In this video blog, Numerix SVP of Financial Engineering Dr. Dan Li and Jim Jockle, CMO of Numerix discuss key highlights from Risk’s 13th Annual Quant Congress USA. The discussion addresses the debate surrounding the evolving role of quantitative finance in today’s financial institutions, and its shifting focus to address risk management and model validation needs in response to ever-increasing regulations.
The analysis also explores additional trends on the insurance side of the business—such as actuaries embracing capital markets modeling and bridging real-world and risk-neutral frameworks. Lastly, Dr. Li addresses the impact of the negative interest rate, low-yield environment in terms of quantitative finance innovations and the search for alpha. The discussion concludes with an exploration of modeling innovations, including the Free Boundary SABR and big data challenges.
Jim Jockle (Host): Welcome to Numerix Video Blog, your expert source for derivative trends and challenges. I am your host Jim Jockle. Quant Congress in its 13th year was just held recently this week and joining me today is SVP of financial engineering of Numerix, Dan Li, to discuss some of his thoughts and insights from the conference. Welcome Dan.
Dan Li (Guest): Hi. Thank you.
Jockle: So Dan, to Quant or not to Quant seems to be kind of the theme as we’re looking ahead to risk management and there seems to be a little bit of a controversy as it related to what is the future role of quantitative finance in some institutions. So, maybe we just kind of jump right in there and give us a little bit of the issues of the debate that happened at the event.
Li: Yeah. Yeah, actually I attended a panel discussion actually specifically about this market trend and it was very interesting and we can also clearly see that after Lehman collapsed and after the financial crisis, we can see the market trends shift really for the Quant roles really from the front-office role. Really focus more on the mid-office role and the risk management, model validation and focus on the regulations. There are a lot of regulations really taking on that risk management reality, from liquidity to counterparty risk, to market risk, so it covers everything including the capital. So those have really been a trend we have seen in the market.
Jockle: So how is that changing the profile of the Quant, or the day to day? You know, you think of back into the days of the boom, or innovations of products coming to market. There are still so many quantitative challenges in meeting all of these new regulatory requirements or changes in valuation. How is that changing the skill set required?
Li: Yeah, that’s a very good question. And actually, there are also some debates related to these topics. And we can see the trend, I mean in terms of the skill sets, that for front office quants that used to be very mathematical oriented, meaning, they typically would be PhD’s in physics, or PhD’s in mathematics or statistics, they were very attractive back to 1990’s and 80s. And now we can see that those type of skill sets are very useful, and we still need all those types of skill sets. In terms of risk management, and in terms of underpriced solutions, and usually we’ve see more trends in terms of the skill sets, we may also need to see good probabilities and knowledge in the probabilities and statistics and also some of the econometrics, portfolio optimization type of the skill set. So those we can see the trend on that area. Also, in terms of market risk, counterparty risk, that actually needs even higher skill sets in terms of the model understanding because you really need to understand more than like one asset class. It’s like that really pushes, in terms of the skill set, it’s not just a simple shift, and people usually assume you have a PhD background, but ideally also have a mathematical finance background as well. So, that is sometimes where we see the mix. Some of the banks, actually they have a slightly decreasing trend in terms of hiring the fresh PhD’s, but they prefer some of the mathematical, finance degree, either fresh from school or maybe with a few years of experience. That is what we can see.
Jockle: So, looking ahead, I think one of the things when you think about CCAR or some of the other regulations that are coming down, it’s also the evolution of projections into better understanding the future. So, no longer just Monte Carlo and backwards looking into historical data, but where are you seeing the role of, you know of simulation technology and being played going forward.
Li: Yeah, that’s actually really true, especially, the whole world, not only finance, actually moved to a big data world, so from that reason especially in the risk management world or model validation world, that we really see the trend and the demand on the scalable, like underpriced solution. So, for that reason, in terms of the skill sets they really actually shift; we need a good programmer, we need really an architect, on all those scalable solutions side and in terms of Monte Carlo, also we need there’s market needs that really demand on the high computing, really efficient computing, in terms of the convergence, in terms of to really handle the large dimension of the whole portfolio. It’s really, we move to the very complex world, even more exotics than before, even most of the portfolios tends to be more vanilla, compared to several years ago before the crisis.
Jockle: Other trends I’d love to get your perspective on is in the insurance side of the business we’ve seen actuaries embracing more typical capital markets modeling and get a wider understanding of different risk factors as it relates to securities performance. But in the typical quant world we’re starting in, and you even mentioned it a little bit before, more of the introduction of econometrics into the thought process. You know, how do you see that evolving in realms of capital markets and banking and finance?
Li: Yeah, that’s actually back to the old 80’s. Those type of the trends it’s like a risk neutral role with all the modeling vs econometrics or statistics or like a historical estimate and do the forecasting. So those are like two types of world, running simultaneously: one is really focused on the portfolio, focused on portfolio management or focused on like hedge fund hunting whatever the profit that they can get from the market and from the market maker starts dealer specs they typically really within the same consistent risk neutral framework not for all asset class and using that for the risk management or all the risk measures. And now we can see actually the two worlds, seems like, not merged, but definitely we can see the converging to the degree, meaning people need to think especially like the insurance industry that you mentioned, insurance used to use like the historical estimate, everything for the future simulations could be under the real-world measure. And, but in the capital market, actually, people when they try to do the hatch, try to do pricing, and get the sensitivities of the value at risk or maybe counterparty risk on top of those portfolios those are all in the risk neutral. So in other worlds it’s really the real-world versus risk neutral but we can see actually the trend bridge them together, that’s also ideally, actually it would also need to be within the same framework but maybe with resampling techniques or Monte Carlo techniques that really can bridge those two together; and from the real-world, the modeling part that could involve like statistical, econometrics, and other advanced metrics. And for the risk neutral that would still be the market standard models and calibrate to the market and then bridge them together. So that’s what we can see also the trend.
Jockle: And also, I’m sure being benefactors of better data, more robust data, and longer data sets, is only going to help advance that. So, I want to turn to the other side of the debate. So I think it’s like 12 or 13 currencies we have negative rates, low-yield environment, the US is poised to raise rates which puts everybody in some sort of concern, even though its priced into the market. So, as investors, we need to make our return. So, finding and seeking alpha, and what is the debate on the other side of not just the risk management change, but what are some innovations in terms of quantitative finance that people are thinking about in terms of getting return on their risk?
Li: Yeah, on the one side, for negative rates or others, we can see that some of the after crisis, some of the market behaviors or market trends really push, even the market quotes break the fundamental. Like Russia now, or even push some of the existing standard models to the limit. In this case as what you just mentioned the negative rate, that people traditionally assumes all those arbitrage free conditions, then all the curves need to be, especially the forward rate, needs to be positive. That’s really the foundation for the arbitrage free. But after the crisis, you could see the Euro, you could see the Switzerland and that’s all the negative rates. This is what we can observe in the market, but in terms of the typical, traditional, modeling, that actually breaks all the modeling assumptions there, that’s what we can see from that point of view; actually we do see the many innovations I mean in terms of the shift to SABR, or even like Free Boundary SABR, like a more advanced approach to really tackle the heart of these problems in the market. On the other side of the world, that the seeking alpha we can see, I mean most of the portfolio management or buy-side, they would still adapt and have the deep understanding of the existing model framework and including the limitation, and then from their side what they need is really a strategy to seeking alpha or reduce variants in terms of the more optimization and maybe how you really control the frequency of rebalancing and also in terms of a better approach basically for the portfolio management and also for that reason because usually we are in the big data world and then they typically would also seeking to tackle the big data challenges in terms of they typically need a more scalable solution and maybe a more flexible framework can really help them with a more different strategy and to enhance their returns.
Jockle: Well Dan, I want to thank you so much sharing your thoughts on Quant Congress and of course we want to talk about all the trends that you want to talk about, so please follow us on LinkedIn or Twitter@nxanalytics. Thank you, I’m Jim Jockle.