Juan Arismendi Zambrano, D.Phil.
ABOUT ME
I am an inquisitive and diligent person, with a special talent for solving complex and hard-work quantitative and programming problems, that recognize the importance of team-work for success. An individual that is engaged with diversity, multiculturalism, and freedom. Ethical, resilient and proficient.
I am Lecturer (Assistant Professor) of Banking and Finance of the UCD School of Business (Smurfit, Quinn, International and Executive), and Visiting Scholar at the Kellogg School of Management, Northwestern University. In the past, I've been Lecturer (Assistant Professor) in the Department of Economics, Finance and Accounting of the Maynooth University, National University of Ireland (NUI). I was Head of the Department of Economics, Finance and Accounting of the Business School in the University of Monterrey (UDEM), Senior Visiting Professor of the Department of Finance, at the Business School of the Technological of Monterrey (ITESM) Campus Leon, Assistant Professor of Quantitative Finance and Economics at the Department of Economics of the Federal University of Bahia (UFBA), Brazil. I also hold a Visiting Research Fellow position from the ICMA Centre, University of Reading.
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In the past, I worked in the financial industry. I was Quantitative Strategist at BancTrust & Co. where I developed High Beta Fixed-Income trading strategies for the research team (Miami, US - Caracas, Venezuela).
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I was Vice-president and partner of Clever Financial an investment advisors and wealth management company based in Caracas, Venezuela (2003 - 2008). My principal duty was to develop investment portfolios that range from low risk Agencies - Treasuries to Emerging Markets Bonds and High Yield Bonds. Also I was CEO of Financial Intelligence Systems, a company that works with Clever Financial in development of automata for trading the FX, Stocks, Bonds Futures and Options market.
EDUCATION
PROFESSIONAL CERTIFICATIONS
2009 - 2013
ICMA Centre - Henley Business School - University of Reading
DPhil. in Quantitative Finance
Professional Risk Manager Association - PRMIA
Professional Risk Manager
2008 - 2009
7city Learning Centre, London - United Kingdom
Certificate in Quantitative Finance
Global Association of Risk Professionals - GARP
Financial Risk Manager
2003 - 2006
The Institute of Superior Studies in Administration (AACSB, AMBA, EQUIS)
Msc. in Finance
2002 - 2005
Simon Bolivar University
PhD in Mathematics (First 2 years)
Msc. in Computer Science - Artificial Intelligence
Bsc. Computer Engineering
Financial Industry Regulatory Authority - FINRA
Investment Advisor Law Examination Series 65 - US
Chartered Investments and Securities Institute - United Kingdom
Certificate in Investment Management - Level 3 Certificate
Unit 05 - Investment Management
Unit 06 - Principles of Financial Regulation
INDUSTRY EXPERIENCE
2016 - Present
WikiStrat
Senior Analyst
2012
BancTrust & Co.
Quantitative Strategist
2003 - 2009
Clever Financial
VP of Investment
2002 - 2009
Financial Intelligence Systems
CEO - Founder
2015 - 2017
Bitcoin Mining Solindus
CEO - Founder
HOBBIES: LATIN AMERICA (and Spain, Portugal, Italy and the Mediterranean influence) - A MUSICAL JOURNEY
One of my hobbies is to explore music genres around the world. But between all the countries that I lived and visited in the past, Latin America is like a Universe in musical terms. Our roots lie between the origins of music and culture from the Iberian peninsula, plus the Mediterranean region, mixed with our ancestral heritage from pre-Colonisation period (Incas, Olmecs, Mayan, Tolecs, Aztecs).
HOBBIES: The magic of AI in LATIN AMERICA Music
The magic of AI as it transforms our world, creating connections and enhancing creativity like never before. Just as iconic Latin American singers like Luis Miguel and Christian Castro enchant us with their melodies, AI brings a harmonious blend of innovation and artistry. Together, they inspire us to explore new horizons in music and technology. Embrace the magic and let it elevate your experience!
MY INTELLECTUAL JOURNEY: Uncertainty, Risk, and Asset
Pricing Theory
Over the past decade, I built a portfolio of research spanning theoretical finance and practical policy implications. My journey began with fundamental advances in higher-order statistical moments and simulation methods, laying a mathematical foundation for later applications in asset pricing and risk management.
By the mid-2010s, I was tackling applied problems in commodity volatility, option pricing, and trading strategies, demonstrating how nuanced modeling (e.g. seasonal effects or improved Monte Carlo methods) can reduce pricing errors and uncover market inefficiencies.
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Moving into the late 2010s, my work increasingly focused on systemic risk and tail dependencies, identifying how extreme events
propagate in banking networks and how misestimating dependence can drastically understate risks.
In the 2020s, my research has bridged into macroeconomic and policyrelevant domains – from developing an “entropic” measure that signaled financial stress before the 2008 crisis , to using machine learning for high-frequency market predictions, and even measuring how the Fed Chair’s communication tone can moderate monetary policy uncertainty .
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​Multivariate Truncated Moments (2009–2013)
The journey began in the late 2000s with a fundamental question about uncertainty in data. During my doctoral research, I started with the challenge of calculating moments in truncated distributions – situations where extreme outcomes are censored or bounded. I developed new mathematical tools to compute “higher-order tail moments” for truncated multivariate distributions .
In my 2013 paper, I derived recursive formulas for arbitrary-order moments under various distributions (normal, Student’s t, lognormal, and even finite mixture models) . This work not only generalized classic results (Tallis 1961, 1963) but also introduced novel measures of shape: for example, I proposed tensor-based definitions of multivariate skewness and kurtosis that capture more information about distributional shape than previous definitions . These early contributions, though highly technical, were motivated by practical needs – from robust statistics to survival analysis – where understanding the tails of a distribution is crucial . Reflecting on this period, I realised it taught me how much insight lies in the “hidden corners” of a distribution.
By 2013, I laid a mathematical foundation for quantifying extreme risk, an obsession with tails that would weave through much of my later work: “We derive formulae for the higher order tail moments of the lower truncated multivariate standard normal (MVSN), Student’s t, lognormal and a finite-mixture of multivariate normal distributions... Potential applications include robust statistics, reliability theory, survival analysis and extreme value theory.”
Multi-Asset Option Approximation (2010–2016)
Building on the truncated-moments framework, I turned to a classic problem in derivatives pricing: How can one efficiently price an option that depends on multiple underlying assets? Traditionally, multi-asset (or “basket”) options had resisted closed-form solutions, often requiring slow Monte Carlo simulations. Around 2010, I saw an opportunity to apply the new moment techniques to this problem.
Collaborating with a Marcel (supervisor), we developed a model-free analytical approximation for multi-asset option prices. The approach has been used before for single-asset options: now, using a Multivariate Generalized Edgeworth Expansion (MGEE) to represent the unknown risk-neutral distribution in terms of its moments, we extended previous single-asset expressions to the multi-asset case. Essentially, creating an infinite series expansion that “isolates the effects of multivariate moments over the option prices” .
By incorporating known moments into a Monte Carlo scheme, we could greatly enhance computational efficiency without sacrificing accuracy. In the paper, we demonstrated this method on both jump-diffusion and fat-tailed processes, showing that a properly calibrated MGEE provides an excellent fit to true option values . Notably, the method generalizes classical Gram–Charlier expansions by allowing flexibility in the choice of an auxiliary distribution, thus improving stability .
This project taught me the power of marrying analytical insight with simulation: by blending moments and Monte Carlo, one could achieve speed and precision. It was a theme that would recur – the trade-off between efficiency and accuracy in modeling – but years later I would examine that trade-off more formally. At the time, however, the results of the multi-asset approximation showed significant advances: it illustrated how understanding higher moments and tail behavior can solve practical problems in asset pricing: “We derived a model-free analytical approximation of the price of a multi-asset option… applying a semiparametric expansion of the unknown risk-neutral density with the moments. The analytical expansion termed the Multivariate Generalised Edgeworth Expansion (MGEE) is an infinite series… incorporating information about moments of the risk-neutral distribution.”
American Options and Implied Density (2011–2016)
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In parallel with the multi-asset project, I embarked on another derivatives challenge – this time involving American options. American-style options (which can be exercised early) pose a notorious difficulty: unlike European options, one cannot directly compute their price as the simple expected payoff under a single static density. This also complicates extracting the implied risk-neutral density (RND) from market prices, a key tool for understanding market expectations. Around 2011, motivated by this gap, I teamed up with Marcel to develop a moment-based approximation for the implied RND of American options. Our strategy was to "calculate" the "equivalent in value" European contract RND ” that a American option will match. By leveraging the moments of that implicit contract’s payoff, they derived a model-free formula for the RND consistent with the American option’s price.
The solution again utilized the multivariate Edgeworth expansion, combined with a clever “reverse engineering” via least-squares to obtain the American option’s moments . Crucially, the theory of multivariate truncated moments was employed to approximate the early-exercise premium – a direct carryover from his 2009–2013 work. Published in 2016, this research provided an analytical way to extract the market’s implied probability distribution even when options have complex features. It had “important consequences for the hedging of variance, skewness and kurtosis swaps” – in other words, it helped traders hedge higher-order risks by understanding how American option prices embed information about the third and fourth moments. At a deeper level, this project expanded our appreciation of how market information can be hidden in plain sight. Even if American options don’t explicitly reveal a probability density (the way European options do via Breeden-Litzenberger), with the right mathematical key – in this case, moment expansions and truncated moment theory – one can unlock that information.
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“For American options… we derive a model‑free analytical formula for the implied risk-neutral density based on the implied moments of the implicit European contract under which the expected value will be the price of the equivalent payoff with the American exercise condition. The theory of multivariate truncated moments is employed for approximating the option price, with important consequences for the hedging of variance, skewness and kurtosis swaps.”
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Commodity Option Pricing with Seasonal Volatility (2015–2016)
By the mid-2010s, my interests began to broaden beyond equity and financial options to the world of commodity markets. Commodities like natural gas and corn exhibit pronounced seasonal patterns – not only in prices but also in volatility. I became intrigued by how these seasonal swings in uncertainty impact option values. In 2015, working with Marcel and team of German researchers, I developed a research on commodity option pricing under seasonal stochastic volatility. We proposed a model in which the volatility’s long-run mean itself fluctuates with seasons. By embedding a seasonal cycle into a standard stochastic volatility framework, we derived semi-closed-form formulas for option prices on commodity futures .
When we calibrated the model to market data (NYMEX natural gas options and CBOT corn options), the impact was striking. The seasonal volatility factor materially improved pricing accuracy: allowing volatility to vary seasonally “significantly reduces pricing errors” for these contracts . In other words, the model captured reality far better than one assuming constant long-term volatility. Through this project, I gained a deep appreciation for domain-specific nuances of risk. It was a lesson that uncertainty in financial markets can arise not only from abstract statistical distributions but also from concrete, physical cycles – harvest seasons, weather patterns, storage cycles – that manifest as risk.
This research also reinforced the importance of collaboration: by teaming up with commodity experts, I translated a practical market observation (seasonality) into a rigorous quantitative model. The work, published in 2016, bridged my expertise in stochastic processes and moments with the gritty realities of commodity trading.
“Many commodity markets contain a strong seasonal component not only at the price level, but also in volatility… We propose a seasonally varying long-run mean variance process… Semi-closed form option valuation formulas are derived. We then empirically study the impact… Our results demonstrate that allowing stochastic volatility to fluctuate seasonally significantly reduces pricing errors for these contracts.”
Trading Rule Profitability in Emerging Markets (2016)
The year 2016 marked a pivot where my curiosity led me to examine financial market efficiency from a different angle. Amid my theoretical work on options and risk measures, I joined a project investigating the age-old question: Can simple trading rules consistently beat the market? Focusing on emerging equity markets – including the BRICS countries – we analyzed the profitability of moving-average technical trading strategies over 2000–2015 . This venture, somewhat outside my usual domain of asset pricing theory/academic domain -- yet in my actual "industry" field of expertise, was spurred by discussions about whether emerging markets had become more efficient (thus rendering technical strategies futile) or whether pockets of predictability remained.
Our findings were intriguing. Indeed, in several countries we found statistically significant “alpha” from certain moving-average timing strategies, even after accounting for transaction costs. The results indicated that “for some countries, there is a combination of periods for moving averages producing better outcomes” than a simple buy-and-hold strategy . In plain terms, not all emerging markets in that era were fully efficient – savvy traders could still earn excess returns by following the right technical signals.
This study, published in 2016, gave me a broader perspective on market uncertainty versus predictability. It underscored how institutional frictions, investor behavior, or slower information diffusion in emerging markets leave traces of exploitable patterns. For me, an intellectual used to thinking about uncertainty in terms of probability distributions and tail risks, this was a reminder that
uncertainty also stems from human and institutional factors. The project added a practical, empirical dimension to my portfolio of research, and it seeded ideas about the limits of arbitrage and how risk premia might be captured, themes that would resurface in my later work on equity premiums.
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“Technical analysis and trading systems have been widely used by practitioners… We considered the period from 2000 to 2015… The main results indicate that, for some countries, there is a combination of periods for moving averages producing better outcomes.”
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Fed Communication & Monetary Uncertainty (2014–2024)
By 2014, part of my journey turned to the exploration of uncertainty with a macro and policy-oriented focus. Starting around 2014, amid debates about transparency at central banks, I became fascinated by how central bank communications influence market uncertainty. Together with multi-disciplinar co-authors, I focused on the U.S. Federal Reserve Chair’s public statements – arguably the most influential words in global finance – and asked: Can one measure the “personal communication risk profile” of Fed Chairs, and does it affect monetary policy uncertainty? This was an ambitious leap into textual analysis and high-frequency market response.
Over several years, I built a new dataset quantifying the sentiment (tone) of each Fed Chair’s speeches and testimonies . Then, we applied machine learning and linguistic algorithms to score the communications as positive, negative, or uncertain in tone. Then, using changes in Fed Funds futures implied probabilities, we measured how surprised markets were by Fed actions, and how this related to the Chair’s prior communications. Our research, was finally published in 2024, and yielded nuanced insights. We found that each Fed Chair (Bernanke, Yellen, Powell, etc.) indeed has a distinct communication fingerprint, and these differences significantly affect the market’s reaction to policy announcements . In particular, the sentiment in the Chair’s communications plays an important role in moderating policy surprises.
For example, a consistently dovish-toned Chair might lead markets to be less shocked by a rate cut, because the “softening up” through language had prepared them. We introduced the notion of using sentiment as a tool: effectively, communication can be a policy instrument to manage uncertainty, alongside interest rates themselves. This finding has deep implications: it suggests that clarity and tone from the Fed Chair reduce volatility around FOMC meetings and can anchor expectations more smoothly. For me, this project was a culmination of my broadening horizons – from granular math to the intangibles of human communication. It demonstrated how uncertainty straddles both the quantitative and qualitative realms. A decade prior, I was parsing probability distributions for clues; now I was parsing words. Yet, the unifying theme remained: understanding and mitigating uncertainty.
This work also had a personal resonance, as it bridged academia and policy application. While presenting these findings to central bank audiences, I found that quantifying communication gave policymakers a new lever to consider in their pursuit of stability. And for me, it was a gratifying journey into the psyche of markets: after all, behind every tail risk and every model error, there are people – investors, bankers, policymakers – whose decisions and emotions drive outcomes. By analyzing the sentiment of the most influential financial figureheads, I had come to appreciate the profoundly human element of uncertainty in markets.
“After controlling for the state of the economy, we find that, based on heterogeneity across Fed Chairs and their personal traits, there is a significant difference in the communications’ sentiment, which is likely to affect the market’s reaction to policy announcements. Specifically, the sentiment in the Chairs’ communications plays an important role in moderating the potential surprises in the Fed announcements, and it can be effectively used as a tool for controlling and measuring monetary policy shocks.”
