Ai Jun Hou

Ai Jun Hou

Associate professor

Visa sidan på svenska
Works at Stockholm Business School
Telephone 08-674 70 97
Visiting address Kräftriket, hus 7
Room 7:135A
Postal address Företagsekonomiska institutionen 106 91 Stockholm

About me

Ai Jun Hou is a professor in the finance group, Stockholm Business School. Hou has a broad research interest:  empirical asset pricing, International finance and financial microstructure.   Dr. Hou has also conducted research in the financial networks (Trading network and Social capital networks).  She has published in the Journal of Financial StabilityJournal of Financial Econometrics, Journal of Empirical Finance, Quantitative Finance, Journal of International Financial Markets, Institutions and Money, etc. 

Hou received the Ph.D. in Financial Economics from the Lund University in 2011. She joined the Stockholm University Stockholm Business School (SBS) in 2013. Before joining SBS, she worked as an assistant professor (tenure tracked) in the Business and Economics department, University of Southern Denmark from May 2011 to July 2013 . 

Hou is also the head of finance section. She teaches courses in both master level (Financial Institutions Management) and bachelor level (Empirical Finance).



Empirical Finance (final year of Bachelor level) and Financial Instituitions Management (Master level)


Ai Jun Hou's research is mainly conducted in the empirical asset pricing and financial econometrics, i.e., the financial market volatility and correlations modelling, etc.



A selection from Stockholm University publication database
  • 2016. Asma Mobarek (et al.). Journal of Financial Stability 24, 1-11

    In this paper, we use the DCC MIDAS approach to assess the validity of the wake-up call hypothesis for developed and emerging markets during the global financial crisis (GFC). We use this approach to decompose the total correlations into short- (daily) and long-run (quarterly) correlations for the period from 1999 to 2011. We then examine the transmission mechanisms by regressing the quarterly economic, financial, and behavioral variables on the quarterly DCC–MIDAS correlations. We find that country specific factors are crisis contingent transmission mechanisms for the co-movements of emerging country pairs and mixed pairs of advanced and emerging countries during the global financial crisis. However, we do not observe wake-up calls in the transmission of the crisis among advanced country pairs. The classification of the transmission mechanisms for crisis and non-crisis periods with the different country pairs has important implications for crisis management as well as for portfolio investment strategies. Thus, our findings contribute to the discussion on the role and effectiveness of the international financial architecture.

  • 2016. Hossein Asgharian, Charlotte Christiansen, Ai Jun Hou. Journal of Financial Econometrics 14 (3), 617-642

    We investigate long-run stock–bond correlation using a model that combines the dynamic conditional correlation model with the mixed-data sampling approach and allows long-run correlation to be affected by macro-finance factors (historical and forecasts). We use macro-finance factors related to inflation and interest rates, illiquidity, state of the economy, and market uncertainty. Macro-finance factors, particularly their forecasts, are good at forecasting long-run stock–bond correlation. Supporting the flight-to-quality phenomenon, long-run correlation tends to be small and negative when the economy is weak.

  • 2015. Hossein Asgharian, Charlotte Christiansen, Ai Jun Hou. Finance Research Letters 13, 10-16

    In this paper we show that the long-run stock and bond volatility and the long-run stock-bond correlation depend on macroeconomic uncertainty. We use the mixed data sampling (MIDAS) econometric approach. The findings are in accordance with the flight-to-quality phenomenon when macroeconomic uncertainty is high.

  • 2015. Hou Ai Jun. Journal of Forecasting

    This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict shortterm and long-term components of the return variance. We investigate several alternative models and use a large group of economic variables. A principal component analysis is used toincor porate the information contained in different variables. Our results show that including low frequency macroeconomic information into the GARCH-MIDAS model improves the prediction ability of the model, particularly for the long-term variance component. Moreover, the GARCHMIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle.

  • 2013. Hou Ai Jun. Quantitative finance (Print) 13 (3), 451-470

    This paper examines the spillover effects from the short term interest rates market to equity markets within the Euro area. The empirical study is carried out by estimating an extended Markov Switching GJR in mean model with a Bayesian based Markov Chain Monte Carlo (MCMC) methodology. The result indicates that stock markets in the Euro area display a significant two regimes with distinct characteristics. Within a bear market regime, stock returns have a negative relationship with the volatility, and the volatility process responds asymmetrically to negative shocks of equity returns. The other regime appears to be a bull market regime, within which the returns have a positive relationship with the volatility, and the volatility is lower and more persistent. We find also that there is a significant impact of fluctuations in the short term interest rate on the conditional variance and conditional returns in the EMU countries. Such impact is asymmetrical, and it appears to be stronger in the bear market and when interest rate changes upward.

  • 2011. Hou Ai Jun, Suardi Sandy. Journal of Empirical Finance 18 (4), 692-710

    This paper employs a semiparametric procedure to estimate the diffusion process of short-term interest rates. The Monte Carlo study shows that the semiparametric approach produces more accurate volatility estimates than models that accommodate asymmetry, level effect and serial dependence in the conditional variance. Moreover, the semiparametric approach yields robust volatility estimates even if the short rate drift function and the underlying innovation distribution are misspecified. Empirical investigation with the U.S. three-month Treasury bill rates suggests that the semiparametric procedure produces superior in-sample and out-of-sample forecast of short rate changes volatility compared with the widely used single-factor diffusion models. This forecast improvement has implications for pricing interest rate derivatives.

  • 2013. Hou Ai Jun. Journal of international financial markets, institutions, and money 23, 12-32

    The unique characteristics of the Chinese stock markets make it difficult to assume a particular distribution for innovations in returns and the specification form of the volatility process when modelling return volatility with the parametric GARCH family models. This paper therefore applies a generalized additive nonparametric smoothing technique to examine the volatility of the Chinese stock markets. The empirical results indicate that an asymmetric effect of negative news exists in the Chinese stock markets. Furthermore, compared with other parametric models, the generalized additive nonparametric model demonstrates a better performance for return volatility forecasts, particularly for the out-of-sample forecast. The results from this paper have important implications in risk management, portfolio selection, and hedging strategy.

  • 2012. Hou Ai Jun, Suardi Sandy. Energy Economics 34 (2), 618-626

    The use of parametric GARCH models to characterise crude oil price volatility is widely observed in the empirical literature. In this paper, we consider an alternative approach involving nonparametric method to model and forecast oil price return volatility. Focusing on two crude oil markets, Brent and West Texas Intermediate (WTI), we show that the out-of-sample volatility forecast of the nonparametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. These results are supported by the use of robust loss functions and the Hansen's (2005) superior predictive ability test. The improvement in forecasting accuracy of oil price return volatility based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to the commonly used parametric GARCH models.

  • Hou Ai Jun, Norden Lars. Journal of futures markets
Show all publications by Ai Jun Hou at Stockholm University

Last updated: March 5, 2021

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