Each year, participants of EADA’s International Master in Finance attended electives in the strategic specialisations in the third trimester. The specialisations include 12 electives divided into three areas focus aras: corporate finance, investment banking and FinTech. Here we talked to EADA professor Lidija Lovreta, who led the course Volatility Forecasting.
Dr Lovreta holds a Ph.D in Management Sciences with European Doctorate Mention from ESADE and her research focuses on credit risk (modelling, calibration, applications). The work of Dr Lovreta has been published in the Journal of Banking and Finance, the European Financial Management Journal, the Finance Research Letters, Physica A: Statistical Mechanics and its Applications and the European Journal of Finance, among others. She has participated in a number of national and international conferences.
Dr Lovreta has also worked as a professor at CUNEF and the Universidad Autónoma de Barcelona (UAB). She has professional experience as a consultant, mainly in the valuation of companies.
In this interview, Dr Lovreta shares her expertise on volatility and its role in the financial sector.
What role does volatility play in finance?
Volatility is one of the most important risk measures and an essential element in almost all areas of finance. It measures the degree of movements in financial markets and is a key variable in risk management, portfolio management, asset pricing and hedging. For example, we need volatility to determine the price of options and structured products, to optimise the risk-return trade-off, to calculate risk-adjusted returns, to design a hedging strategy, or to estimate potential losses. Volatility also became an asset class on its own. It is possible to trade volatility, for example, through VIX options and futures, or through VIX related exchange-traded products.
Many investors are focusing now on volatility-based strategies or are using volatility in a multi-asset portfolio as a diversification tool. As a result, we need a measure of volatility, but also a measure of the volatility-of-volatility. It is important to note that volatility is changing over time and there is a need to constantly update our estimates and forecasts of volatility due to the constantly changing environment.
Is it realistically possible to forecast volatility?
In finance and in particular in the process of investment, we are interested in future outcomes. Therefore, one way or another the process of investment is inherently linked to giving either subjective or objective opinions about future risk and returns. We cannot ex-ante calculate these values in the way we can, for example, determine the exact future position of the major planets in the sky. We can just try to forecast these values, with more or less success. To put it simply, we can just try to point out the right direction.
The empirical fact is that volatility is more predictable compared to other variables with which we commonly work in finance, like returns. That is, the accuracy of forecasted values is much higher for volatility in comparison to financial returns. This is because volatility possess some features that make it inherently more predictable than other variables in financial markets, like for example clustering, mean-reversion, asymmetry, or long-memory. The forecastability of volatility also depends on the time horizon, being higher at shorter horizons and lower at longer horizons. For example, short horizons would be more relevant for trading-desk risk management, while longer horizons are typically more relevant in portfolio management.
How important is an understanding of statistics and econometrics for finance professionals today?
Statistics and econometrics play and essential role in finance. Almost any statistical technique has its direct practical application in finance. It allows finance professionals to analyse and interpret data. This knowledge is essential for making informed decisions.
Basically, statistics allows us to convert data into information that is relevant for making business decisions. The practical application of important models in finance, like CAPM and the beta of a stock, portfolio construction, portfolio analysis and performance evaluation, or Black-Scholes-Merton option pricing, relies on econometrics and statistical inference.
Nowadays, there are many statistical applications that generate ready-made outputs. However, if these are used without knowledge about the underlying model, its assumptions, and above all its disadvantages, then users are not able to properly interpret the results obtained or to adapt to a constantly changing situation. This ultimately leads to making wrong decisions. Without understanding statistics and financial econometrics, we cannot understand financial markets.
What attracted you to this field?
Actually, it took me some years before I discovered what I truly enjoy doing. After completing my undergraduate studies in economics, I first started to work in a corporate finance consulting firm. I am very risk averse and prefer data driven decision making, so I was spending my free time on coding and learning new material from finance books and articles with the intention of constantly enhancing my work. Then is when I realised that I mostly enjoy data analysis, programming and exploring what data has to tell us. This led me to change my career and pursue a PhD. Now, I feel privileged to have chosen a profession that allows me to search for the answers to questions that I am interested in and to create knew knowledge.
What skills and/or qualities is it important for finance professionals working in volatile markets to have?
Finance professionals nowadays need a number of skills, both quantitative and qualitative. From the quantitative side, they need strong analytical abilities and critical thinking skills. The world is becoming more and more complex and uncertain. Consequently, it is becoming increasingly more difficult to extract important and useful information from the large volume of data. Finance professionals need analytical skills to be able to extract important information from the available mass data.
Who do you admire most professionally? Why?
There are a lot of people that I admire and that have marked my professional life. Starting from my grandparents, who conveyed to me the philosophy of the constant need for learning. This is what determines long-term success, otherwise you quickly become obsolete. At the start of my career, I was fortunate to have been surrounded by inspiring people that always emphasised the importance of perseverance and self-learning.
How has your professional experience informed what you teach at EADA?
I started my professional career in consulting where I spent several years, mainly on the valuation of companies. After that initial experience, I did a PhD in Management Sciences with a research focus on credit risk modelling. I have been involved in teaching and research on a full-time basis for more than a decade. I feel that this dual industry-academic experience helps me keep up to date on the latest advances in the field that are of practical importance and to translate this to students. In shaping the teaching material, I always try to think what students will need tomorrow.
What is most rewarding part of teaching young professionals about to launch their careers in finance?
I think that teaching is purely vocational, and you really have to be passionate about it. The most rewarding part is the feeling of actively participating in the process of developing students’ abilities and skills that enable them to get the job they want and build a successful career. That is when your efforts really pay off.
What do you know now that you wish somebody had told you when you started your career in finance?
I think that it is important to understand your own strengths and interests to be able to find the particular field in which you will feel comfortable. You have to be passionate about what you do, but also to feel comfortable. Explore and learn, invest in your self-development. The more you know, the more opportunities you will have.