Let the fight (to publish) begin:

In theoretical econometrics, my work also revolves around the statistical properties of long memory stochastic volatility models, and the autocorrelation function of exponential autoregressive conditional duration models.

In mathematical finance, I provide a closed form solution for the equilibrium yield curve in the special case where the interest rate is given by a mixture of autoregressive and random walk processes.

In the area of

Time Series Econometrics

I have recently written the following papers:

1. ” A unified theory for the family of time varying models with ARMA representations: One solution fits all”:

(revised and resubmitted, third round)

The paper develops a widely applicable approach to examine the dynamics of the large family of `time varying’ models with ARMA representations. We provide the general solution, which gives rise to explicit or analytic formulas for all the main time series properties of these processes, such as the optimal forecasts and impulse response functions. The practical significance of the proposed methodology is illustrated with an application to U.S. inflation data. The main finding is that inflation persistence has been high since 1967, whereas in the post-crisis period the persistence reduces but it remains higher than the pre-1967 levels.

2. “On the Transmission of memory in GARCH-in-mean models”:

(Journal of Time Series Analysis, 2015;

edited by Robert Taylor)

In this article, we show that in times series models with in-mean and level effects, persistence will be transmitted from the conditional variance to the conditional mean and vice versa. Hence, by studying the conditional mean/variance independently, one will obtain a biased estimate of the true degree of persistence. For the specific example of an AR(1)-APARCH(1,1)- in-mean-level process, we derive the autocorrelation function, the impulse response function and the optimal predictor. Under reasonable assumptions, the AR(1)-APARCH(1,1)-in-mean-level process will be observationally equivalent to an autoregressive moving average (ARMA)(2,1) process with the largest autoregressive root being close to one. We illustrate the empirical relevance of our results with applications to S&P 500 return and US inflation data.

When you lose your key don’t worry. Sooner or later you are going to find it. But when you lose your mind then it is a whole different story:

3. “The fundamental properties of time varying AR models with non stochastic coefficients”:

The paper examines the problem of representing the dynamics of low order autoregressive (AR) models with time varying (TV) coefficients. The existing literature computes the forecasts of the series from a recursion relation. Instead, we provide the linearly independent solutions to TV-AR models. Our solution formulas enable us to derive the fundamental properties of these processes, and obtain explicit expressions for the optimal predictors. We illustrate our methodology and results with a few classic examples amenable to time varying treatment, e.g, periodic, cyclical, and AR models subject to multiple structural breaks.

Regarding the above paper, four! editors told us:

Despite the four rejections I am boastful about this part of my research! Yes boastful as a peacock who displays his opening feathers:

4. “Negative volatility spillovers in the unrestricted ECCC-GARCH model”:

(Econometric Theory, 2010;

edited by Peter Phillips)

This paper considers a formulation of the extended constant or time-varying conditional correlation GARCH model that allows for volatility feedback of either the positive or negative sign. In the previous literature, negative volatility spillovers were ruled out by the assumption that all the parameters of the model are nonnegative, which is a sufficient condition for ensuring the positive definiteness of the conditional covariance matrix. In order to allow for negative feedback, we show that the positive definiteness of the conditional covariance matrix can be guaranteed even if some of the parameters are negative. Thus, we extend the results of Nelson and Cao (1992) and Tsai and Chan (2008) to a multivariate setting. For the bivariate case of order one, we look into the consequences of adopting these less severe restrictions and find that the flexibility of the process is substantially increased. Our results are helpful for the model-builder, who can consider the unrestricted formulation as a tool for testing various economic theories.

In the area of Financial Development:

1. “The Finance-growth nexus and public-private ownership of banks: Evidence for Brazil since 1870″:

(accepted subject to minor revisions)

How does finance affect economic growth? And does ownership matter? This paper investigates whether and how deposits in public vis-a-vis private banks affect economic growth. It uses the power- ARCH framework with annual time series for Brazil from 1870 to 2003. There are three main …findings: (a) the effect of private banks on growth is mostly direct, (b) that of public banks is mostly indirect (through growth variance), and (c) the short-run effect of public and private banks is negative, while only for the latter does the positive long-run effect dominate.

2. ” Financial development, political instability, trade openness and growth in Brazil: Evidence from a new dataset, 1890‑2003″:

(Open Economies Review, 2023)

What is the relationship between financial development, political instability, trade openness and economic growth and how does it change over time? This paper examines these links using a new econometric approach and unique data set. In this paper, we apply the logistic smooth transition model (LST) to annual data for Brazil from 1890 to 2003. The main finding is that financial development has a time-varying effect on economic growth, which depends significantly on (jointly estimated) trade openness thresholds. In addition, political instability displays a negative effect on growth whereas trade openness a positive one. Finally, our estimates show that in 56% of the years in which financial development has a ‘below the mean’ effect, we f ind that trade openness experiences a substantial ‘above the mean’ change.

3. “From riches to rags, and back? Institutional change, financial development and economic growth in Argentina since 1890″:

(Journal of Development Studies, 2016)

Argentina is the only country in the world that in 1900 was “developed” and in 2000 was “developing”. Although economic historians have identified and explored various possible explanations (chiefly institutions, political instability, financial development, inflation, trade openness, and international financial integration), no study so far has attempted a comprehensive quantitative assessment of their relative importance. This paper tries to fill this gap using the power-ARCH framework and annual data since 1896 to study the effects of these factors in terms of both growth and growth volatility. The results highlight two main factors to understand the remarkable growth trajectory of Argentina over the very long-run, financial development and institutions (formal and informal political instability) and stress the importance of differences in their short vis-à-vis long-run behaviour.

Sahay et al. (2015) find that the gains for growth as well as for economic and financial stability from further financial development remain large for most emerging markets. But there are speed limits on financial deepening: when financial sectors deepen too fast, it often leads to crises and instability:

4. “Political instability, institutional change and economic growth in Brazil since 1870″:

(Journal of Institutional Economics, 2020)

Are institutions a deep cause of economic growth? This paper tries to answer this question in a novel manner by focusing on within-country variation, over long periods of time, using a new hand-collected data set on institutions and the power-ARCH econometric framework. Focusing on the case of Brazil since 1870, our results suggest (a) that both changes in formal political institutions and informal political instability affect economic growth negatively, (b) there are important differences in terms of their short- versus long-run behaviour, and (c) not all but just a few selected institutions affect economic growth in the long-run.

I presented this paper in a finance conference in Sydney, Australia, in December 2014. It was so hot and I was so thirsty!

5. “Two to tangle: financial development, political instability and growth in Argentina”:

(Journal of Banking and Finance, 2012)

This paper studies the impact of financial liberalization on economic growth. It contributes to this literature by using an innovative econometric methodology and a unique data set of historical series. It presents power ARCH estimates for Argentina for the period from 1896 to 2000. The main results show that the long-run effect of financial liberalization on economic growth is positive while the short-run effect is negative, albeit substantially smaller. Interestingly, we find that financial development affects growth only directly, that is, not through growth volatility.

In the area of

Applied Macroeconomics:

1. “Apocalypse now, apocalypse when? Economic growth and structural breaks in Argentina (1886-2003)”:

(Economics of Transition and Institutional Change, 2021)

Apocalypse Now, Apocalypse When?

The End of the World:

The Best Poems about Apocalypse

2. Robert Frost, ‘Fire and Ice’.

2. “Inflation convergence in the EMU and the link between inflation differentials and their uncertainty”:

(Journal of Empirical Finance, 2016)

3. “Modelling the link between US inflation and output : The importance if the uncertainty channel”:

Stratovirus: Uncertainty

(Scottish Journal of Political Economy, 2015)

This article employs an augmented version of the UECCC GARCH specification proposed in Conrad and Karanasos (2010) which allows for lagged in-mean effects, level effects as well as asymmetries in the conditional variances. In this unified framework, we examine the twelve potential intertemporal relationships among inflation, growth and their respective uncertainties using US data. We find that high inflation is detrimental to output growth both directly and indirectly via the nominal uncertainty. Output growth boosts inflation but mainly indirectly through a reduction in real uncertainty. Our findings highlight how macroeconomic performance affects nominal and real uncertainty in many ways and that the bidirectional relation between inflation and growth works to a large extent indirectly via the uncertainty channel.

After a lot of rather plain fights (and rejections!) with many referees (in  the last five years)  the above paper was accepted for publication:

4. “Conditional heteroscedasticity in macroeconomics data: UK inflation, output growth and their uncertainties”:

(in Handbook of Research Methods and Applications in Empirical Macroeconomics, Eds. Nigar Hashimzade and Michael Thornton, 2013)

The conditional heteroskedasticity models are widely used in the financial economics and less frequently so in other fields, including macroeconomics. However, certain applications lend themselves naturally to the investigation of possible links between macroeconomic variables and their volatilities, and here the conditional heteroskedasticity approach proved to be a powerful tool. The basics of the univariate models with conditional heteroskedasticity have been introduced in Chapter 2 in this volume. In this chapter, we extend this to a bivariate model and illustrate how this approach can be used to investigate the link between UK inflation, growth and their respective uncertainties, using a particular bivariate model with conditional heteroskedasticity. For recent surveys on multivariate GARCH specifications and their importance in various areas such as asset pricing, portfolio selection, and risk management see, for example, Bauwens et al. (2006) and Silvennoinen and Teräsvirta (2007).

To most people, uncertainty simply means not knowing: not knowing what next year’s vacation plans are, who their next client will be, or how long their savings will last.But to economists, uncertainty is paradoxically knowable. It can be measured, indexed, modeled, and correlated with other elements in the business cycle. One such element is economic growth—uncertainty is lower during booms and higher during busts: Kellogg School of Management at Northwestern University

5. “The link between macroeconomic performance and variability in the UK”:

(Economics Letters, 2010)

This paper examines the link between inflation, output growth and their respective variabilities. We employ a bivariate GARCH model, which incorporates mean and level effects, to investigate in a unified empirical framework all the possible interactions between the four variables. We show that not only does variability affect performance but the latter influences the former as well. Specifically, inflation has a positive impact on both variabilities.

In the area of Empirical Finance:

A. Research on Futures:

1. “Modelling time varying volatility spillovers and conditional correlations across commodity metal futures”:

(International Review of Financial Analysis, 2018)

This paper examines how the most prevalent stochastic properties of key metal futures returns have been affected by the recent financial crisis. Our results suggest that copper and gold futures returns exhibit time varying persistence in their corresponding volatilities during the crisis period. The estimation of a bivariate GARCH model further shows the existence of time varying shock and volatility spillovers between these returns during the different stages of such a crisis. Our results are broadly robust irrespective of whether mapped or unmapped data are employed.

2. “Modelling returns and volatilities during financial crises: a time varying coefficient approach”:

(Journal of Empirical Finance, 2014; edited by Richard Baillie)

We examine how the most prevalent stochastic properties of key financial time series have been affected during the recent financial crises. In particular we focus on changes associated with the remarkable economic events of the last two decades in the volatility dynamics, including the underlying volatility persistence and volatility spillover structure. Using daily data from several key stock market indices, the results of our bivariate GARCH models show the existence of time varying correlations as well as time varying shock and volatility spillovers between the returns of FTSE and DAX, and those of NIKKEI and Hang Seng, which became more prominent during the recent financial crisis. Our theoretical considerations on the time varying model which provides the platform upon which we integrate our multifaceted empirical approaches are also of independent interest. In particular, we provide the general solution for time varying asymmetric GARCH specifications, which is a long standing research topic. This enables us to characterize these models by deriving, first, their multistep ahead predictors, second, the first two time varying unconditional moments, and third, their covariance structure.

Without the help of four co-authors I wouldn’t manage to have this paper published:

B. Research on Volume and Volatility:

1. “Long-run dependencies in stock volatility and trading volume: evidence from an emerging market”:

(working paper)

This paper provides empirical evidence on the degree of long run dependence of volatility and trading volume in the Korean Stock Exchange using the semiparametric estimators of Robinson (1994, 1995a). The results of testing for long memory support the argument for long run dependence in both Garman-Klass volatility and trading volume (turnover). Total and domestic trading volume exhibit very similar long memory characteristics for all sample periods. The degree of long memory in foreign volume is significantly lower than that experienced in domestic volume. Interestingly, the results for trading volume are not influenced by structural breaks in the mean of the series. On the other hand, the long range dependence in volatility is quite sensitive to the different sample periods considered and comparable to foreign volume. Furthermore, the null hypothesis that volatility and volume share a common long memory parameter is only accepted for foreign volume and Garman-Klass volatility in all three subperiods. This result is consistent with a modified version of the mixture of distributions hypothesis in which volatility and volume have similar long memory characteristics as they are both ináuenced by an aggregate information arrival process displaying long range dependence. Finally, we find no evidence that foreign volume and volatility share a common long memory component.

Still struggling to publish this paper

2. “Investors’ trading behaviour and stock market volatility during crisis periods: a dual long-memory model for the Korean stock exchange”:

(International Journal of Finance and Economics, 2021)

This study examines the impact of investors’ buy and sell trades on Korean stock market volatility across two crisis events, the Asian crisis of 1997 and the 2008 global financial crash. We investigate the trading behaviour of domestic vs. foreign and institutional vs. individual investors. Our results suggest that the buy and sell trades have an asymmetric effect on volatility that depends on the type of investor trading and on the phase of the business cycle. Buy orders appear to be more informative than sell orders since they mostly lower volatility in the pre-crisis periods, while sell and post-crisis buy trades affect volatility positively regardless of who trades (institutional or individual investors) and on what information (member, non-member). Most importantly, decomposing total buy and sell trades into trader-type categories reveals that some institutional investors are more informed traders that stabilize the market compared to individuals that always increase volatility. Foreign investors reduce volatility with their purchases and total trading activity in the whole Asian crisis sample, but only in the pre-crisis period before the recent global financial turmoil.

3. “The Informative Role of Trading Volume in an Expanding Spot and Futures Market”:

(Journal of Financial Management, 2016)

This paper investigates the information content of trading volume and its relationship with range-based volatility in the Indian stock market for the period 1995-2007. We examine the dynamics of the two variables and their respective uncertainties using a bivariate dual long-memory model. We distinguish between volume traded before and after the introduction of futures and options trading. We find that in all three periods the impact of both the number of trades and the value of shares traded on volatility is negative. This result is consistent with the argument that the activity of informed traders is inversely related to volatility when the marketplace has increased liquidity, an increasing number of active investors and high consensus among investors when new information is released. We also find that (i) the introduction of futures trading leads to a decrease in spot volatility, (ii) volume decreases after the introduction of option contracts and, (iii) there are significant expiration day effects on both the value of shares traded and volatility series.

4. “Analyzing the link between stock volatility and volume by a Mackey-Glass GARCH-type model: the case of Korea”:

(QASS, 2012)

In this study we investigate the Korean stock volatility-volume relation for the period 1995-2005 and hence contribute to the study of emerging markets’ liberalization after the financial crisis in 1997. In particular, we examine whether the crisis affects the dynamic interaction between volume and volatility. The main contribution of this work is that taking the complex behavior of the Korean stock market into account we model the real financial data by a non-linear chaotic process disturbed by dynamic noise. Then we consider the augmentation of the Mackey-Glass GARCH-type model to allow for lagged values of market volume as predictors of future volatility. Moreover, in this research the total trading volume is separated into the domestic investors’ and the foreign investors’ volume. By doing this the information used by two different groups of traders can be separated. Finally, by conducting sub-sample analyses we show that there are structural shifts in causal relations. Specifically, before the financial crisis in 1997 there was no causal relation between domestic volume and stock volatility whereas during and after the crisis a positive relation began to exist. Additionally, the effect of either foreign or total volume on volatility was negative in the pre-crisis period but turned to positive during and after the crisis.

C. Research on Modelling of Stock Volatility:

1. “Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis”:

(International Review of Financial Analysis, 2016)

This paper applies the vector AR-DCC-FIAPARCH model to eight national stock market indices’ daily returns from 1988 to 2010, taking into account the structural breaks of each time series linked to the Asian and the recent Global financial crisis. We find significant cross effects, as well as long range volatility dependence, asymmetric volatility response to positive and negative shocks, and the power of returns that best fits the volatility pattern. One of the main findings of the model analysis is the higher dynamic correlations of the stock markets after a crisis event, which means increased contagion effects between the markets. The fact that during the crisis the conditional correlations remain on a high level indicates a continuous herding behaviour during these periods of increased market volatility. Finally, during the recent Global financial crisis the correlations remain on a much higher level than during the Asian financial crisis.

2.  “Multivariate Fractionally integrated APARCH modelling of stock market volatility: a multi country study”:

(Journal of Empirical Finance, 2011)

Tse (1998) proposes a model which combines the fractionally integrated GARCH formulation of Baillie, Bollerslev and Mikkelsen (1996) with the asymmetric power ARCH specification of Ding, Granger and Engle (1993). This paper analyzes the applicability of a multivariate constant conditional correlation version of the model to national stock market returns for eight countries. We find this multivariate specification to be generally applicable once power, leverage and long-memory effects are taken into consideration. In addition, we find that both the optimal fractional differencing parameter and power transformation are remarkably similar across countries. Out-of-sample evidence for the superior forecasting ability of the multivariate FIAPARCH framework is provided in terms of forecast error statistics and tests for equal forecast accuracy of the various models.