“Women should stand up for themselves and persevere”
Dr Laura Ballotta writes for #QuantWomen on International Women’s Day
Dr Laura Ballotta, Reader in Financial Mathematics at Cass Business School, has taken part in a Q&A for the #QuantWomen series, answering questions about her career path, giving advice for women starting out in the industry and considering the future of quant finance.
Laura said there were many complex reasons why women were reluctant to consider a career in quant finance but that women should not be afraid of pursuing what interests them the most.
“As an academic and an educator, this issue always rings a bell. Young girls should not be afraid of pursuing what interests them the most, even if this is mathematics and perhaps does not fit the stereotype. As many women have realized, you can be anyone you want: you do not have to choose between say being popular with your friends and being a Quant. You can be both. For example, I have always been an athlete: a discus thrower first, a pole-vaulter after, and I even represented my country in both disciplines at few international meets.
“Starting from school, we should always promote a culture based on ‘Question Everything’ (stereotype included) and persevering when a hard problem comes along which needs a long time to solve. Society and life are not so simple; and women should stand up for themselves and persevere,” she said.
You can read the Q&A with Laura here.
Laura also wrote a short blog piece discussing her forthcoming presentation at QuantMinds International in May where she will explore calibration performance, hedging errors and forward implied volatilities in different markets.
Read the blog piece Volatility by Jumps here.
About Dr Laura Ballotta
Laura's research interests are in the areas of Mathematical Finance, Risk Management, and Financial Engineering, with particular focus on problems of practical relevance in current financial markets conditions, such as Counterparty Credit Risk (CCR) valuation and collateral design, and development of realistic models for the dynamics of the relevant risk drivers which also recognize the interdependence in place between them.