We establish that a large class of non-Markovian stochastic volatility models converge to an invariant measure as time tends to infinity. Our arguments are based on a novel coupling idea which is of interest on its own right.
Invariant measures for fractional stochastic volatility models. (arXiv:2002.04832v1 [math.PR])
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Intra-Horizon Expected Shortfall and Risk Structure in Models with Jumps. (arXiv:2002.04675v1 [q-fin.MF])
The present article deals with intra-horizon risk in models with jumps. Our general understanding of intra-horizon risk is along the lines of the approach taken in Boudoukh, Richardson, Stanton and Whitelaw (2004), Rossello (2008), Bhattacharyya, Misra and Kodase (2009), Bakshi and Panayotov (2010), and Leippold and Vasiljevi'c (2019). In particular, we believe that quantifying market risk by strictly relying on point-in-time measures cannot be deemed a satisfactory approach in general. Instead, we argue that complementing this approach by studying measures of risk that capture the magnitude of losses potentially incurred at any time of a trading horizon is necessary when dealing with (m)any financial position(s). To address this issue, we propose an intra-horizon analogue of the expected shortfall for general profit and loss processes and discuss its key properties. Our intra-horizon expected shortfall is well-defined for (m)any popular class(es) of L'evy processes encountered when modeling market dynamics and constitutes a coherent measure of risk, as introduced in Cheridito, Delbaen and Kupper (2004). On the computational side, we provide a simple method to derive the intra-horizon risk inherent to popular L'evy dynamics. Our general technique relies on results for maturity-randomized first-passage probabilities and allows for a derivation of diffusion and single jump risk contributions. These theoretical results are complemented with an empirical analysis, where popular L'evy dynamics are calibrated to S&P 500 index data and an analysis of the resulting intra-horizon risk is presented.
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Reflections on the nature and meaning of teamwork
Teamwork is a complex social dynamic, at once a principle of optimal efficiency and an occasion of personal cooperation. We might visualise this complexity in terms of a vertical pursuit (the achievement of some collective goal) and a horizontal relationship (the cooperation among team members as they pursue the collective goal). The vertical is conceptually prior: teammates do not come together for the primary purpose of interacting with each other, as if that were an end in itself; there is always some goal or purpose that structures the cooperative efforts. At the same time, each individual who contributes to this purpose brings a particular set of talents and, even more importantly, a personal dignity and inviolability that should never be transgressed in the vertical pursuit.
In ideal situations the synergy of the horizontalâhow well the teammates work together and blend their talentsâtranslates directly into greater vertical achievement. Good teams accomplish more working together than individuals can working separately. But the two values are distinguishable and can reside in some tension with one another. For instance, we can imagine a teammateâletâs say a basketball playerâwho because of her exceptional talent does more than anyone else to help the team win but is rather selfish and uncooperative. Compare her to a teammate less talented but more versatile, cooperative, and supportive. An obvious question presents itself: Between these two, who is the better teammate? The answer to that question might turn on which of the following questions one considers the more helpful gloss: Who gives you a better chance to succeed? With whom would you prefer to play? Teams typically work or play together over extended periods of time, which might argue in favour of the more cooperative teammate. Team morale matters and will usually manifest itself in objective results, especially over the long haul.
It is useful to distinguish tangible and intangible assets, both of which contribute to effective teamwork. By the former I understand those skills and expertise that coordinate with those of others, and, especially, the practical intelligence to know how to support other performances. Good teammates know how to put others in a position to succeed, an ability that is not only or even primarily a matter of simple good will. Of course, there is no substitute for sheer talent in team enterprises, especially when we keep in mind that the vertical pursuit always enjoys a structural primacy. Nevertheless, some intangible qualities are indispensable elements of team success: good energy, enthusiasm, non-critical support of teammatesâ efforts, and a willingness to take on less glamorous or enjoyable roles for the sake of the team. Anyone who has ever been part of a team knows how valuable these qualities are.
The social construction of the self
Genuine teamwork involves both commitment and risk. Teammates pledge themselves to one another in an effort to become a unified group rather than a collection of individuals, a pledge that involves a leap of faith. This leap requires that one subsume oneâs self-interest to the interest of the team. There are a couple of ways to understand this. First, one could consider the leap of faith an investment strategy according to which individuals forego immediate satisfactions, such as scoring a lot of points or gaining a lot of attention, for an opportunity to reap greater satisfactions, such as contributing to a more successful team effort. The implicit suggestion is that one would prefer to contribute in some fashion, large or small, to a winning or successful teamâor at least a team that is doing things the âright wayâârather than shine on a losing team, even if there is more glory or attention in the latter.
Needless to say, not everyone will be convinced by this offer. The second way to understand the team commitment is as an instantaneous or transformative experience for the players; the appeal is still to oneâs self-interest, but the commitment here redefines precisely what constitutes self-interest. The transformative paradigm understands humans to be fundamentally relational and collaborative beings. This understanding does not depend for its intelligibility upon future dividends; it suggests that quite apart from the increased efficiency of the team dynamic, teammates experience a deep satisfaction from the mutual commitment to a larger purpose.
The outline offered above provides a general account of teamwork even as a few examples have made reference to world of sport. There are obviously different kinds of teams, which we can distinguish according to different vertical pursuits: athletic teams pursue victory, and there are analogous instances in medicine, business, law, and many other fields. Just as the vertical principle can vary, the horizontal relationship obtaining among teammates can vary as well. For example, using a sport analogy, we identify some teamwork as essentially additive (teammates work separately but accumulate their efforts, as in golf); some teamwork is coordinated (teammatesâ efforts are still largely separate but involve some interaction, such as in relay races); some teamwork is corporate (individuals occupy different positions and have different skill-sets, such as in baseball or American football); and some teamwork is interchangeable (individuals can, depending on circumstances, fluidly trade roles, as in basketball). Despite all these variations, the basic challenge of teamwork is similar.
Belonging
The teamwork is a model of human society and undeniably presents some formidable challenges, but in its best instances it offers to participants an opportunity to experience the simple and profound satisfaction of knowing that one belongs.
Notes:
- This blog post is based on The Nature and Meaning of Teamwork, Journal of the Philosophy of Sport, Volume 42, 2015, Issue 1.
- The post expresses the views of its author(s), not the position of LSE Business Review or the London School of Economics.
- Featured image by Mike Benson on Unsplash
- When you leave a comment, youâre agreeing to our Comment Policy.
Paul Gaffney is an associate professor in the philosophy department at St Johnâs University (New York City). Dr. Gaffney also regularly teaches business ethics and other courses at John Cabot Universityâs summer program in Rome, Italy, since 1999. He was a visiting fellow at the Institute for Public Philosophy at the University of North Dakota (Spring 2009), and has received grants and fellowships from the National Endowment for the Humanities and the Heritage Foundation. He is the editor of the Journal of the Philosophy of Sport since 2015. He is currently writing a book entitled Teamwork in Sport and Society.
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Computational humanness, analogy and innovation, and soft concepts
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Do Copycat CTAs Outperform Individualistic CTAs?
Our society teaches us, that it is good to be different. That our trading strategy must be always unique, creative and individualistic. It is boring and unprofitable to be the "average", to do what the others do. And then, there is a research paper written by Bollen, Hutchinson and O'Brian which offers the opposite view. Their analysis explains there exist one hedge fund style where everything is the other way round - trend-following CTAs funds. Their interesting (but for some maybe controversial) paper shows that CTAs with returns that correlate more strongly with those of peers have higher performance. It appears that CTA strategy conformity is a signal of managerial skill. Now, that is an eccentric idea :)
Authors: Bollen, Hutchinson and O'Brian
Title: When It Pays to Follow the Crowd: Strategy Conformity and CTA Performance
The post Do Copycat CTAs Outperform Individualistic CTAs? appeared first on QuantPedia.
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A Markov-switching COGARCH approach to cryptocurrency portfolio selection and optimization
Abstract
Blockchain is a new technology slowly integrating our economy with cryptocurrencies such as Bitcoin and many more applications. Bitcoin and other versions of it (known as Altcoins) are traded everyday at various cryptocurrency exchanges and have drawn the interest of many investors. These new types of assets are characterized by wild swings in prices, and this can lead to large swings in profit and losses. To respond to these dynamics, cryptoinvestors need adequate tools to guide them through their choice of portfolio selection and optimization. Bitcoin returns have shown some form of regime change, suggesting that regime-switching models could more adequately capture the volatility dynamics. This paper presents a two-state Markov-switching COGARCH-R-vine (MSCOGARCH) model for cryptocurrency portfolio selection and compares the performance to the single-regime COGARCH-R-vine (COGARCH). The findings here are in line with the literature where MSCOGARCH outperforms the single-regime COGARCH with regard to the expected shortfall risk. The COGARCH specifications here capture the structural breaks and heavy tailness within each state of the Markov switching in order to achieve a minimal risk and a maximum return. The flexibility of R-vine copula allows adequate bivariate copula selection for each pair of cryptocurrencies to achieve suitable dependence structure through pair-copula construction architecture.
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Economic policy uncertainty and non-performing loans: The moderating role of bank concentration
Publication date: Available online 12 February 2020
Source: Finance Research Letters
Author(s): Helen Louri, Maria Karadima
Abstract
Following the financial and debt crises in the euro area and the delays in formulating a cohesive policy response, banks faced serious problems with the increase in non-performing loans (NPLs) being the most threatening. Economic policy uncertainty (EPU) has often been blamed for initiating and propagating NPLs. In this study, we attempt to estimate empirically if EPU has a significant effect on NPLs and if this effect can be restrained by another legacy of the crisis, namely bank concentration. By employing a panel dataset of 507 banks from four major euro area countries (France, Germany, Italy and Spain) during the period 2005-2017, we find that EPU has a positive impact on NPLs but this impact is significantly moderated by higher bank concentration.
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Optimal Risk Taking under High-Water Mark Contract with Jump Risk
Publication date: Available online 12 February 2020
Source: Finance Research Letters
Author(s): Congming Mu, Jingzhou Yan, Zhian Liang
Abstract
This paper studies the effects of jump risk in returns on the hedge fund managerâs optimal risk taking under high-water mark contract. The results show that the fund managerâs optimal risk taking under jump-diffusion risk is not a simple combination of that under pure-jump risk and pure-diffusion risk. The increase in jump intensity and jump size discourages the fund managerâs risk choice.
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Investigating Solutions for the Development of a Green Bond Market: Evidence from Analytic Hierarchy Process
Publication date: Available online 12 February 2020
Source: Finance Research Letters
Author(s): Chuc Anh Tu, Ehsan Rasoulinezhad, Tapan Sarker
Abstract
Green bonds are an important financial tool for funding environmental projects through a low-carbon financing approach. This study aims to investigate various solutions for the development of the green bond market in Vietnam, which the country is currently trying to establish as an effective investment channel to finance low-carbon projects. The study's major results revealed that the presence of an efficient legal framework for green bond operations, monetary policies of the State Bank of Vietnam, and the official interest rate of green bonds are important accelerators the country can utilize to strengthen its green bond market.
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MoneyScience: MoneyScience's post: Call for Abstracts - Conference on Complex Systems, 19-23rd October, 2020
Call for Abstracts - Conference on Complex Systems, 19-23rd October, 2020 https://t.co/eO7bm4kdaA pic.twitter.com/RnI9N4z0KH â moneyscience (@moneyscience)â¦
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moneyscience on Twitter
Abierto hasta el 15 de mayo el plazo para el envÃo de colaboraciones a uno de los eventos de referencia en el estudio de los sistemas complejos.â¦
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The Hodge Conjecture
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SEC Announces Nancy Sumption as Senior Advisor to the Chairman for Cybersecurity Policy
The Securities and Exchange Commission today announced that Nancy Sumption will serve as Chairman Jay Clayton's Senior Advisor for Cybersecurity Policy. In this role, Ms. Sumption will coordinate efforts across the agency to address cybersecurity policyâ¦
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Quantpedia Premium Update – 14th February 2020
Two new strategies have been added.
Three new related research papers have been included into existing strategy reviews. And two short free blog posts have been published during last few weeks.
The post Quantpedia Premium Update – 14th February 2020 appeared first on QuantPedia.
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Obscure Math
Hello all.
I am a sophomore college student majoring in CS and math, hoping to move into a quant role after college. I am fairly well versed in AI, Kalman filters, basic estimators, etc. I was wondering, what are the types of math that are either fun to learn or extremely useful as a quant or in algorithmic trading that courses really don't teach you about [Cointegration, unit roots, transition and observer matrices, etc]? This could also include stuff that is only really taught in obscure senior level math classes. I have taken up to calc 3 but am enthralled by maths and things like estimators and am always looking for new ways to get ahead of the curve.
Thank you for taking the time to read this post.
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How much SQL is needed?
Hi,
Iâm applying for an internship in Quantitative Analysis and it says that I need to have knowledge of SQL in order to get it. So Iâve been teaching myself sql for that past few days. Iâve been able to teach myself basic stuff like using SELECT, WHERE and expressions so far. But I was wondering if typically in quantitative analysis, there was a set amount of things I should know?
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Opinion on Temple University's MS quantitative finance and risk management program?
I have been admitted to the program, I am international student. Could any of you share your insights to the program?
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Looking for a financial data provider for Asian and Oceanian share markets. Please help.
I am looking for a financial data provider that provides the following information for Asian (China, Korea, and Japan) and Oceanian share markets.
- historical price data (open, low, high, close prices)
- historical value measures such as PER, PBR and EV/EDITBA etc.
- historical financial measure such as debt ratio etc (if entire historical balance sheet, cash flow and income statement are provided, it would be very good).
I asked a similar question in the past, and all answers are very helpful. However, they are mostly for US markets.
I am a retail investor, so expensive providers such as Bloomberg terminal do not suit to me.
Thank you for the help in advance.
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Questions about opinion on the ARPM Marathon Certificate
Hi,
I am planning on enrolling in the ARPM Marathon course (https://www.arpm.co/marathon/program/at-a-glance/).
I am currently a software developer at a financial services firm and am interested in moving to a quantitative developer role.
From reading on here, I understand that saying "quant" is a broad term. My primary interests in the field are related to risk management, modelling, and macroeconomic and geopolitical (fundamental ?) impacts on financial markets. I'm not sure if I'm so much interested in Pricing (but could be wrong as I learn about it).
These are just my interests from an outside pov and don't claim to know more about any of the fields.
I wanted to know what the community's opinion is on the ARPM Marathon course. If you think it's worth it, or if there are other, better courses/certifications that might help me learn and make the transition to a Quantitative developer role?
I look forward to reading your answers and opinions!
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Backtesting with known trades / portfolio accounting?
I have a large list of historical trades and for each I have the security, open price, open date-time, close price, close date-time and the number of units bought/sold.
So, I have enough information to know the return for each trade and the return overall but I do not know the mark-to-market P&L for the portfolio over time, nor can I see easily at any give time what is in the portfolio. I basically need some sort of portfolio accounting solution.
There is no script that has generated the trades and so I cannot feed it into any backtesting software I'm familiar with.
Is there a ready-made solution for this, or am I going to have to build one?
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Hedging with options vs futures
Hey r/quant I just got a job at a small fund, one of our clients has been buying expensive put options with year long maturities and it crapped the shit out of his returns.
My team is tasked with figuring out a strategy to hedge the portfolio possibly using options/futures/gold/VIX whatever. Iâm new and Iâm not expected to contribute a lot but I still want to bring something to the table.
Iâm looking for resources that (preferably quantitatively) compare the performance of a hedge using option vs futures. I know how both work (math degree + CFA) but for the life of me I canât think past the basics. Looking for papers, reports, analysis to help me get started on my own analysis.
Any and all help greatly appreciated, thanks quants!
P.S. big bonus if the resource touches upon EURUSD/currency hedging too.
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Chaos theory
Can chaos theory be successfully used to forecast or benefit from option/future movements? Examples of proving for and against?
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Where can one find long-term forward rates based on FRAs and swaps in the eurozone interbank market?
This is a very specific question.
I am trying to test some models on the European bond market in this low rate environment. While it is easy to find the data for the US interbank market, I can't seem to find it for the eurozone. Spot yields are easy to find, but finding information on forward rates is impossible on the ECB and Eurostat websites. All I could find was forward rates for a maturity of less than a year.
I'm willing to compute the rates myself if anyone has information on swaps.
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The Top Automated Time Series Models in Python (AtsPy)
submitted by /u/OppositeMidnight
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Tradebot, Pioneer of High-Speed Trading, Struggles With Profit Slump
submitted by /u/_quanttrader_
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https://algorithmictrading.net
Has anyone used this company before, there old company was mystocktradingbiz.
There annual return since 2003 back tested is 42% which is pretty amazing. I would have some insights
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Am unable to verify the low-beta/low-vol anomaly
Hi so I'm trying to verify the low-vol/low-beta anomaly [https://en.wikipedia.org/wiki/Low-volatility_anomaly] with a L/S dollar neutral portfolio. For low-vol I constructed three different factors:
zscore(rank(-vol_3m))
zscore(rank(-vol_6m))
zscore(rank(-vol_12m))
and similarly for low-beta:
zscore(rank(-beta_3m))
zscore(rank(-beta_6m))
zscore(rank(-beta_12m))
Note that beta is benchmarked to SPY. I tested between 2002-2020 and found a negative sharpe ratio for all six. The returns are pretty abysmal. The inverse returns (high-beta/high-vol) were pretty reasonable, as one could imagine.
There's so much academic research on this anomaly but as far as I can tell, it was never done on a L/S dollar neutral portfolio, only long only.
What I was expected to find was a higher Sharpe ratio for the low-vol/low-beta portfolio and when levered up to the same beta or vol level, higher absolute returns. I have found nothing of the sort, and am a little disturbed.
Why isn't this working?
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Papers or processes on forecasting Vol?
E.g. how to come up with an estimate on vol for the next 10 years of a specific equity security.
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Jim Simons seems to be a mathematician as well
https://www.youtube.com/watch?v=K67JjKAn3wc
he is doing lecture on k theory.. pretty cool. was he accomplished at math before he got into finance?
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Learn from the Experts Ep 1: Full Algorithm Creation with Vedran
submitted by /u/_quanttrader_
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Can anyone explain this problem to me please?
An atomic absorption method for the determination of the amount of iron present in used jet engine oil was found, from pooling 30 triplicate analyses, to have a s = 2.4 mg Fe/mL. If s is a good estimate of sigma, calculate the 80% and 95% confidence interval of the result, 18.5 mg Fe/mL, if it was based on
(a) single analysis, and
(b) the mean of four analyses.
I know the equation for it which is, CI = xbar +- zs/SqrtN. What I am confused about is the z. Is z a constant?
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Literature on Securitized Products (Loans or Fixed Income)
Any well regarded literature on the study of Loan defaults, Bonds and impact on XVA/Swaps/Sec Product Tranching? BK model for example I have played around with. Or doubly stochastic processes.
Thanks!
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option modeling
Hi, this is kind of a beginner's question to mathematical finance and derivative pricing my apologies. When should one be using the Heston Model vs GARCH vs ARCH vs Binomial model when pricing options? Are stochastic vol. models better predictors of future option pricing or should closed models/lattice based models be used?
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Risk measurement
If we can derive risk measurement tools from black Scholes ( delta, gamma, etc) can we do the same for schotastic volatility models like the Heston model?
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Interesting Papers
Hi guys,
Just wanted to ask around as to what interesting papers your reading at the moment?
Currently Iâm reading this paper about how ETFâs are structured and there systematic risks. Paper
It can be as weird and random as youâd like, I just like learning new stuff.
Thanks.
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How do corporations come up with a volatility estimate when valuing their options granted in their financial reports?
Options can have vesting schedules up to 10 years and most papers on volatility I've seen focus on much shorter terms or are auto regressive and just provide an estimate for the next period not a single input usable over time.
I am studying CFA level 2 and option valuation in financial reporting analysis is a topic and they say one of the main inputs to determining the value of these options on the books is of course volatility.
So what are the standard methods for estimating volatility for these purposes?
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Portfolio Optimization: Re-balancing by buying and not selling?
submitted by /u/caxus1
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Quantitative Investment Strategies - Market Size
Hello everyone, I am making a quick market study about quantitative finance, and I am looking for statistics about quantitative investment strategies (QIS) compared to the global financial markets strategies.
For example:
- Number and type of companies involved in QIS, split Hedge funds / Banks / Trading houses
- How much AuM in QIS compared to total AuM, worldwide, per continent
- Growth of the QIS in term of AuM and surrounding services (data, software...).
I found information, but it's sometimes quite contradictory and doesn't make any sense.
For example, https://www.advratings.com/ gives the AuM of top 65 asset management firms (70tr+ USD) in the world, but it doesn't make any sense to look at private equity or property investment and compare it with AuM in QIS. Same for the top largest banks and their assets.
I also looked at investopedia: https://www.investopedia.com/articles/investing/060915/which-are-top-10-private-banks.asp But once again, not sure the 12tr+ USD AuM of those 10 biggest private banks is relevant to compare to AuM in QIS.
This report: https://www.risk.net/asset-management/6605026/quantitative-investment-strategies-special-report-2019 says that 480 Billion USD are invested in QIS by 14 majors banks, but what can I compare this to?
Thanks for your help.
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Mark out Analysis
Hello;
Could anyone share a little detailed idea of how to conduct markout analysis.
Mark out as I understand is, using different time frames to see if you would have held the position how much + or - pnl could be
But my question is, what position do you consider - the current netpos or its calculated from every execution ? (Think hft when positions change frequently)
And for price should one go with mid point ?
I am trying to do this for fx futures and not equity so any examples using that would help or for that matter any white paper or link
Thanks
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Papers on size of markets (by volume, traders, etc) and inefficiency?
Has there been any studies for when exactly a market becomes inefficient and you can take advantage of mispricing. I imagine it is somewhere between 3 and 7 billion but I'm curious if anyone has quantified at exactly what level of volume, or participants, a market becomes inefficient?
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An Open Letter To /r/quant
Hi all, mod here. So, I know a lot of folks here have PhDs and so on.
I've been reading Limits to Growth [1], among other things. The methodology and conclusions can be debated easily enough. However, the simple, irrefutable argument which underpins this line of thought is that we live on a finite planet. Therefore, limitless growth cannot continue forever. It's just a matter of time. And that time may in fact be quite short.
- Can any of you smart people see something I don't see, about this simple and irrefutable argument?
- Can anyone suggest how capitalism, rewarder of consumption and predicated on endless growth, can be re-engineered to reward conservation?
- Can anyone explain how capitalism is compatible with the long-term sustainability of our species and our planet?
If not, it's probably time to look for a new job.
Me, I'm looking for new mods here. Please PM or comment if interested.
Wikipedia says [1]:
"In 2016, a report published by the UK All-Party Parliamentary Group on Limits to Growth concluded that "there is unsettling evidence that society is still following the 'standard run' of the original study â in which overshoot leads to an eventual collapse of production and living standards". The report also points out that some issues not fully addressed in the original 1972 report, such as climate change, present additional challenges..."
Links:
- [1] https://en.wikipedia.org/wiki/The_Limits_to_Growth
- LTG critique, by Jorgen Randers, one of the authors of the original 1972 report (2012) - The Real Message of The Limits to Growth [PDF]
- LTG critique, by Brian Hayes (2012) - Computation and the Human Predicament [PDF]
- LTG online interactive model, by Brian Hayes - http://bit-player.org/extras/limits/ltg.html
- Javascript source of Brian Hayes' Limits to Growth interactive model: http://bit-player.org/extras/limits/ltg.js
"People are dying. Entire ecosystems are collapsing. We are in the beginning of a mass extinction, and all you can talk about is money and fairy tales of eternal economic growth." - Greta Thunberg (Sept 23, 2019 at the UN)
"If we donât have a planet, weâre not going to have a very good financial system." - James Gorman, CEO of Morgan Stanley
"What is underway is a transition to a low-carbon future. That direction is irreversible and the smart money has worked that out and can see globally the trillions of dollars of infrastructure required ... As I heard someone say recently, you canât do business on a dead planet." - Geoffrey Summerhayes, chair of the Sustainable Insurance Forum - full article here
See also:
- 33 banks commit to immediate action towards aligning with global climate goals
- Why Growth Can't Be Green
Edit: it's been more than 4 weeks and none of the 11k+ wizards here have solved the problem, although we've gained hundreds of subscribers since then. Take the hint. This cannot be solved, we all bought a lie, it's Santa Claus and the Tooth Fairy all over again. Your options are either: a) leave now; or b) be terminated. There are no other outcomes on the table. Other fields which need bright people, use computational skills, and will probably be needed after the transition to a sustainable economy include:
Don't agree? Go ahead and rebut the paper. Publish your work in a peer-reviewed academic journal and you may win a prize.
Edits by the Connect-the-Dots Dept:
- Bank of England boss Mark Carney says global finance is funding 4C temperature rise ... and that firms that ignore the climate crisis will go bankrupt
- Bank of Canada warns of stranded assets and an abrupt transition to clean economy - "It's about the transition risk, right, as everyone's portfolio and all their lending practices kind of converge on a different climate trend line..." - Stephen Poloz, governor, Bank of Canada
Thank you all for the thoughtful comments. Feel free to chime in if there's more. :)
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Making Your Portfolio About #Goals
By Aaron Filbeck, CFA, CAIA, CIPM, Associate Director, Content Development at CAIA Association Central Issue of the Paper If youâre a social media junkie, you have probably seen â#goalsâ in your timeline. In most cases, the hashtag is referring to an attractive couple or an aesthetically pleasing plate of food.Read More
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Fast mean-reversion asymptotics for large portfolios of stochastic volatility models. (arXiv:1811.08808v2 [math.PR] UPDATED)
We consider an SPDE description of a large portfolio limit model where the underlying asset prices evolve according to certain stochastic volatility models with default upon hitting a lower barrier. The asset prices and their volatilities are correlated via systemic Brownian motions, and the resulting SPDE is defined on the positive half-space with Dirichlet boundary conditions. We study the convergence of the loss from the system, a function of the total mass of a solution to this stochastic initial-boundary value problem under fast mean reversion of the volatility. We consider two cases. In the first case the volatility converges to a limiting distribution and the convergence of the system is in the sense of weak convergence. On the other hand, when only the mean reversion of the volatility goes to infinity we see a stronger form of convergence of the system to its limit. Our results show that in a fast mean-reverting volatility environment we can accurately estimate the distribution of the loss from a large portfolio by using an approximate constant volatility model which is easier to handle.
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Analysis of intra-day fluctuations in the Mexican financial market index. (arXiv:2002.05697v1 [q-fin.ST])
In this paper, a statistical analysis of high frequency fluctuations of the IPC, the Mexican Stock Market Index, is presented. A sample of tick-to-tick data covering the period from January 1999 to December 2002 was analyzed, as well as several other sets obtained using temporal aggregation. Our results indicates that the highest frequency is not useful to understand the Mexican market because almost two thirds of the information corresponds to inactivity. For the frequency where fluctuations start to be relevant, the IPC data does not follows any alpha-stable distribution, including the Gaussian, perhaps because of the presence of autocorrelations. For a long range of lower-frequencies, but still in the intra-day regime, fluctuations can be described as a truncated L'evy flight, while for frequencies above two-days, a Gaussian distribution yields the best fit. Thought these results are consistent with other previously reported for several markets, there are significant differences in the details of the corresponding descriptions.
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Are American options European after all?. (arXiv:2002.05571v1 [q-fin.MF])
We call a given American option representable if there exists a European claim which dominates the American payoff at any time and such that the values of the two options coincide in the continuation region of the American option. This concept has interesting implications from a probabilistic, analytic, financial, and numeric point of view. Relying on methods from Jourdain and Martini (2001, 2002), Chrsitensen (2014) and convex duality, we make a first step towards verifying representability of American options.
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Top of the Batch: Interviews and the Match. (arXiv:2002.05323v1 [econ.GN])
Most doctors in the NRMP are matched to one of their most-preferred internship programs. Since various surveys indicate similarities across doctors' preferences, this suggests a puzzle. How can nearly everyone get a position in a highly-desirable program when positions in each program are scarce? We provide one possible explanation for this puzzle. We show that the patterns observed in the NRMP data may be an artifact of the interview process that precedes the match. Our analysis highlights the importance of interactions occurring outside of a matching clearinghouse for resulting outcomes, and casts doubts on analysis of clearinghouses that take reported preferences at face value.
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Sharing of longevity basis risk in pension schemes with income-drawdown guarantees. (arXiv:2002.05232v1 [q-fin.RM])
This work studies a stochastic optimal control problem for a pension scheme which provides an income-drawdown policy to its members after their retirement. To manage the scheme efficiently, the manager and members agree to share the investment risk based on a pre-decided risk-sharing rule. The objective is to maximise both sides' utilities by controlling the manager's investment in risky assets and members' benefit withdrawals. We use stochastic affine class models to describe the force of mortality of the members' population and consider a longevity bond whose coupon payment is linked to a survival index. In our framework, we also investigate the longevity basis risk, which arises when the members' and the longevity bond's reference populations show different mortality behaviours. By applying the dynamic programming principle to solve the corresponding HJB equations, we derive optimal solutions for the single- and sub-population cases. Our numerical results show that by sharing the risk, both manager and members increase their utility. Moreover, even in the presence of longevity basis risk, we demonstrate that the longevity bond acts as an effective hedging instrument.
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Decreasing market value of variable renewables is a result of policy, not variability. (arXiv:2002.05209v1 [q-fin.GN])
Although recent studies have shown that electricity systems with shares of wind and solar above 80% can be affordable, economists have raised concerns about market integration. Correlated generation from variable renewable sources depresses market prices, which can cause wind and solar to cannibalize their own revenues and prevent them from covering their costs from the market. This cannibalization appears to set limits on the integration of wind and solar, and thus contradict studies that show that high shares are cost effective. Here we show from theory and with numerical examples how policies interact with prices, revenue and costs for renewable electricity systems. The decline in average revenue seen in some recent literature is due to an implicit policy assumption that technologies are forced into the system, whether it be with subsidies or quotas. If instead the driving policy is a carbon dioxide cap or tax, wind and solar shares can rise without cannibalising their own market revenue, even at penetrations of wind and solar above 80%. Policy is thus the primary factor driving lower market values; the variability of wind and solar is only a secondary factor that accelerates the decline if they are subsidised. The strong dependence of market value on the policy regime means that market value needs to be used with caution as a measure of market integration.
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How consumers perceive sustainable products
When tire producers started launching recycled tires, truck drivers were sceptical. They didn’t trust the quality and assumed that there had to be a trade-off with the durability and effectiveness of the tires. Similarly, when there is a flu epidemic or pipes are clogged, consumers systematically choose the non-sustainable hand disinfectants and drain openers. Consumers are afraid that the contents of green products don’t work effectively and that they are not as effective as regular products. Therefore, products for which functional effectiveness is an important characteristic might have a sustainability liability.
Consumers, however, respond in the opposite way to so-called âgentleâ products with sustainable contents, such as a body lotion. For such products, sustainability can be a quality indicator â seen as an asset by consumers.
In a recent study, we uncover new insight into when âsustainability liabilityâ and âsustainability assetâ effects occur. We investigated whether any type of green product attribute produces the sustainability liability effect (for strong products) and the sustainability asset effect (for gentle products).
This topic is highly relevant for business in a time where more and more companies launch different kinds of green products, such as bottles of recycled plastics, food products with only natural ingredients, recyclable packaging, and so on.
Our study took as point of departure the previously demonstrated sustainability liability for green products in strong product categories, where consumers value strength and effectiveness. But do necessarily all products have such a sustainability liability? Would we be equally concerned about quality trade-offs if the strength of the product was not an important characteristic, such as for baby shampoo? Furthermore, is the liability as strong in the case of green packaging as when the actual contents of the product are green?
We investigated these questions in a series of four experiments and a so-called meta-analysis, which smokes out the overall effect across all four studies. We chose two product categories â one gentle and one strong, and we distinguished between central green attributes (related to the product itself) and peripheral green attributes (related to the packaging).
In collaboration with the Norwegian consumer goods company Orkla, which is a corporate partner in our research project, we created two fictitious product series, âSERAâ and âAvenoâ, and produced physical bottles with body lotion (mild product category) and drain opener (strong product category). For each of the product categories, we made one version labelled â100% natural ingredientsâ and one version labelled â100% recycled materialsâ, in addition to the regular product.
Figure 1. The three labels used for the fictitious products: body lotions with natural ingredients, recycled materials, and regular product, respectively
Notes: Labels in Norwegian; reproduced from the original paper, which is published under a CC-BY- 4.0 international license
Our findings reveal an interesting pattern of considerable importance to companies that want to launch green products. For products in so-called strong categories â drain openers, hand disinfectants, and so on â we find a sustainability liability. Consumers systematically expect such green products to be less effective, and they are less attracted to these products. This holds both when the contents and the packaging (that is, both central and peripheral attributes) are green. In our field study, we even find that consumers use more drain opener when it is labelled âsustainableâ. This is behavioural evidence that consumers perceive green products as less effective, and it has the paradoxical effect that consumers overuse green products.
This means that even when a completely regular drain opener is placed in a recycled bottle, consumers associate it with ineffectiveness â even though its contents are identical as in the regular product!
For gentle product categories â such as body lotion and baby shampoo â our study reveals other, clear tendencies. Here, we find the opposite result â sustainability is an asset: Consumers systematically perceive the products to be better, and they are also more inclined to prefer them. However, this only holds when the contents are green â not when the green attribute is recycled packaging.
These results have important implications for the question of whether sustainability can be good business. As our results show, consumers perceive green products very differently depending on the product category to which they belong. Of course, this does not mean that companies should only launch green versions of products in gentle categories and avoid sustainability characteristics in strong product categories. On the contrary, the natural follow-up question is: How can companies convince their customers that green products in strong categories can also be trusted? Can they for instance use different kinds of ânudgesâ or other interventions that can make these products more attractive in the eyes of the consumer? Perhaps they even need to change their business models, as Michelin did for their recycled tires, for which truck drivers now pay per mile driven.
We are now in the process of investigating these questions in several follow-up studies â and this knowledge is needed for companies that aim to contribute to a sustainable and profitable future.
Notes:
- This blog post is based on When is Sustainability a Liability, and When Is It an Asset? Quality Inferences for Core and Peripheral Attributes, Journal of Business Ethics, 2020.
- The post expresses the views of its author(s), not the position of LSE Business Review or the London School of Economics.
- Featured image by Christin Hume on Unsplash
- When you leave a comment, youâre agreeing to our Comment Policy.
Sveinung Jørgensen is an associate professor at NHH Norwegian School of Economics. He works closely with companies to give research-based advice on how to align sustainability and profitability in innovative and circular business models. He is an active public speaker, serves on several boards and does extensive research on sustainable business. Together with Lars Jacob Tynes Pedersen he is the author of the Open Access, Palgrave book âRESTART Sustainable Business Model Innovationâ. Twitter: @ungJorgensen
Lars Jacob Tynes Pedersen is an associate professor and Head of the Centre for Ethics and Economics at NHH Norwegian School of Economics. He does research in two main areas: (1) the design and innovation of sustainable business models, and (2) lab and field experiments on economic decision making, with a particular emphasis on socially and/or environmentally behaviours. He is an active public speaker and strategic advisor to business organisations. Twitter: @LJTPedersen
Siv Skard is an associate professor at the department of strategy and management at NHH Norwegian School of Economics. She does research on topics related to green consumer behaviour, nudging, sponsorship and service innovation. She is an active public speaker and serves on corporate boards and as an advisor to business organisations. Twitter: @SivSkard
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IBOR Global Benchmark Transition Roadmap 2018
ISDA, AFME, ICMA, SIFMA and SIFMA AMG have today launched a roadmap that aggregates and summarizes existing information published by regulators and various public-/private-sector risk-free rate (RFR) working groups on the work conducted to date towards transitioning financial products and practices from certain interbank offered rates (IBORs) to the selected RFRs. The roadmap is designed to provide a single point of reference for those interested in understanding more about the motivation behind the initiative and some of the key challenges to be addressed.
Scope and Market Footprint
The roadmap covers LIBOR and certain other IBORs denominated in five currencies: euro, sterling, Swiss franc, US dollar and yen. Based on publicly available data, the roadmap notes that total outstanding notional exposure to the IBORs has been estimated at over $370 trillion. Derivatives, syndicated loans, securitizations, business and retail loans, floating-rate notes (FRNs) and deposits are all significantly exposed to LIBOR and other IBORs.
Next Steps: Global Industry Survey and Report
The roadmap is the first part of a comprehensive analysis of the issues and potential solutions related to transitioning from IBORs for a wide spectrum of financial instruments. The associations are also initiating a global survey of buy- and sell-side firms and infrastructure providers, which will feed into an in-depth report aimed at supporting interest rate benchmark transition planning efforts.
Please click on the PDF below to read the full roadmap.
The post IBOR Global Benchmark Transition Roadmap 2018 appeared first on International Swaps and Derivatives Association.
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