network of global stock market (with Tingting Cheng, Junli Liu and Albert Bo Zhao), Pacific-Basin Finance Journal, 2022.
Abstract: This paper investigates how the COVID-19 pandemic affects the connectedness network of stock market volatility in 19 economies around the world. Our method builds on the Diebold-Yilmaz volatility network model to construct the volatility spillover index, and uses lag sparse group LASSO to accommodate the high-dimensional system. We find that the outbreak of the COVID-19 pandemic strengthens the overall volatility connectedness, and the global connectedness level remains high throughout 2020. In particular, connections across different continents have become stronger during this period. However, China is shown to be disconnected from the global volatility connectedness network until late November 2020. We find evidence that China is not the main source of volatility spillover during the COVID-19 pandemic.
Forecasting the volatility of asset returns: The informational gain from option prices (with Vance Martin and Chrismin Tang), International Journal of Forecasting, 2021.
Abstract: A new class of forecasting models is proposed that extends the Realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices resulting in a set of cross-equation restrictions on the model's parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term 1-day ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate 5-day ahead forecasts.
Modelling financial contagion using high frequency data (with Mardi Dungey and Vitali Alexeev), Economic Record, 2020.
Abstract: This paper develops a methodology for detecting and measuring contagion using high frequency data which disentangles continuous and discontinuous price movements. We demonstrate its finite sample properties using Monte-Carlo simulation, focusing on the empirically plausible parameter space. Decisions to extend the role of financial regulation around the world to the supervision of insurers post-GFC has been met with literature which supports both the systemic importance of insurers and contrasting evidence that insurers are rather the 'victims' of shocks transmitted via banks. We contribute to this debate by considering the time-varying evidence for contagion at both the firm level and the sectorial level impacts. A number of insurance companies exhibits bank-like characteristics. Our evidence for contagion effects from banks to the real economy, with similar impact from the insurers, supports the view that financial regulation on banks does need to be extended to the insurance sector.
Jump risk in the US financial sector (with Dinesh Gajurel, Mardi Dungey and Nagaratnam Jeyasreedharan), Economic Record, 2020.
Abstract: In this paper, we establish empirical evidence for the relationship between the systematic jump betas of financial institutions and two types of systemic risk indices: a capital shortfall index and a interconnectedness index. Using high-frequency data for US financial sector stocks, we show that equity market jumps are related positively to capital shortfall and negatively to interconnectedness. Higher potential capital shortfall measures of systemic risk lead to a greater sensitivity to systematic jumps, while increased interconnectedness leads to greater resistance. Our findings, along with indicators such as size and leverage, provide a means to identify the possible trade-offs that regulators might face when assessing the systemic risks of financial institutions, particularly in the context of the cross-multiple influences within the sector.
High-dimensional predictive regression in the presence of cointegration (with Heather Anderson, Bonsoo Koo and Myung Hwan Seo), Journal of Econometrics, 2020.
Abstract: We propose a Least Absolute Shrinkage and Selection Operator (LASSO) estimator of a predictive regression in which stock returns are conditioned on a large set of lagged covariates, some of which are highly persistent and potentially cointegrated. We establish the asymptotic properties of the proposed LASSO estimator and validate our theoretical findings using simulation studies. The application of this proposed LASSO approach to forecasting stock returns suggests that a cointegrating relationship among the persistent predictors leads to a significant improvement in the prediction of stock returns over various competing forecasting methods with respect to mean squared error.
Asymmetric jump beta estimation with implications for portfolio risk management (with Vitali Alexeev and Giovanni Urga), International Review of Economics and Finance, 2019.
Abstract: We evaluate the impact of extreme market shifts on equity portfolios and study the difference in negative and positive reactions to market jumps with implications for portfolio risk management. Employing high-frequency data for the constituents of the S&P500 index over the period 2 January 2003 to 30 December 2017, we investigate to what extent the portfolio exposure to the downside and upside jumps can be mitigated. We contrast the risk exposure of individual stocks with those of the portfolios as the number of holdings increases. Varying the jump identification threshold, we show that the number of holdings required to stabilise portfolios’ sensitivities to negative jumps is higher than when positive jumps are considered and that the asymmetry is more prominent for more extreme events. Ignoring this asymmetry results in under-diversification of portfolios and increases exposure to sudden extreme negative market shifts.
News and expected returns in east Asian equity markets: The RV-GARCHM model (with Vance Martin and Chrismin Tang), Journal of Asian Economics, 2018.
Abstract: Using intraday data to construct realized variance estimates combined with daily data on equity returns from January 1996 to May 2017, equity markets in East Asia are found to be relatively more risky than other markets. The framework uses an intertemporal capital asset pricing model with conditional moments based on realized volatility and a GARCH-in-mean specification to study the impact of news. Significant non-linear dynamics are also identified, with a positive relationship between expected returns and news associated with small shocks, and a negative relationship for large shocks. A similar relationship is found for the Australian market, but not for the US and UK equity markets.
High frequency characterization of Indian banking stocks (with Mohammad Sayeed and Mardi Dungey), Journal of Emerging Markets Finance, 2018.
Abstract: Using high-frequency stock returns in the Indian banking sector, we find that the beta on jump movements substantially exceeds that on the continuous component, and that the majority of the information content for returns lies with the jump beta. We contribute to the debate on strategies to decrease systemic risk, showing that increased bank capital and reduced leverage reduce both jump and continuous beta with slightly stronger effects for capital on continuous beta and stronger effects for leverage on jump beta. However, changes in these firm characteristics need to be large to create an economically meaningful change in beta.
Vector autoregressions and macroeconomic modelling: An error taxonomy (with Don Poskitt), Journal of Business & Economic Statistics, 2017.
Abstract: In this article, we investigate the theoretical behavior of finite lag VAR(n) models fitted to time series that in truth come from an infinite-order VAR(∞) data-generating mechanism. We show that the overall error can be broken down into two basic components, an estimation error that stems from the difference between the parameter estimates and their population ensemble VAR(n) counterparts, and an approximation error that stems from the difference between the VAR(n) and the true VAR(∞). The two sources of error are shown to be present in other performance indicators previously employed in the literature to characterize, so-called, truncation effects. Our theoretical analysis indicates that the magnitude of the estimation error exceeds that of the approximation error, but experimental results based upon a prototypical real business cycle model and a practical example indicate that the approximation error approaches its asymptotic position far more slowly than does the estimation error, their relative orders of magnitude notwithstanding. The experimental results suggest that with sample sizes and lag lengths like those commonly employed in practice VAR(n) models are likely to exhibit serious errors of both types when attempting to replicate the dynamics of the true underlying process and that inferences based on VAR(n) models can be very untrustworthy.
On weak identification in structural VAR(MA) models (with Timothy Kam and Farshid Vahid), Economics Letters (lead article), 2017.
Abstract: We simulate synthetic data from known data generating processes (DGPs) that arise from economic theory, and compare the performance of fitted VAR and VARMA models in estimating the true impulse responses to structural shocks. We show that while the VARMA structures implied by these DGPs are theoretically identified and lead to precise estimates of impulse responses given enough data, their parameters are close to the non-identified ridge in the parameter space, and that makes precise estimation of the impulse responses in small samples typical of macroeconomic data improbable. As a result, VARMA models barely show any advantage over VARs in characterizing the known DGPs in small samples. This is a refinement of the conjecture that near non-stationarity, near non-invertibility or weak identification could be possible reasons for the failure of structural VARMA models in providing good estimates of theoretical impulse responses of particular DSGE models.
Time-varying continuous and jump betas: The role of firm characteristics and periods of stress (with Vitali Alexeev and Mardi Dungey), Journal of Empirical Finance (lead article), 2017.
Abstract: Using high frequency data we decompose the time-varying beta for stocks into beta for continuous systematic risk and beta for discontinuous systematic risk. Estimated discontinuous betas for S & P500 constituents over 2003-2011 generally exceed the corresponding continuous betas. Smaller stocks are more sensitive to discontinuities than their larger counterparts, and during periods of financial distress, high leverage stocks are more exposed to systematic risk. Higher credit ratings and lower volatility are each associated with smaller betas. Industry effects are also apparent. We use the estimates to show that discontinuous risk carries a significantly positive premium, but continuous risk does not.
Determination of long-run and short-run dynamics in EC-VARMA models via canonical correlations (with George Athanasopoulos, Don Poskitt and Farshid Vahid), Journal of Applied Econometrics, 2016.
Abstract: This article studies a simple, coherent approach for identifying and estimating error-correcting vector autoregressive moving average (EC-VARMA) models. Canonical correlation analysis is implemented for both determining the cointegrating rank, using a strongly consistent method, and identifying the short-run VARMA dynamics, using the scalar component methodology. Finite-sample performance is evaluated via Monte Carlo simulations and the approach is applied to modelling and forecasting US interest rates. The results reveal that EC-VARMA models generate significantly more accurate out-of-sample forecasts than vector error correction models (VECMs), especially for short horizons.
Continuous and jump betas: Implications for portfolio diversification (with Vitali Alexeev and Mardi Dungey), Econometrics, 2016.
Abstract: Using high-frequency data, we decompose the time-varying beta for stocks into beta for continuous systematic risk and beta for discontinuous systematic risk. Estimated discontinuous betas for S&P500 constituents between 2003 and 2011 generally exceed the corresponding continuous betas. We demonstrate how continuous and discontinuous betas decrease with portfolio diversification. Using an equiweighted broad market index, we assess the speed of convergence of continuous and discontinuous betas in portfolios of stocks as the number of holdings increase. We show that discontinuous risk dissipates faster with fewer stocks in a portfolio compared to its continuous counterpart.
Working Papers
Multiple testing for the topology of financial networks (with Richard Luger and Matthew Greenwood-Nimmo).
Optimal bandwidth selection for forecasting under parameter instability (with Yu Bai, Bin Peng and Shuping Shi).
A constrained dynamic Nelson-Siegel model for monetary policy analysis (with Jamie Cross, Aubrey Poon, and Dan Zhu).
Tail connectedness: Measuring the network connectedness of equity markets during crises (with Tingting Cheng and Junli Liu).
Cojump anchoring (with Lars Winkelmann).
Do market-wide circuit breakers calm markets or panic them? Evidence from COVID-19 pandemic (with Xiaoyang Li).
Teaching
2024
- Data Analytics for Decision Making, Online MBA, Melbourne Business School
- Statistical Learning for Business, Master of Business Analytics, Melbourne Business School
2023
- Data Analysis, MBA, Melbourne Business School
- Statistical Learning for Business, Master of Business Analytics, Melbourne Business School
2022
- Predictive Analytics, Master of Business Analytics, Melbourne Business School
- Data Analysis, MBA, Melbourne Business School
2021
- Analytical Methods in Economics and Finance, 2nd year undergrad, Department of Economics, Deakin University
- Economic Policy and Practice, 3rd year undergrad, Department of Economics, Deakin University
2020
- Analytical Methods in Economics and Finance, 2nd year undergrad, Department of Economics, Deakin University
2017---2019
- Advanced Econometrics, 1st year PhD, Department of Economics, Deakin University
- Applied Econometrics for Economics and Finance, 3rd year undergrad, Department of Economics, Deakin University
2015
- FIRN masterclass PhD Course---High Frequency Empirical Finance: A Practical Introduction to Time Series Problems, invited by Financial Integrity Research Network (FIRN) in July 2015
- Econometrics, 3rd year undergrad, Tasmanian School of Business and Economics, University of Tasmania
- Banking and Financial Institutions (guest lecturer), Tasmanian School of Business and Economics, University of Tasmania
2014
- Econometrics, 3rd year undergrad, Tasmanian School of Business and Economics, University of Tasmania
- Honours Finance (guest lecturer), Tasmanian School of Business and Economics, University of Tasmania
- Honours Econometrics (guest lecturer), Tasmanian School of Business and Economics, University of Tasmania
2010---2012
- Introductory Econometrics (tutor), Department of Econometrics & Business Statistics, Monash University
2009
- Econometric Modeling (tutor), School of Economics, The Australian National University