“We strive to have a selective portfolio of not very many positions. Essentially, picking the best trends out there and combining them into the portfolio in a way that provides an optimum de-correlation of these candidates”
Our next show provides you with the opportunity to learn from a highly educated founder and fund manager.
He Studied Economics at the Luzon Universidad de Carlos III de Madrid. He went on to earn a PhD in Quantitative Finance in Evolutionary Finance at University of Zurich. Upon graduating he agreed to a research position with Zurich Capital Bank.
Horizon21 made an offer to have Mathias and his business partner Dr. Tilman Keese build a systematic trading program. In 2010 they left Horizon21 to go out as entrepreneurs with AllMountainCapital.
Please give a warm welcome to, Dr. Mathias Bucher.
In This Episode, You’ll Learn:
- The story of founding AllMountainCapital and how much AUM they currently manage
- How they outsource all non-core aspects of the business so they can focus on Research, Trading & Client services
- On the changes in the CTA industry from 2007 to the present
“On a positive note, a drawdown triggers a huge amount of creativity and research in many aspects”
- Why central bank actions are correlated with a drop in volatility since 2009
- The nature of the AllMountain trading model and how it has coped during challenging times
“So now all major central banks are doing the same thing which totally and fully killed volatility, especially since autumn 2011”
- About the Modules that make up the AllMountain trading program
- Sectors and markets that AllMountain trade
- How their different system works and why they use it the way they do
- How they quantify trend strength in a market
“I actually discovered that finance is actually more interesting than they teach you in the college classroom”
Resources & Links Mentioned in this Episode:
“We want to be invested only in the 10-20 best markets at any time.”
Sponsored by Swiss Financial Services and Saxo Bank:
Connect with AllMountainCapital:
Visit the Website: www.allmountaincapital.com
Call AllMountainCapital: +41 (0) 55 511 05 85
E-Mail AllMountainCapital: firstname.lastname@example.org
Follow AllMountainCapital on Linkedin
“I also know that the low volatility state will not go on forever, and we have convincing evidence from many studies”
Niels: You are listening to Top Traders Unplugged, Episode number 011, with Mathias Bucher, co-founder and partner of AllMountain Capital. This episode is sponsored by Swiss Financial Services.
Introduction: Imagine spending an hour with the world’s greatest traders. Imagine learning from their experiences, their successes and their failures. Imagine no more. Welcome to Top Traders Unplugged, the place where you can learn from the best hedge fund managers in the world, so you can take your manager due diligence, or investment career to the next level. Here is your host veteran hedge fund manager, Niels Kaastrup-Larsen.
Niels: Welcome to another episode of Top Traders Unplugged. Thanks so much for tuning in today. I really do appreciate it. On today's show I'm talking to Mathias Bucher, co-founder and partner of AllMountain Capital. AllMountain Capital is a Swiss based asset management firm, and is a spin-off of Horizon 21 back in 2010. They also won the investors choice European hedge fund awards in 2012 in the category of Emerging Managed Futures Fund. For those of you who are new to the show, I just want to let you know that you can find all of the show notes including a full transcript of today's episode on the TOPTRADERSUNPLUGGED.COM web site. Now let's get started with part 1 of my conversation. I hope you will enjoy it.
Niels: Mathias, thank you so much for being with us today, I really appreciate it.
Mathias: Well Niels, let me thank you first for having this conversation with me tonight. I think it's actually a really great idea of yours to do this pod cast series. It should, I think, certainly help investors in gathering relevant and easily accessible information, if they want to invest into CTAs. So yeah, two thumbs up for that.
Niels: Excellent. Thank you so much. Mathias, I think it's fair to say that you belong to the newer generation of CTA firms, who often come at the trading world with a deep academic background, and not like some of the old legends in our industry who often started out as a discretionary trader, kind of a by-the-seat-of-their-pants trader, so perhaps a good starting point would before you to take us all the way back to the beginning, telling us the story about how you went from the stress free world of academia to the high temper world of global finance, as well as the transition from being an employee, early on in your career to now being an entrepreneur, what inspired you to make all these leaps and how has All Mountain evolved since its inception?
Mathias: Right, your assessment is certainly correct. So as a short introduction to myself, I started into economics at HEC de Lausanne, and at the Universidad Carlos III de Madrid, and after having finished my studies, I first went to strategy consulting working for McKinsey & Company for a couple of years. During this time my wife was in the high yield business and I gradually discovered that finance is actually, in many ways, more interesting than what they teach you in the college classroom. Finance actually brought me to the point where I decided to do a PhD in Quantitative Finance, and more specifically Evolutionary Finance, and I did so at the University of Zürich, with professor Hentz. During this time I also worked as a researcher to Zürich Kantonal Bank.
Now, while doing my PhD, I had a dream, a vision, that I really wanted to go into active, institutionalized, professional trading, and so I was totally happy when Horizon 21 made me an offer after my PhD. They actually hired us to build, (together with my business partner today at All Mountain Capital, Tilman Keese) their systematic trading within their single hedge fund unit. So we did that, and in 2010 we had the opportunity to do a spin off from Horizon 21, and we of course jumped at this opportunity. So we founded our company, All Mountain Capital in 2010, and launched our program and have been trading it ever since. And today, I'm happy to talk with you about that.
Niels: Exactly. How long did it take for you and Tilman to build the program, and how did you come up with the initial ideas, because you were obviously coming straight from academia and, I'm guessing, at this time probably didn't have much real trading experience?
Mathias: That's correct for me. Tilman already had a lot of trading experience. I had a lot of background in system development because part of my thesis was actually about creating profitable trading systems with a technique called Genetic Programming and then Power Stream Evolution - this is a proprietary technique that I developed during this PhD. So I was really deeply into model development already, and that was also one of the criteria of why I was hired by Horizon 21. Tilman had a lot of actual trading experience already. So we teamed up and immediately developed chemistry for working together. After some other strategies that we developed, in the single stock equities, we, quite quickly in 2006, started to focus on developing a CTA strategy. In about mid 2007, the strategy was right because it had progressed to the point where we were fully confident to trade it in the market. So we started trading our program in July 2007.
Niels: And at that time did you look at any other CTAs trading in our industry? Did you look at any of them to kind of have a sense for what they were doing and where you wanted to be different, or was it really just based on the research and experiences that you had from your own?
Mathias: No, no, absolutely, we clearly were aware of the traditional ways that CTAs were built: time series, moving averages, volatility breakouts, all that. We were also conscious, at this time that we needed to have an edge, we needed to differentiate ourselves. That was clear from the beginning, and we had quite a good intuition of how we wanted to be different. So that was, essentially, our starting ground.
Niels: Interesting. Perhaps you could give me a brief overview of the program you run today: when it started, and how much AUM you have in the program?
Mathias: As I mentioned, we started in July 2007, and we currently manage $60,000,000 US dollars.
Niels: Excellent. Fantastic. Well before we jump into the detail of the program, I just wanted to jump to a slightly different part, and that's a little bit about your company and how you've organized it. Clearly today there is a lot of technology available, there are a lot of firms today that offer services that allow smaller firms to outsource. How have you gone about dividing between what to keep internal and what to outsource, how have you done that?
Mathias: Right. You know, Niels, coming from this strategy consulting background, for me this was, of course, an exciting challenge, to build our own company. We had, from the start, a very clear view what segment in the value chain we wanted to occupy - where we wanted to focus on, and this is clearly research, it's trading, and it's client activities. All the rest, we designed our firm to outsource to high quality specialized partners: be it in the whole product management, be it in the IT infrastructure part. This was not up and running from the start, but today we have service on three continents, fully encrypted, etc. etc. all the bells and whistles. This, of course, you cannot achieve in a new company, with limited resources, you need to rely on a strong partnership with external parties.
This was our clear strategic focus from the start. To focus on our segment in the value chain and outsource everything else. You know, Niels, you've been around this industry for quite some time, and until about 10 years ago it would not have even been possible to do it the way we do it today, because the electronic interfaces were not in place. Today, this is the case, but it wasn't the case in 2010, and I think It's totally crucial that a young company makes all the leverage it gets from this situation.
Niels: Yeah, I agree, and I think almost in the sense it's like smaller newer companies have a slightly competitive edge, because I do see that some of the larger, very well established firms that have been around for a long time, do carry some quite heavy infrastructure simply because their systems were built at a different time when technology was not as available as it is today. So I agree with you, you can get a lot done with smart thinking and adequate strategy. In terms of the program itself, do you have any target or optimal size that you think you're striving towards?
Mathias: We don't have real limitations in terms of how much we can trade. We consciously trade only the most liquid futures globally, so this gives us no limitation in the foreseeable future.
Niels: Great. Now you mentioned that the program started in 2007. You obviously took it as a spin off in 2010 and formed All Mountain Capital. In many ways, that's an interesting period to start a new firm. Clearly 2008 was a great year for the industry, and I'm sure you benefited from that as well. But, come 2009, just before you go on your own, the market environment has been somewhat different for sure. How do you from a top down view, just looking at your track record, your expectation to the system itself, how do you look at those two periods before 2009, after 2009?
Mathias: So first of all I can confirm what you said. We had an excellent 2008, 2007 was already good, 2008 was very good, 2009 not so good, 2010 was very good again, 2011, comparative to the industry, was excellent, and I think we can talk about the reasons why that was the case, later. In 2012 we suffered with everybody else. 2013 was full. Let me share with you my view why this was the case. I like to segment these periods, 2007 until 2014, where we stand now, into some periods, into some regimes that have different characteristics and where I think I can see some reasons why these periods have been different. So 2007 was a typical bull market, 2008 a typical bear market, so nothing new here. Central banks were accommodating, helping so we saw a recovery in 2009. Until this point there was nothing different in my view from other bull/bear markets before.
Now into 2011 I think we saw a discrepancy between the behavior of the Fed on the one side, who was already very accommodating, and European Central Bank who was not that much accommodating. So we saw a certain discrepancy in the behavior of the two major central banks. Now in summer 2011 was the debt crisis. The whole behavior pattern of the European central bank has changed. Before they were behaving in the wake of the German Bundes Bank, so price stability was very important, and this has dramatically changed in 2011. So from autumn 2011 on, what you see is that all major central banks are behaving in a streamlined way, and what it means...it's like an easing rat race to the bottom. So the Fed is easing, the European Central Bank starts being very accommodating in 2011, and the Japanese Central Bank in 2012.
So now all major central banks are doing the same thing which totally and fully killed volatility, especially since autumn 2011. Already, before, from 2009 until 2011 you saw volatility decreasing, but still there were some pockets of resistance of volatility because not everything was streamlined. This changed in 2011, and ever since, volatility has essentially evaporated from the system. This is, of course, not a nice environment for CTAs. You can see it with the performance figures of many CTAs out there. Now, this being said, I also know that the low volatility state will not go on forever, and we have convincing evidence from many studies. I recently heard about a study that goes back as far as 800 years, analyzing price trends, and they clearly show that you have these periods of low performance, of trend-less periods, but trends always come back. So there will be a future for CTAs and the question is how long will it take until we get to this pick up in volatility, and here I'm not a prophet.
Niels: Well, absolutely. I think you are referring to the study from Larry Hite, at ISAM, which is obviously very interesting, and great to see that these firms are able to go back all these years, these centuries, really to get data, and in fact that they data confirms what I think at least practitioners of the CTAs strategies believe, namely that trends will always be there, and but it doesn't mean that you make money every year. So yeah, very interesting indeed. Thank you so much for sharing that.
Now, when you look at your own track record, in order to set the stage before we jump in and talk about the program itself, is there any different periods, different stages of the track record, meaning, have there been any major discoveries or upgrades to the system that people should be aware of when they look at the track record? What I mean by that is if you look at someone who has been around for 20 years, you can be sure that the models they trade today are not the models they traded when they first started out. So I'm just trying to set the stage here, to find out whether you have evolved the model a lot, before we talk about the model itself.
Mathias: I think it's a super relevant question. The nature of our model has not changed. This said we can talk later about the reasons for that. I think we believe a lot in robustness, however, there have been a lot of different steps and measures have been taken, a lot of research effort has gone into many aspects of how we trade, for example, the portfolio composition has clearly become better. The risk management has improved. The efficiency of execution has been improved. We have almost fully automated all processes. These kinds of things have dramatically improved, of course, but we strongly believe if we commit ourselves to be a medium term trend follower, we should stay a medium term trend follower, and not start trading intra-day patterns or this kind of deviations, and allow for style drift in the end.
Niels: Sure, makes sense. Now, for the trading program itself, maybe you could explain in your own words how you have stuck to the program and why you've designed it in the way you have. Is there a particular philosophy behind it, etc. etc.? Just in your own words how you best describe what you do.
Mathias: Sure. Our trading program has essentially two modules. The backbone module is a medium term trend following module, and there is a second module that kicks in that is situationally dependent - which is a mean reversion module, that essentially protects the invested capital if you see strong volatility increase against the trend. So these are our two modules, core modules of our program. Now let me talk a bit about the trend following module.
Mathias: Maybe I could start talking about this by looking at our typical trend following model would look like. It will consider time series information: looking at moving average crossovers, volatility break outs, and create a portfolio from the bottom up. So it's time series information and bottom up creation of the portfolio according to when these trading signals happen. So we take a different approach here. Based on our quantitative background, we essentially try to rank the markets in our universe, and then pick the qualitatively best trends, and selectively combine these best trends into a portfolio that will not encompass all markets of course. It will typically have 10 to 20 positions so we combine them into a portfolio by achieving a second goal, or trying to achieve a second goal, which is optimal diversification of the portfolio positions picked. So we strive to have a selective portfolio of not very many positions - essentially picking the best trends out there, and combining them into the portfolio in a way that we have an optimum de-correlation of these candidates.
Niels: so let's break that down a bit, because that's a big mouthful for people to comprehend. So let me just start by setting the scene a little bit. Perhaps you could mention, not individually, but perhaps you could tell us a little bit about the markets you do trade, how many, and also the sectors, whether you are fully diversified, let's start in that area.
Mathias: Sure, sure. As I mentioned before, we trade all future markets globally that are liquid enough to trade. So we never want to run into any liquidity problems, so we constantly monitor the liquidity of these markets, and so, at the moment, we are looking at about 80 to 90 markets globally. We trade all sectors: equities, bonds, meats, metals, Forex, softs, grains, we trade all sectors, and we typically will strive to have picks from all sectors at the time. This will be a major contributor to this diversification that I mentioned.
Niels: Sure, there's no doubt that sector weights plays an enormously important role in determining performance, at the end of the day, so that's a very important point. Let's go back to the model itself. You mentioned that the core is a trend following model.
Mathias: Exactly, a medium term trend follower, but instead of using a moving average that will give me a signal when to enter, I will apply one econometric model to create a score for each market in the whole universe at all times. So we are looking at markets cross-sectional and we use one model because in the end, for robustness reasons, to come up with this core and this core will tell us, (as a hypothetical example) in this market copper has a great trend right now up, bonds are strongly up, and equities are up, so we will have the same score for all markets cross-sectional, and this allows us to compare these markets. This is our first step that we calculate on a daily basis. We compare all markets on a daily basis.
Niels: But what I'm actually interested in, Mathias, is just to take a step back because, I think most people listening to us today are used to thinking of trend following as being, as you rightly say, it's a crossover of two moving averages. It might be a price breakout of a channel where the market, if it goes above the last 50 day high, it's a breakout. You talk about the signal generation in a different way, and I'm not sure I actually understand what you mean by a model like that. So if you can explain that a little bit more as to how does it know that copper is in a big trend if it doesn't look at the price breakout or the moving average crossover?
Mathias: Right. Let me maybe take one step back and let me explain the motivation of why we use this different approach. If you use time series signals, moving averages, volatility breakouts, you will not be able to control the amount of markets that you trade - the signal happens when it happens and you trade it. So your portfolio typically will be rather large and will have many positions, maybe 50 positions at the time. We try to be more selective. We want to be invested only in the 10 to 20 best markets at the time. So we need a methodology for how we can compare the trend quality at a given time, cross-sectional, across all markets. So what we have developed is an econometric model that essentially scores the trend with various criteria that I won't go into right now. It scores the quality of this trend for each market.
Niels: Just to clarify here. What you need in order to make that score, is that based on anything other than the price of the market?
Mathias: No, it's absolutely only price driven. That's totally right. So we have a single score for each market, and some scores will be high, some scores will be close to zero and some scores will be very low. So the positions that have a very high score will be picked long, because there is quite a big probability. The positions that have a very low score, will be picked because there is quite a high probability, and the scores that are close to zero are likely not to be picked.
Niels: Sure, and the inputs that you need, i.e. the price, is that just a daily sample. So at the end of the day you need to calculate this, or is it something that happens during the day as well.
Mathias: No, that's right we use high, low, open, close prices to calculate this score. I think your calculation frequency should also be in relation with the span that you are looking at. We want to have a program that has characteristics of a medium trend follower, so it does not make sense to recalculate these score more than once per day. You can do so, but the difference in the score values will be so minimum that it essentially has no relevant impact.
Niels: Sure, sure. So you get this score and how does the model know whether to pick the top 10 or the top 20, is there something that dictates, saying OK, if it's above a score of 70 or below a score of 30, just to pick two numbers, we take all of those signals and we leave everything in-between alone, or do you set other parameters that decides the outcome of how many positions that you end up with?
Mathias: Essentially we have several boundaries. So we want to pick a minimum amount of positions to ensure a certain based diversification. We want to be sure to pick in very attractive situations, like in 2008, more positions because there is more juice in it, and we want to make sure that, while picking, we don't give too much weight to one sector or one asset class. If only equities are trending today, we don't want to have a portfolio that consists only of equities.
Niels: Sure, OK, excellent, and does the model know, at this stage, when it brings you on a daily basis, say the top 20 markets that you should be involved in...how does it go if there is a new market coming along tomorrow that actually gets a score that is high enough to warrant a position...how does it know how much to risk in that trade?
Mathias: Yes, so essentially what we are looking at here are thresholds that need to be breached, so when you have just a minor change in the relative ranking, it would not justify to replace this position because of trading costs, slippage, etc. etc. So we want to see a distinctive outperformance of a relative score to change one market against another.
Niels: OK. When you get in a position, does it automatically mean that you have a stop loss associated with that position, or do you need the model to basically kick a market out of its preferred list status in order to get out of that market?
Mathias: No, essentially what you are talking about here is our risk management approach.
Niels: Well, more the exit side. I know it ties into the overall risk management which we'll probably talk about also on top of this, I was just trying to get a feel for whether you treat your positions individually, meaning that I'm going to allocate maybe 50 basis points risk, so if I lose more than 50 basis points on this position, I'm out, or whether there is a different mechanism that actually decides how much risk to have.
Mathias: So there are two ways that our program can lose a position. Either it's through risk management, and I would count the stop losses as a part of risk management; or it can get crowded out, so it can be replace with a better score - let's say that it lost the space in the portfolio for this asset class, so it will get crowded out. So there are two ways of losing a position.
Niels: What I'm sensing from our conversation is that the model is really somewhat different from a traditional trend following model, because it's not really something that would go from being long to being short in a market. It's really just looking at the overall universe of markets, deciding which of these opportunities should be in the portfolio at any given time.
Mathias: Correct, yes.
Niels: Excellent. If we just look from an overall point of view, what does that equate to when it comes to say, trade duration? How long are you typically involved in a winning trade, or in a losing trade, how does that all fit together?
Mathias: You know, after the fact, and despite the different way we do many things, a lot of these key parameters tend to be pretty similar to typical medium term trend followers, so we have 4 to 7 weeks on average for a winning position; and losing position, depending on the market environment, we can lose it very quickly. Also, maybe just a bit more of a top down picture, you know. It's always helpful (to get a good perspective). These more technical facts will provide a little bit of the market flavor. In 2007 and 2008 we had a great year, like all other trend followers. 2009 we had a rather difficult year like many trend followers. 2010 was very good, and I think a lot of other trend followers had a good year. 2011 was also very good for us and here, this is clearly a year where we could distinguish ourselves from many of the competitors with the industry being down and we were 14% up. Why were we up at this time? It was essentially because we were able to be way more selective. When all markets go up we will be up. When there is no trend in the markets like 2012, we are down, like many of the others. If there are selective pockets of volatility, that our model can discover and trade, and the majority of the market has not much volatility not much going on - in 2011 there were some very juicy pockets of volatility, our model can discover this and trade them because we are more selective. So that's maybe something that brings a little bit of flavor or illustrates better how our model can distinguish itself.
Niels: It's quite interesting because, in my mind, selectivity is a two edged sword, because in a year like 2008, in a sense, a lot of markets were trending. So the more markets you could get involved in the more positions you could have the more money you made, yet, you also had a very good 2008, so how does that tie in with trading fewer markets, or asked in another way, how many positions can your system expand to if it recognizes lots of good opportunities in many markets at the same time?
Mathias: So, we will not exceed 20 positions at one time. And to answer your question, why is it possible that we have a good year in 2008, is simply because you allocate more risk to a given position. If you trade, let's say, 3 US bonds with small risk, or 1 US bond with more risk. The output, the performance will be the same if all go up, so essentially what happened, we had just fewer bonds and fewer other positions than a typical trend follower, but the individual risk budget was larger per position, which the total result was very good.
Niels: And how does the system know to turn up the risk budget, meaning just for clarification, if you want to add a new position, and it has a certain score, is it the score itself that allocates...so just as an example, say it gets a score of 65, does all positions with a score of 65 get the same risk allocation, and maybe a market coming with a score of 75, it gets a slightly higher risk allocation, or how do you decide how much to risk in any new opportunity?
Mathias: So there are two criteria: there is the pure score, which tells us this is a good opportunity; and then there is the diversification function in the existing portfolio. So two criteria here. It's also very important to mention that once we have picked our candidates, we will not size the positions according to the score. Score has to function to identify opportunities, but the sizing is driven by risk management. It's not driven by scores. Scores is more the flag that waves, "Hey!" This is an interesting market to get involved in. The sizing will be done by risk management.
Niels: OK, interesting. Now, we are going to talk a little bit more about that for sure, but in terms of performance drivers, if I can call it that, what do you think is the key performing drivers in your model? Clearly selectivity seems to be very important, but is there anything else that you would say is part of your difference that you can point at?
Mathias: The performance driver is the volatility expansion in the market, and our model is able to identify volatility expansion situations well, and then trade it selectively. And then we have, of course, a pretty good risk management on top to mitigate the risks that are associated with it. But it's really very important to state and understand that in the market, (and in a bull market) where volatility is collapsing, you can have as good a risk management as possible, but you will not be able to make a system like ours profitable. You would need to trade markets differently.
Niels: And so once you are, for clarification, when you are in a position, so do you change the position size during the lifetime of the position, or does that stay constant until it gets kicked out of the portfolio.
Mathias: Again this is a function of risk management. If the overall risk increases the position (if nothing else changes) would increase too. If risk management perceives danger for this position it gets, maybe, totally cut, or it gets reduced. So the sizing is always a function of risk management in our model.
Niels: OK, very interesting. Trade implementation is a subject that I wanted to also touch upon, and that is a little bit about how the system actually runs. How difficult or easy is it to run and maintain? Because I think, for a lot of people, this may sound like a black box, and so on and so forth, but the reality is, of course, that people using these types of systems they know what goes in and they know what to expect to come out. But just sort of the operational side of running a model that follows so many different markets and has a few different parameters, how does that work in practice?
Mathias: So we, from the start, went to great lengths to fully automate and only trade electronically. It's very important in our set up, in our case, so our signal generation is fully automated. Our stops are fully automated, and we trade everything electronically, algorithm based, so we did, essentially...a key ingredient to running a model like ours successfully over time. It mitigates a lot of risks.
Niels: So does that mean that nobody needs to watch the computer, or what does actually happen?
Mathias: (laugh) No, of course not. It's totally an illusion if you think you can program a system and then let it run. That would be fully not responsible behavior. You could never do that in a real trading environment. Of course, monitoring is up and running. It means that we don't need to intervene unless there is a hiccup somewhere. It can be that somewhere in the internet an outage occurs or that we want to trade a market and there is a market outage or market feeds are not coming properly in. You know, API feeds for example, these kinds of things need to be constantly monitored and, again here you can rely on a lot of technology, you can get alerts via email, pop-ups, etc. etc., and we fully use this opportunity.
Niels: But I guess what you're saying is that actually the data will come in automatically, the system will make its calculations automatically and it will send any adjustment orders automatically. Nobody needs to press any buttons on a daily basis.
Mathias: This automatic trade entry happens in case of stops, just because it doesn't make sense that you wouldn't need to review this. However, in the case of portfolio adjustments, we always have this "four eye principle", that we manually oversee, before the order gets executed, and we verified that it's in line with the whole program.
Niels: OK, so you get told every day whether there is an adjustment amount?
Niels: Now you mentioned another subject which I would like to talk about next - risk management. You've mentioned it and talked about it so that my understanding, at least, is that this is very important in the way you approach things, the way you design things, so maybe you could tell me how you define risk and what targets of risk you're looking at, and how you've gone about using this approach?
Mathias: So, we're typically looking at risk as the volatility with the VAR budget, like pretty much in line with the industry here. Our risk management consists of three layers...
Ending: Thanks for listening to Top Traders Unplugged. If you feel that you have learned something of value from today's episode, the best way to stay updated is to go on over to iTunes and subscribe to the show so that you'll be sure to get all the new episodes as they're released. We have some amazing guests lined up for you, and to ensure that our show continues to grow, please leave us an honest rating and review on iTunes. It only takes a minute and it's the best way to show us you love the pod cast. We'll see you next time on Top Traders Unplugged.
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Date posted: 07 Jul 2014no comments