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Nov
11
comment the law of comparative advantage and exchange rate
The author is half right, half wrong. Right in that PPP does not cause currencies to adjust for different purchasing power when currencies are pegged. Wrong, in that comparative advantage does not disappear just because currencies are pegged. Look at China and the US. A large driver of the persistent trade imbalance is the artificially weak yuan. It presents a persistent comparative manufacturing advantage to China.
Nov
8
comment Is the risk-reward ratio considered in Quantitative Finance?
My whole point is that quants should do what they do best, test out theories, develop models and by all means, work closely with traders and other risk takers to fully understand the repercussions when models eventually blow up or correlations break down. Strictly speaking an algorithmic trading strategy should not concern itself at all with risk-reward in the portfolio sense but it should generate trading signals based on the model intrinsic risk-reward characteristics. Whether signals are taken, how orders are sized, how pre-trade risk correlates with open positions should be left to traders
Nov
8
comment Is the risk-reward ratio considered in Quantitative Finance?
@sets, no question, I holeheartedly agree. But when you leave real risk and rewards in the hands of those with very little to no risk taking and managing experience due to an over-reliance on academic theories you end up with something like all current global central banks = blown out of any feasible proportion balance sheets and a gamble-like experiment that nobody understands nor risks to estimate the outcome of. Same stories goes with uncountable "quant desks" that blew up because of lack of risk management experience and over-reliance on models.Model risk is the most underpriced risk today
Nov
7
comment Is the risk-reward ratio considered in Quantitative Finance?
@SRXX, fair point which is why I qualified my comment in response to the question above in that it reflects just my opinion and not necessarily a universal view. It is just that I find the term "quant" in general quite overrated. I do not consider myself a quant because I lack a rigorous mathematical and/or statistical background.
Nov
7
comment Is the risk-reward ratio considered in Quantitative Finance?
@Shane, I guess I had a specific sub-group in mind when I said quants do not engage in risk/reward considerations. For me "quants" rarely should be tasked with risk-return considerations because most severely lack real trading and risk management experience. You can't say you fulfill your fiduciary duty to clients and at the same time put billions at risk off the back of theoretical concepts and historically tested ideas and strategies. Banks and hedge funds are generally very good at segregating who thinks about risk/reward and who performs mathematical/statistical analysis...
Nov
7
comment Is the risk-reward ratio considered in Quantitative Finance?
if this is "quant" to you then what is not? Portfolio selection and Modern Portfolio Theory (MPT) lies pretty much at the heart of any discretionary and on-fundamentals focused portfolio manager. I guess knowledge of MPT is most likely tested in one of the first interview questions of any junior starting out in the traditional buy-side industry.
Nov
6
comment Is the risk-reward ratio considered in Quantitative Finance?
@TomTucker, with traders I mean anyone who takes and manages risk, someone who signs responsible for profits and losses.
Nov
6
comment compute sharpe ratio for options?
@godzilla, please take this in a positive spirit, but I highly recommend you to familiarize yourself with basic finance concepts before you advance to option pricing and theory. The reason even today many new grads and desk juniors undergo "jungle bootcamp" at sell-side trading desks is because basics are so important. I honestly do not mean that in a derogatory way, it is just that your question and comments point to couple missing fundamentals in your arsenal.
Nov
6
comment How to properly take averages to reduce data in regression/panel data analysis
@Cindy88, Praise the Lord for bell-curve(s) is all that comes to mind (but then I focused on your profile not your question). Sorry, just could not help it. And back to the topic: I love to help but can you please provide more details. Similar to variance reduction techniques on the Monte Carlo side, much more details are needed what you exactly try to achieve in order to decide on the best algorithm optimization. Care to share the type of regression you try to run?
Nov
6
comment How to value VIX Option?
Do you understand exactly how the VIX index is derived?
Nov
6
comment Is the risk-reward ratio considered in Quantitative Finance?
Quants do not care about risk-reward, its not part of their job description. Strategists and traders do.
Nov
6
comment Black-Scholes: Why the focus on volatility?
@AndrewDabrowski, I am not sure I understand what you are trying to say. I suggest to look at the big picture. Single plain vanilla options represent views on implied volatility (for most part). If you think implied volatility should be lower than the market prices then you sell the option and vice versa. Simple as that. How you arrive at your own implied volatility estimate is limited only by your imagination.
Nov
6
comment Black-Scholes: Why the focus on volatility?
@rwolst, I did not make any claims about the distributional assumption of any of the pricing models. What I mean to say is that IV is generally the variable to which vanilla options are most sensitive to (assuming delta-hedged option positions mostly eliminate exposure to the underlying price). I tried to disagree with the notion that IV is "just" a "fudge factor" as implied by the OP.
Nov
6
comment Black-Scholes: Why the focus on volatility?
I believe solving PDEs is a more sophisticated and also the more popular approach at exotic trading desks.
Nov
6
comment Black-Scholes: Why the focus on volatility?
Implied volatility is the expected future return volatility of the underlying asset over the lifetime of the option. It is not a value that is calculated but it is in itself something that is bid and offered in the market as a function of each trader's view on future volatility. Participants express a view on implied volatility and trade it, paid for through the translated option price. Implied volatility can be modeled and forecast and there are various models that accomplish such.
Nov
5
comment Black-Scholes: Why the focus on volatility?
@AndrewDabrowski, no, there are many approaches, pick whatever suits your needs. I find the approach through the risk neutral probability measure very intuitive, thats all.
Nov
5
comment why does graphic of log differenced of renminbi look similar to hkd?
Most likely it is related to low dollar volatility during that time period which impacted both USDCNY and USDHKD rates.
Nov
5
comment Trouble arriving at Black-Scholes Formula
Bob, thanks for the LaTex edit
Nov
5
comment why does graphic of log differenced of renminbi look similar to hkd?
Incorrect, a) returns in USDCNY are on average much more volatile than returns in USDHKD as you can see from your own charts, b) you can also see that USDCNY reflects more negative than positive returns while that is not the case for USDHKD.
Nov
5
comment Black-Scholes: Why the focus on volatility?
Andrew Dabrowski, please take a look at this question, (so far) you are incorrect in most of your claims regarding the subject matter of option pricing: quant.stackexchange.com/questions/8247/…