PSYCHOLOGY OPTIMISM VS PESSIMISM ESSAY 08 30
brown evolution writers tickets goose island 05 05
APA STIL UNI AUGSBURG 09 04
essay on christmas in telugu 05 02
TIPS ON WRITING AN ESSAY ABOUT MYSELF 08 26
velarized examples of thesis 05 13
brown evolution writers tickets delete event 05 04
OPINION BIRCHAM INTERNATIONAL UNIVERSITY 09 03
francis report camhs birmingham 05 14
MG UNIVERSITY COST ACCOUNTING QUESTION PAPER 09 04
a level general evolution writers 2005 answers 05 03
brown evolution writers tickets website 05 03
UNIVERSITY RESEARCH COUNCIL UGANDA 09 04
SAMPLE REFERENCE LETTER FOR UNIVERSITY APPLICATION FROM EMPLOYER 09 03
minimalist music essay 05 12
three ancient writing materials 05 04
type article review on capital punishment 05 11
KAPITALISTISCHER REALISMUS ESSAY HELP 08 24
GOOD ASSIGNMENT HELP 08 30
need help writing philosophy evolution writers 05 04
nuffield scholar reports definition 05 13
PAPERS UNI OF WAIKATO 09 03
CSIS JAPAN REPORT FINDS 08 25
LOUGHBOROUGH UNI SHORT COURSES 09 04
1984 research essay topics 05 02
kakabadse reporting 05 11
INTIPATI PERSIDANGAN ASEAN 2018 REPORT 08 26
olympus stylus sz 16 16mp review of literature 05 14
QUEST LAB UNIVERSITY OF MIAMI 09 02
MICHIGAN UNIVERSITIES COST COMPARISON 09 04
UNIVERSITY OF SOUTHERN CALIFORNIA ARTICLES OF INCORPORATION 09 02
MANCHESTER UNIVERSITY CHEMISTRY PAST PAPERS 09 03
ARTICLE ABOUT UNIVERSITY OF MICHIGAN 09 04
evolution writers com 05 03 3
ADMISSION UNIVERSITIES COM 09 03
ACADEMIC STAFF COLLEGE KASHMIR UNIVERSITY 09 04
easybibedu 05 02
cottweiler presentation software 08 21
NEED DISSERTATION METHODOLOGY ON MANAGEMENT ONLINE 08 26
beloved ap essay 05 15
igcse english paper 1 tips 05 03
ib english hl evolution writers 1 guide 05 04
agriturismo biorussi prezi presentation 05 08
49ERS VS SEAHAWKS BOWMAN INJURY REPORT 08 26
live evolution writers reviews 05 04
super evolution writers mario chapter 1 3 blue pillar 05 05
paperhelp reviews 05 03
ESSAY EXAMPLES UNIVERSITY EDUCATION 09 03
evolution writers of diary writing 05 04
brown evolution writers tickets yelp 05 03
Cornell Tech, Manhattan ​you welcome to a blessed TV today we have niggle kelegian the founder and CEO of quest partners a CTA was 680 million dollars in AUM with 13 and a half percent annualized returns over the last 16 years now niggle your CTA generates alpha through strict discipline to positive convexity in the markets now before we get into your strategy can give me an introduction into your background in the industry so I with a degree in electrical engineering from there I went to work in consulting and by chance ended up being placed on a project at Salomon Brothers in the late 80s and I became interested in system design pretty much on my own so it became a personal quest to actually you know learn about system design and finance I got a MBA from Columbia Business School and after Business School I was ready to go you know ready to go to start the CTA I had models running every day and I was waiting for capital to start an actual CTA couldn't raise enough money to get going decided to actually take a job as a trader in a long short equity fund for a couple of years then ran a founder phone for a couple of years finally in 98 I had enough interest to start a founder fund and in 99 CTA initially in 99 I had a partner in the CTA in 2001 we decided to go our own ways and in 2001 I started quest part of our history is that in 2003 we got an allocation from man global strategies the founder phone of man and from 2003 to 2010 effectively 90% of our capacity was utilized by man in 2010 they redeemed and we've been growing the business sense so we go how many products do you actually have and how many strategies do you run so we're on four different strategies the flagship strategy is the Alpha quest original program which has analyzed about thirteen and a half percent over 16 years with seven and a half percent of annual alpha to the CT index we also run a CT a replicator that one has that program has four year track record eighty-six percent correlation to the CT index with about one and a half percent of annual alpha three index we also run a short equity short buy strategy and a fixed-income short buy strategy as well the equity short buy strategy has about thirteen percent of annual alpha positive alpha three SNP and the fixed income strategy has about one percent of annual alpha to the yes than your short one of the strategies have generated alpha to their benchmarks Nicoll give us a background into the traditional large CTA index replicator strategies and trend following strategies and tell us about the impact of style drift in these large CTAs the way we see the CTA industry is that the it today it's easily replicable with the classical trend following strategies such as moving averages or channel breakouts our city a replicator since we started in 2011 has generated an alpha of one and a half percent two indices with 86 percent correlation and it is 100% transparent relative to these classical trend following strategies we've seen seven style drifts introduced themselves in the CTA indices these are a larger allocation to fixed income which has been a very large contributor to performance more allocation to very long-term trend following strategies which are less capable of capturing trend reversals during high wall periods an increase in allocation to long only trading which also is less positively skewed than shorts in increase in allocation to equity and fixed income bottom picking which is a very uncorrelated source of return but again negatively skewed in increase in allocation to fixed equity long trading carry trades in foreign in foreign exchange and spread trading in commodities all of these style drifts have negative convexity and result in negative returns during equity Corrections in particular if you look at how the city indices performed in oh seven to a nine relative to replicators that have better of one two the indices the city indices underperformed by sixty percent on an absolute level in over a two year period as a result of this tolerance so the city indices as a result of these toddlers have become less capable of generating strong returns during equity Corrections you so what differentiates your core vestment philosophy in your strategy a key differentiator in what we do and in the Alpha that we try to generate is that we have a very strict discipline in normalizing the Alpha for the convexity that it's taking so the more negative convexity a strategy is exposed to the higher the alpha the higher the Sharpe ratio now alpha which has a byproduct of negative convexity is highly cyclical it's replicable and I would say an investor does not need to pay incentive fees to be exposed to it so particularity of the alpha that we've generated is that it's actually all positively convex so nine out of ten source of alpha today in the hedge fund industry being negatively convex we're very limited in terms of what we choose to do as we're aiming for positive convexity this makes our alpha more stable and it actually is particularly attractive because it kicks in in periods of equity Corrections can you elaborate for us and explain the concept of positive versus negative convexity in alternative investments and how it plays into your strategy the difference between positive and negative convexity is that with a positively convex strategy when you're making money you tend to have larger returns when you're losing money you tend to have smaller returns for a negatively skewed strategy or negatively convex strategy positive returns tend to be smaller on the way up and larger when
you're actually losing money another way is to looking at it is is the acceleration and the returns when you're making versus losing money in today's world and seen 90 percent or every single hedge fund strategy outside of short sellers tend to make money slowly and lose money very fast that's typically negative skew even CTAs which used to be positively skewed or now have a neutral skew of around zero so with negative skew comes an acceleration of losses once the market starts to go down so if you're making money with 10 percent of all you're losing money with 20 or 30 percent of all so negative convexity with positive alpha positive convexity with negative alpha effectively outside of the convexity there's very little alpha left in the hedge fund industry one particular interesting factor to evaluate is can we generate alpha which is positively convex at the same time which is skill based which is not replicable the dangers of alpha which is negatively convex is that they can get very easily crowded due to the very steady returns or high Sharpe ratios we have shown that high Sharpe ratios come with larger drawdowns than lower Sharpe ratios so strategies that are negatively skewed such as most - strategies tend to get crowded when you sell insurance there is a premium which is collected for selling that insurance the risk of a storm against which the insurance is on the written is not affected by the price of the insurance this is not true in financial assets as the price of the insurance becomes cheap and people continue to underwrite that insurance the potential financial storm is initially delayed but then once the factor is actually crowded there is a deleveraging effect and you can have quite substantial Corrections the Corrections are not used due to changes in fundamentals in that factor they purely due to crowding and leveraging which is reversing with negatively skewed strategy you have alpha which gets crowded which is cyclical which is predictably negatively skewed with very large losses you what is your process to find premiums in the market and to generate alpha for investors the goal of our process is to generate alpha which is positively convex where the typical alpha jet in the hedge fund industry is due to its negative skew or negative convexity we want alpha which actually outperforms which really kicks in during equity Corrections or during vol expansions the way we do that is we're looking for markets where the volatility has compressed or where the volatility is exhibiting certain characteristics of potential valley expansion so the upside vol versus the downside wall is very different when the market is going up the volatility is slowing or going down when the market is going down the volatility is going up such characteristics and the volatility help us predict the behavior of a correction and market lines when it comes so when we see these type of environments in the market we're not actually entering a trade yet where we're looking to wait for a move to actually start once a move starts within a day or two we're actually entering a trade and the vol environment typically helps us predict the size of that move we also exit trades much quicker than typical CTA which one which makes us quite complimentary to the large long term trend followers we're exiting trades when our alpha expectation goes down so we try to maximize the alpha that we generate relative to the beta exposure that we have so if you look at our returns our alpha Teresita industry is over 50% of our returns our beta tricity industry is only 30% of our returns so we generate very large amounts of alpha relative to Beto it takes a very strict discipline not to accept sources of alpha which are negatively skewed such as mean reversion or bottom picking equities we believe that those sources of alpha are highly unstable and when they break they break with four five six or larger standard deviations which are not very pleasant and they typically break at times where you need returns the most when equities are going down in the process of generating positive convexity and alpha at the same time we've realized that the optimal time frame to trade is between seven to ten days per trade which is much more short-term than the classical CTAs this makes us very complementary to a portfolio of large CTAs as a matter of fact during the three largest city a draw downs with our overall returns were positive 2009 2014 City industry went into its longest road on ever we actually were making returns in that period so our time our time frame is very important in generating positive convexity we've seen that if you trade the shorter than seven days per trade you're positive convexity goes down if you trade longer term than ten days per trade you're positive convexity goes down as well today if you look at the typical City Index replicators your trading thirty forty or fifty days per trade on average so much more long-term than we are by trading more short-term you become more transaction cost sensitive but that's where our volatility filtering techniques become critical and that's where the skill comes in and to what we do so what environments can prove challenging for your strategy and how do you anticipate draw downs in the market the most challenging environments for our strategy is when typically when the implied Vols are compressing very fast so our largest drawdown was in 2004 when the VIX went from over 42 but 10 or second largest roll down was in 2009 when the VIX went from close to 90 all the way down to about 15 in those periods is our largest draw downs and typically there's a reduction in our alpha relative to the CPI index as well the most important discipline in reducing the risk of large drawdown again for us is positive convexity when we if you look at the draw downs of our strategy historically they've been only our largest Rodon has been 1.2 times our fall for the CTA index the largest drawdown is 1.6 times its valen index is 3.6 times its vault for example so positive convexity means that draw downs are a much smaller morphable multiple of volatility that's really critical for what we
do our largest draw downs have substantially decreased over time as we have been able to internally diversify their sources are the sources of alpha that we've been utilizing in our in our trading when we are going into a drawdown the rest of your hedge fund portfolio is stated typically having its top decile performance so we're extremely complementary to a portfolio we're not trying to be the best standalone investment we're trying to make returns both and up and down markets and equities but in particular we're looking at our incremental risk adjust returns rather than our standalone risk adjust return so as an addition to a hedge fund portfolio we're extremely attractive can you give some historical examples of volatility compressions and expansions and how you responded in your position to interesting periods for our program where 2013 where the CTA index was down five and where of 15% despite a low vol environment the volatility had already compressed quite substantially coming into 2013 volatility was stable over the year but throughout the year there were six short-term vol expansions or VIX increases where the VIX went from 14 to 18 in each one of those vol expansion periods we generate fourth we generated four to five percent and returns overall on the year we outperformed the index by 20% despite a low vol environment due to our short-term trading style another interesting period is you know January and February 2016 SNP dropped about 12% CTA indices were up about 7% we were up around 24% in the period the vol expansion that happened in that period was quite substantial and our models were able to allocate to you know have larger positions with very tight stops very high conviction positions and we were able to return almost four times what the CT index returns in that period tell us who your investors are and why they're looking at your strategy investors allocate to us due to the returns that we provide during equity Corrections where during typical equity Corrections you see the volatility of typically h1 increase at exactly the worst possible time and the correlation to the equity is also increase in the worst possible time our returns are positive at those specific moments so the large sophisticated pensions are looking for protection which is cheap or that actually even pays during equity rallies or our typical investors so today we have for pensions invested with us two of those pensions are actually quite large with over a hundred billion in assets themselves so very sophisticated city investors other traders also use us as a hedge sometimes for option on the writing so they have a short option book and they use us as a hedge for that option book you The Debbie Friedman School of Sacred Music.
https://Dissertation.space