My recent and current research agenda focuses on the modeling and prediction of financial asset returns. In particular, my team and I develop and study multivariate stochastic processes suitable for complex data arising from a variety of financial instruments. By calibrating these constructs to real data with sophisticated statistical methodologies, we can make accurate predictions of risk and return.

This, in turn, leads to algorithms for improved asset allocation. We derive optimal investment strategies and portfolio weights, through time, for a given universe of risky assets, such as stocks, bonds, currencies, options, crypto-currencies, and commodities, accounting also for transaction costs and illiquidity issues.

To get an idea of the improvements we obtain, Graphic 1 shows the performance of some of our models when applied to the stocks of the DJIA (top lines; total return over time) versus existing investment strategies (bottom lines) such as equally weighted (1/N), Markowitz, and traditional GARCH.

The various methods I work on can be divided into five categories (see Graphic 2). The top four are based on multivariate constructs, and all support large-dimensional asset universes, despite the proliferation of parameters and the "curse of dimensionality". The fifth method is for multivariate portfolio allocation, but makes use of only simple, univariate models that have been augmented with extremely fast statistical and numerical approximation procedures to allow it to work.


Graphic 1: Performance of competing methods

Graphic 2: Five different categories of methods

The academic papers associated with these five methods are listed below in their respective categories.

The "COMFORT" methodology (acronym for COmmon Market Factor nOn-Gaussian ReTurn Model) is arguably the most interesting one from a purely theoretical / academic viewpoint, and is among the best performers. The third graphic shows a diagram indicating the core COMFORT model and the numerous extensions we are working on.

A famous quote (often attributed to Kong Tzu, better known as Confucius) is "A scholar who cherishes the love
of comfort is not fit to be deemed a scholar."
There is much truth to this, as we need to relentlessly study and learn new things,
both for the sake of learning, and in order to generate worthwhile new research --- which is often very challenging and time-consuming. However, in our case, "COMFORT" is a core part of our academic efforts! We knew this is a promising and fortuitous approach---the fortune cookie we received, pictured on the right ("You will always be surrounded by comfort") is real.

A complete list of my published research work, along with citation information, can be found on my Google Scholar page.

Graphic 3: The COMFORT model family

Graphic 4: COMFORT fortune cookie



Robust Normal Mixtures for Financial Portfolio Allocation
M. Gambacciani and M. Paolella
Econometrics and Statistics, 3, pp. 91-111


Multivariate Asset Return Prediction with Mixture Models
M. Paolella
European Journal of Finance, 21(13-14), pp. 1214-1252


Stable Mixture GARCH Models
S. Broda, M. Haas, J. Krause, M. Paolella, and S.-C. Steude
The Journal of Econometrics, 172(2), pp. 292-306


Mixture and Regime-Switching GARCH Models
M. Haas and M. Paolella
In Bauwens, L., Hafner, C., and Laurent, S., (eds.)
Handbook of Volatility Models, John Wiley & Sons


Asymmetric Multivariate Normal Mixture GARCH
M. Haas, S. Mittnik, and M. Paolella
Computational Statistics and Data Analysis, 53(6), pp. 2129-2154


Risk Prediction: A DWARF-like Approach
M. Paolella and S-C. Steude
The Journal of Risk Model Validation, 2(1), pp. 25-43



Risk Parity Allocation with Expected Shortfall under Non-Gaussian Returns
M. Paolella, P. Polak, A. Polino, and P. Walker


A Non-Elliptical Orthogonal GARCH Model for High-Dimensional Portfolio Selection under Transaction Costs
M. Paolella, P. Polak, and P. Walker


Regime Switching Dynamic Correlations for Asymmetric and Fat-Tailed Conditional Returns
M. Paolella, P. Polak, and P. Walker

Forthcoming: Journal of Econometrics


Heterogenous Tail Generalized COMFORT Modeling via Cholesky Decomposition
J. Naef, M. Paolella, and P. Polak

Journal of Multivariate Analysis, 172, pp. 84-106


COMFORT: A Common Market Factor Non-Gaussian Returns Model
M. Paolella and P. Polak
Journal of Econometrics, 187(2), pp. 593–605


A New Approach to Markov-Switching GARCH Models
M. Haas, S. Mittnik, and M. Paolella
Journal of Financial Econometrics, 2(4), pp. 493-530


Mixed Normal Conditional Heteroskedasticity
M. Haas, S. Mittnik, and M. Paolella
Journal of Financial Econometrics 2(2), pp. 211-250



COBra: Copula-Based Portfolio Optimization
M. Paolella and P. Polak
In Kreinovich, V., Sriboonchita, S., and Chakpitak, N. (eds.)
Predictive Econometrics and Big Data, Springer-Verlag, pp. 36-77



CHICAGO: A Fast and Accurate Method for Portfolio Risk Calculation
S. Broda and M. Paolella
Journal of Financial Econometrics, 7(4), pp. 412-436


ALRIGHT: Asymmetric LaRge-Scale (I)GARCH with Hetero-Tails
M. Paolella and P. Polak
International Review of Economics and Finance, 40, pp. 282-297

Univariate Collapsing


The Univariate Collapsing Method for Portfolio Optimization
M. Paolella
Econometrics, 5(2), article 18, pp 1-33


Fast Methods for Large-Scale Non-Elliptical Portfolio Optimization

M. Paolella
Annals of Financial Economics, 9(2)


A Fast, Accurate Method for Value-at-Risk and Expected Shortfall
J. Krause and M. Paolella
Econometrics, 2, pp. 98-122

Risk Calculation


Approximating Expected Shortfall for Heavy-Tailed Distributions
S. Broda, J. Krause, and M. Paolella
Econometrics and Statistics, 8, pp. 184-203


Expected Shortfall for Distributions in Finance
S. Broda and M. Paolella

In Cizek, P., Haerdle, W., and Weron, R., (eds.)

Statistical Tools for Finance and Insurance, 2nd ed., Springer


Value-at-Risk Prediction: A Comparison of Alternative StrategiesK. Kuester, S. Mittnik, and M. Paolella

Journal of Financial Econometrics 4(1), pp. 53-89

Distributional Testing


Asymmetric Stable Paretian Distribution Testing
M. Paolella
Econometrics and Statistics, 1, pp. 19-39


New Graphical Methods and Test Statistics for Testing Composite Normality
M. Paolella
Econometrics, 3(3), pp. 532-560

Marc S. Paolella


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