Saturday, October 31, 2009

In the Big Apple

In the New York City this weekend. While I grew up in the Rochester area of New York State, I never been to the Big Apple (it's about a 6hr drive from the folks...) NYC city truly lives up to its reputation. It is the most densely populated city in the United States. People are everywhere and the buildings are built one after another with no space in between. The number of people walking around in the streets is amazing. You just have to wonder where they all came from and where they are going to.

I am staying in New Jersey, just off Manhattan Island. I take a bus under the Lincoln Tunnel and get dropped off at the Port Authority. From there you can walk around to many of the popular sites or take a subway ride to anywhere you like.

I spent my first day checking out the tourist areas around Times Square and the Empire State building. In the evening, I found a poker game in midtown Manhattan one block away from the Empire State building. I found the game on it is run by a Korean guy named Jay.

We played a bit shorthanded (5-6 players). The guys playing were all gamblers and very loose. I managed to win about $70 for about 4hrs of play and ended up being the only player to win, besides maybe house.

The house had a few strange rules for there Poker game. First they required anyone leaving to announce at least one hour ahead of time. Also, they had a rule which I wasn't to sure about. Basically it said "No tight players.." and they reminded me of this rule a number of times. They seem to be kidding, but I'm not sure. Well, I played my normal game and that is "tight is right!"

Bad Beat or Stacked Deck?: I was wondering about there 1st rule and being the suspicious guy that I am, I was looking out for funny business. The host of the game, Jay, proceeded to dump about $500 within the first 2 hours and he picked seat#1. Then in one hand he got all his money back. The hand seem very odd to me. Jay basically flopped a set and turned a boat. The odd part was that the two other players were also in it had huge hands. One guy had a nut flush and the other guy hit top trips.

This kind of setup does happen and it has happen to me, but generally not with 3 people in the hand. Two weeks ago I hit a a full house with someone hitting trips, but never with someone else also hitting a nut flush. I am thinking that maybe it was a stacked deck? Here is the hands and the board. Do you think it was a setup? or just bad luck for the two players?

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Sunday, October 25, 2009

A $1K month!

This month I just hit over $1000 in winnings. Its been 7 months since I did that and that was during my trip to Michigan where they forgot how I played. I have only had 2 other months where I was able to make over $1000 in one month. My average winnings per month, over the last 13 months is $718/month!

I decided to play Saturday night. Normally, if I make my $200/night goal on Friday night, I skip Saturday, but I just felt like playing. I am going to NYC this weekend, so I needed a little extra cash and I wont be playing this weekend. It is also expensive in the Big Apple, so some extra cash would be good.

Saturday night did not disappoint. I started taking down some small pots and slowly built up my stack. I only bought in for $150, thinking I didn't want to lose any more then that. My first big had was A♦A. I raise to $35 and everyone folds, except Nicole. The flop is J♥10 5. Nicole checks and I push all in for $120. Nicole thinks for a while and finally calls. She shows like 86. I look for a moment trying to see what I need to dodge and it's runner runner straight draw? Anyways, she does not hit anything and I take it down. Maybe she thought he had a flush draw?

I patiently wait for my next hand which comes about 1 hour later. I have pocket rockets again, AA! I haven't seen Aces in at least the last 2 sessions, so I knew I was over due. The table is really lose and I am in early position. Nicole calls a straddle for $5, so I raise to $40. German is right next to me and folds pocket 10s, almost with no hesitation. Mike, A.K.A. "Well Dressed," ponders his move. Mike has been gambling all night and looks like he wants to make a move. He finally pushes all-in $251 and says, "he wants to see Anthony gamble!.." Everyone else folds and I look at him, and say, "you really want to mess with me?" It's hard for me to gamble when I hold the nuts! If you are playing cards with me, then you are gambling... I call and show my Aces. Mike has AK suited... The board is all rags, 9 high, and I take down the $500 pot!

This all happens within about 3 hrs and now my stack is over $700. This is way better then average for me. I continue to play some and donk off a few chips. I finally leave a bit past midnight, cashing out $640.

Saturday, October 24, 2009

Along Comes the Good with the Bad.

It was another solid night of poker for me.  This time I finally hit some hands and hit some big pots.  Last week my biggest starting hand was pocket 99s.  This session I hit plenty of pocket pairs, but none of my big pairs made much money or held up, in the beginning.  I wasn't hitting any sets for 5 hours.  Then finally they came.  But along with the big hands, comes the bad beats....

My first set of 77 got me paid with a board of 107Q, the turn was another Queen and river a 3.  The other guy had AQ and just smooth called all my bets to the river, he was afraid that I had what I had and he was right.  To bad he just couldn't fold it..  

This put him on tilt and he wanted to make his money back from me.  I took another $200 from him when I had pocket QQ and he called me down to the river again.  It was a rag flop and he was on some kind of straight draw.  

Finally he cracked me when I hit a set of 99s.  The flop was 79J. Nicole bets $12 on the flop.  I re-raise to $25.  Mevlin and button guy smooth call.  The turn is 3.  Nicole goes all in for $53 so I just smooth call.  Melvin calls and the dealer pushes all-in for $211 more.  I put him on the straight, but have to call.  Melvin folds and the river is an 8, no paired board.  The button guy shows the nuts 10-8...

At that point I still had close to $500 in front of me.  I didn't realize it, but I had my stack up to over $700.  When the rush comes, sometimes you forget how you got there..  I continue to play some and win some of my losses back.  I finally leave cashing out $575 for the session.

Wednesday, October 21, 2009

Recipe for Disaster: The Formula That Killed Wall Street

A year ago, it was hardly unthinkable that a math wizard like David X. Li might someday earn a Nobel Prize. After all, financial economists—even Wall Street quants—have received the Nobel in economics before, and Li's work on measuring risk has had more impact, more quickly, than previous Nobel Prize-winning contributions to the field. Today, though, as dazed bankers, politicians, regulators, and investors survey the wreckage of the biggest financial meltdown since the Great Depression, Li is probably thankful he still has a job in finance at all. Not that his achievement should be dismissed. He took a notoriously tough nut—determining correlation, or how seemingly disparate events are related—and cracked it wide open with a simple and elegant mathematical formula, one that would become ubiquitous in finance worldwide.

For five years, Li's formula, known as a Gaussian copula function, looked like an unambiguously positive breakthrough, a piece of financial technology that allowed hugely complex risks to be modeled with more ease and accuracy than ever before. With his brilliant spark of mathematical legerdemain, Li made it possible for traders to sell vast quantities of new securities, expanding financial markets to unimaginable levels.

His method was adopted by everybody from bond investors and Wall Street banks to ratings agencies and regulators. And it became so deeply entrenched—and was making people so much money—that warnings about its limitations were largely ignored.

Then the model fell apart. Cracks started appearing early on, when financial markets began behaving in ways that users of Li's formula hadn't expected. The cracks became full-fledged canyons in 2008—when ruptures in the financial system's foundation swallowed up trillions of dollars and put the survival of the global banking system in serious peril.

David X. Li, it's safe to say, won't be getting that Nobel anytime soon. One result of the collapse has been the end of financial economics as something to be celebrated rather than feared. And Li's Gaussian copula formula will go down in history as instrumental in causing the unfathomable losses that brought the world financial system to its knees.

How could one formula pack such a devastating punch? The answer lies in the bond market, the multitrillion-dollar system that allows pension funds, insurance companies, and hedge funds to lend trillions of dollars to companies, countries, and home buyers.

A bond, of course, is just an IOU, a promise to pay back money with interest by certain dates. If a company—say, IBM—borrows money by issuing a bond, investors will look very closely over its accounts to make sure it has the wherewithal to repay them. The higher the perceived risk—and there's always some risk—the higher the interest rate the bond must carry.

Bond investors are very comfortable with the concept of probability. If there's a 1 percent chance of default but they get an extra two percentage points in interest, they're ahead of the game overall—like a casino, which is happy to lose big sums every so often in return for profits most of the time.

Bond investors also invest in pools of hundreds or even thousands of mortgages. The potential sums involved are staggering: Americans now owe more than $11 trillion on their homes. But mortgage pools are messier than most bonds. There's no guaranteed interest rate, since the amount of money homeowners collectively pay back every month is a function of how many have refinanced and how many have defaulted. There's certainly no fixed maturity date: Money shows up in irregular chunks as people pay down their mortgages at unpredictable times—for instance, when they decide to sell their house. And most problematic, there's no easy way to assign a single probability to the chance of default.

Wall Street solved many of these problems through a process called tranching, which divides a pool and allows for the creation of safe bonds with a risk-free triple-A credit rating. Investors in the first tranche, or slice, are first in line to be paid off. Those next in line might get only a double-A credit rating on their tranche of bonds but will be able to charge a higher interest rate for bearing the slightly higher chance of default. And so on.

The reason that ratings agencies and investors felt so safe with the triple-A tranches was that they believed there was no way hundreds of homeowners would all default on their loans at the same time. One person might lose his job, another might fall ill. But those are individual calamities that don't affect the mortgage pool much as a whole: Everybody else is still making their payments on time.

But not all calamities are individual, and tranching still hadn't solved all the problems of mortgage-pool risk. Some things, like falling house prices, affect a large number of people at once. If home values in your neighborhood decline and you lose some of your equity, there's a good chance your neighbors will lose theirs as well. If, as a result, you default on your mortgage, there's a higher probability they will default, too. That's called correlation—the degree to which one variable moves in line with another—and measuring it is an important part of determining how risky mortgage bonds are.

Investors like risk, as long as they can price it. What they hate is uncertainty—not knowing how big the risk is. As a result, bond investors and mortgage lenders desperately want to be able to measure, model, and price correlation. Before quantitative models came along, the only time investors were comfortable putting their money in mortgage pools was when there was no risk whatsoever—in other words, when the bonds were guaranteed implicitly by the federal government through Fannie Mae or Freddie Mac.

Yet during the '90s, as global markets expanded, there were trillions of new dollars waiting to be put to use lending to borrowers around the world—not just mortgage seekers but also corporations and car buyers and anybody running a balance on their credit card—if only investors could put a number on the correlations between them. The problem is excruciatingly hard, especially when you're talking about thousands of moving parts. Whoever solved it would earn the eternal gratitude of Wall Street and quite possibly the attention of the Nobel committee as well.

To understand the mathematics of correlation better, consider something simple, like a kid in an elementary school: Let's call her Alice. The probability that her parents will get divorced this year is about 5 percent, the risk of her getting head lice is about 5 percent, the chance of her seeing a teacher slip on a banana peel is about 5 percent, and the likelihood of her winning the class spelling bee is about 5 percent. If investors were trading securities based on the chances of those things happening only to Alice, they would all trade at more or less the same price.

But something important happens when we start looking at two kids rather than one—not just Alice but also the girl she sits next to, Britney. If Britney's parents get divorced, what are the chances that Alice's parents will get divorced, too? Still about 5 percent: The correlation there is close to zero. But if Britney gets head lice, the chance that Alice will get head lice is much higher, about 50 percent—which means the correlation is probably up in the 0.5 range. If Britney sees a teacher slip on a banana peel, what is the chance that Alice will see it, too? Very high indeed, since they sit next to each other: It could be as much as 95 percent, which means the correlation is close to 1. And if Britney wins the class spelling bee, the chance of Alice winning it is zero, which means the correlation is negative: -1.

If investors were trading securities based on the chances of these things happening to both Alice andBritney, the prices would be all over the place, because the correlations vary so much.

But it's a very inexact science. Just measuring those initial 5 percent probabilities involves collecting lots of disparate data points and subjecting them to all manner of statistical and error analysis. Trying to assess the conditional probabilities—the chance that Alice will get head lice if Britney gets head lice—is an order of magnitude harder, since those data points are much rarer. As a result of the scarcity of historical data, the errors there are likely to be much greater.

In the world of mortgages, it's harder still. What is the chance that any given home will decline in value? You can look at the past history of housing prices to give you an idea, but surely the nation's macroeconomic situation also plays an important role. And what is the chance that if a home in one state falls in value, a similar home in another state will fall in value as well?

Enter Li, a star mathematician who grew up in rural China in the 1960s. He excelled in school and eventually got a master's degree in economics from Nankai University before leaving the country to get an MBA from Laval University in Quebec. That was followed by two more degrees: a master's in actuarial science and a PhD in statistics, both from Ontario's University of Waterloo. In 1997 he landed at Canadian Imperial Bank of Commerce, where his financial career began in earnest; he later moved to Barclays Capital and by 2004 was charged with rebuilding its quantitative analytics team.

Li's trajectory is typical of the quant era, which began in the mid-1980s. Academia could never compete with the enormous salaries that banks and hedge funds were offering. At the same time, legions of math and physics PhDs were required to create, price, and arbitrage Wall Street's ever more complex investment structures.

In 2000, while working at JPMorgan Chase, Li published a paper in The Journal of Fixed Incometitled "On Default Correlation: A Copula Function Approach." (In statistics, a copula is used to couple the behavior of two or more variables.) Using some relatively simple math—by Wall Street standards, anyway—Li came up with an ingenious way to model default correlation without even looking at historical default data. Instead, he used market data about the prices of instruments known as credit default swaps.

If you're an investor, you have a choice these days: You can either lend directly to borrowers or sell investors credit default swaps, insurance against those same borrowers defaulting. Either way, you get a regular income stream—interest payments or insurance payments—and either way, if the borrower defaults, you lose a lot of money. The returns on both strategies are nearly identical, but because an unlimited number of credit default swaps can be sold against each borrower, the supply of swaps isn't constrained the way the supply of bonds is, so the CDS market managed to grow extremely rapidly. Though credit default swaps were relatively new when Li's paper came out, they soon became a bigger and more liquid market than the bonds on which they were based.

When the price of a credit default swap goes up, that indicates that default risk has risen. Li's breakthrough was that instead of waiting to assemble enough historical data about actual defaults, which are rare in the real world, he used historical prices from the CDS market. It's hard to build a historical model to predict Alice's or Britney's behavior, but anybody could see whether the price of credit default swaps on Britney tended to move in the same direction as that on Alice. If it did, then there was a strong correlation between Alice's and Britney's default risks, as priced by the market. Li wrote a model that used price rather than real-world default data as a shortcut (making an implicit assumption that financial markets in general, and CDS markets in particular, can price default risk correctly).

It was a brilliant simplification of an intractable problem. And Li didn't just radically dumb down the difficulty of working out correlations; he decided not to even bother trying to map and calculate all the nearly infinite relationships between the various loans that made up a pool. What happens when the number of pool members increases or when you mix negative correlations with positive ones? Never mind all that, he said. The only thing that matters is the final correlation number—one clean, simple, all-sufficient figure that sums up everything.

The effect on the securitization market was electric. Armed with Li's formula, Wall Street's quants saw a new world of possibilities. And the first thing they did was start creating a huge number of brand-new triple-A securities. Using Li's copula approach meant that ratings agencies like Moody's—or anybody wanting to model the risk of a tranche—no longer needed to puzzle over the underlying securities. All they needed was that correlation number, and out would come a rating telling them how safe or risky the tranche was.

As a result, just about anything could be bundled and turned into a triple-A bond—corporate bonds, bank loans, mortgage-backed securities, whatever you liked. The consequent pools were often known as collateralized debt obligations, or CDOs. You could tranche that pool and create a triple-A security even if none of the components were themselves triple-A. You could even take lower-rated tranches of other CDOs, put them in a pool, and tranche them—an instrument known as a CDO-squared, which at that point was so far removed from any actual underlying bond or loan or mortgage that no one really had a clue what it included. But it didn't matter. All you needed was Li's copula function.

The CDS and CDO markets grew together, feeding on each other. At the end of 2001, there was $920 billion in credit default swaps outstanding. By the end of 2007, that number had skyrocketed to more than $62 trillion. The CDO market, which stood at $275 billion in 2000, grew to $4.7 trillion by 2006.

At the heart of it all was Li's formula. When you talk to market participants, they use words likebeautiful, simple, and, most commonly, tractable. It could be applied anywhere, for anything, and was quickly adopted not only by banks packaging new bonds but also by traders and hedge funds dreaming up complex trades between those bonds.

"The corporate CDO world relied almost exclusively on this copula-based correlation model," saysDarrell Duffie, a Stanford University finance professor who served on Moody's Academic Advisory Research Committee. The Gaussian copula soon became such a universally accepted part of the world's financial vocabulary that brokers started quoting prices for bond tranches based on their correlations. "Correlation trading has spread through the psyche of the financial markets like a highly infectious thought virus," wrote derivatives guru Janet Tavakoli in 2006.

The damage was foreseeable and, in fact, foreseen. In 1998, before Li had even invented his copula function, Paul Wilmott wrote that "the correlations between financial quantities are notoriously unstable." Wilmott, a quantitative-finance consultant and lecturer, argued that no theory should be built on such unpredictable parameters. And he wasn't alone. During the boom years, everybody could reel off reasons why the Gaussian copula function wasn't perfect. Li's approach made no allowance for unpredictability: It assumed that correlation was a constant rather than something mercurial. Investment banks would regularly phone Stanford's Duffie and ask him to come in and talk to them about exactly what Li's copula was. Every time, he would warn them that it was not suitable for use in risk management or valuation.

In hindsight, ignoring those warnings looks foolhardy. But at the time, it was easy. Banks dismissed them, partly because the managers empowered to apply the brakes didn't understand the arguments between various arms of the quant universe. Besides, they were making too much money to stop.

In finance, you can never reduce risk outright; you can only try to set up a market in which people who don't want risk sell it to those who do. But in the CDO market, people used the Gaussian copula model to convince themselves they didn't have any risk at all, when in fact they just didn't have any risk 99 percent of the time. The other 1 percent of the time they blew up. Those explosions may have been rare, but they could destroy all previous gains, and then some.

Li's copula function was used to price hundreds of billions of dollars' worth of CDOs filled with mortgages. And because the copula function used CDS prices to calculate correlation, it was forced to confine itself to looking at the period of time when those credit default swaps had been in existence: less than a decade, a period when house prices soared. Naturally, default correlations were very low in those years. But when the mortgage boom ended abruptly and home values started falling across the country, correlations soared.

Bankers securitizing mortgages knew that their models were highly sensitive to house-price appreciation. If it ever turned negative on a national scale, a lot of bonds that had been rated triple-A, or risk-free, by copula-powered computer models would blow up. But no one was willing to stop the creation of CDOs, and the big investment banks happily kept on building more, drawing their correlation data from a period when real estate only went up.

"Everyone was pinning their hopes on house prices continuing to rise," says Kai Gilkes of the credit research firm CreditSights, who spent 10 years working at ratings agencies. "When they stopped rising, pretty much everyone was caught on the wrong side, because the sensitivity to house prices was huge. And there was just no getting around it. Why didn't rating agencies build in some cushion for this sensitivity to a house-price-depreciation scenario? Because if they had, they would have never rated a single mortgage-backed CDO."

Bankers should have noted that very small changes in their underlying assumptions could result in very large changes in the correlation number. They also should have noticed that the results they were seeing were much less volatile than they should have been—which implied that the risk was being moved elsewhere. Where had the risk gone?

They didn't know, or didn't ask. One reason was that the outputs came from "black box" computer models and were hard to subject to a commonsense smell test. Another was that the quants, who should have been more aware of the copula's weaknesses, weren't the ones making the big asset-allocation decisions. Their managers, who made the actual calls, lacked the math skills to understand what the models were doing or how they worked. They could, however, understand something as simple as a single correlation number. That was the problem.

"The relationship between two assets can never be captured by a single scalar quantity," Wilmott says. For instance, consider the share prices of two sneaker manufacturers: When the market for sneakers is growing, both companies do well and the correlation between them is high. But when one company gets a lot of celebrity endorsements and starts stealing market share from the other, the stock prices diverge and the correlation between them turns negative. And when the nation morphs into a land of flip-flop-wearing couch potatoes, both companies decline and the correlation becomes positive again. It's impossible to sum up such a history in one correlation number, but CDOs were invariably sold on the premise that correlation was more of a constant than a variable.

No one knew all of this better than David X. Li: "Very few people understand the essence of the model," he told The Wall Street Journal way back in fall 2005.

"Li can't be blamed," says Gilkes of CreditSights. After all, he just invented the model. Instead, we should blame the bankers who misinterpreted it. And even then, the real danger was created not because any given trader adopted it but because every trader did. In financial markets, everybody doing the same thing is the classic recipe for a bubble and inevitable bust.

Nassim Nicholas Taleb, hedge fund manager and author of The Black Swan, is particularly harsh when it comes to the copula. "People got very excited about the Gaussian copula because of its mathematical elegance, but the thing never worked," he says. "Co-association between securities is not measurable using correlation," because past history can never prepare you for that one day when everything goes south. "Anything that relies on correlation is charlatanism."

Li has been notably absent from the current debate over the causes of the crash. In fact, he is no longer even in the US. Last year, he moved to Beijing to head up the risk-management department of China International Capital Corporation. In a recent conversation, he seemed reluctant to discuss his paper and said he couldn't talk without permission from the PR department. In response to a subsequent request, CICC's press office sent an email saying that Li was no longer doing the kind of work he did in his previous job and, therefore, would not be speaking to the media.

In the world of finance, too many quants see only the numbers before them and forget about the concrete reality the figures are supposed to represent. They think they can model just a few years' worth of data and come up with probabilities for things that may happen only once every 10,000 years. Then people invest on the basis of those probabilities, without stopping to wonder whether the numbers make any sense at all.

As Li himself said of his own model: "The most dangerous part is when people believe everything coming out of it."

Here's what killed your 401(k) David X. Li's Gaussian copula function as first published in 2000. Investors exploited it as a quick—and fatally flawed—way to assess risk. A shorter version appears on this month's cover of Wired.


Specifically, this is a joint default probability—the likelihood that any two members of the pool (A and B) will both default. It's what investors are looking for, and the rest of the formula provides the answer.

Survival times

The amount of time between now and when A and B can be expected to default. Li took the idea from a concept in actuarial science that charts what happens to someone's life expectancy when their spouse dies.


A dangerously precise concept, since it leaves no room for error. Clean equations help both quants and their managers forget that the real world contains a surprising amount of uncertainty, fuzziness, and precariousness.


This couples (hence the Latinate term copula) the individual probabilities associated with A and B to come up with a single number. Errors here massively increase the risk of the whole equation blowing up.

Distribution functions

The probabilities of how long A and B are likely to survive. Since these are not certainties, they can be dangerous: Small miscalculations may leave you facing much more risk than the formula indicates.


The all-powerful correlation parameter, which reduces correlation to a single constant—something that should be highly improbable, if not impossible. This is the magic number that made Li's copula function irresistible.

Felix Salmon ( writes the Market Movers financial blog at

Sunday, October 18, 2009

Coming Back After One Month Of Inaction

Got back to the table last night. I can't believe that I have not played in over one month, I had to check my records. In Malaysia, there is no gambling, Casinos or poker. It is a modern Muslim country with some traditional roots. Muslim woman must cover their heads and wear head scarfs and all Muslims still are not permitted to drink, gamble or eat meat. I was not sure what to do with myself, so I just worked and went to the beach on the weekends. I learn to Para Sail, that was a blast and got a nice foot massage! (getting a massage in Malaysia is probably the only titillating thing you can do there..)

The poker session was just how I left it. Lots of crazy action and loose play. I managed to stay out of most of the craziness. I started out slow and losing about $100 of my $200 buy-in. Then I more then doubled up after hitting a nut flush. A new guy at the table called my all-in bet on the river. German the dealer, never saw my hand but knew I had "the nuts..." So I showed him my nuts! How did he know?

Now, I was up to about $250 and and just took down small pots here and there. I don't think I had another showdown of the cards all night. Everyone just folded to my bets and by 4:40am I had $445 in front of me and I quit. Not a bad session for me not getting any cards.. I will have to wait for another session to get lucky...

Current Stats

Tuesday, October 13, 2009

On my way home..

In Moscow airport now!  They actually have free internet here, but other then that it's not to impressive after staying at Singapore Airport.  What is surprising is that Singapore is just a small island.  Russia is huge, with huge resources and there airport does not reflect the size of the country.  Anyways, it just a stop to refuel and drop off some passengers then onto Houston!

Sunday, October 11, 2009

Last week in Penang, Malaysia

This was my last week in Malaysia.  I decided to stay on Penang Island and enjoy the local sites.  I went back to Batu Ferringhi on Friday night to see the night market (Pasar Malam) .  It was about a half mile stretch of road side stands selling all sorts of goods.  If you want copied DVD movies, cheap knock-off watches, fake branded wallets and counterfeit branded purses, this was the place to be.  I didn't buy anything, just checked it out.

Saturday I slept in until well past noon.  I haven't slept in since 2 weeks ago.  I decided to head back to Batu Ferringhi beach and go Para Sailing one more time and try Horse back riding.  This time I took my camera up with and took some videos.  They said that it was only a 5% chance of getting wet, so I took the risk.  Here is a video clip of me Para Sailing..  Afterwards I went back to Gurney Plaza and saw the movie Gamer.

Sunday I decided to visit Komtar and the local shopping mall there, Prangin Mall.  Komtar is the largest building in Penang and was once the largest building in Asia at 60 stories.  I'll be in New York City in 2 weeks and will have compare the heights.   Here is a video of Komtar.

Sunday, October 4, 2009

Weekend at Bangkok, Thailand

Just got back from a mini vacation in Bangkok, Thailand. I am still working in Penang, Malaysia for work and going on 3 weeks here. I meet up with an old friend from Michigan, Jeff. He moved to Thailand about 3 years ago after going expatriate with his company, Valeo. There was a downsizing at Valeo and Jeff left to setup a shop in a mall in Phuket and now living as a foreigner. Jeff has spoken endlessly about Thailand and so I took the opportunity to check it out for myself.

I flew Air Asia. It is a discount airline in Asia and the flight cost about $137USD from Penang to Bangkok. The flight was only 1.5hr long. It was a good bargain compared to American standards, not sure how they can charge so low?

There is also another discount airline in Malaysia, Fire Flyz. Their rates are unbelievably low to many cities around Malaysia. They only do point to point travel, so maybe that explains why the costs are low. It is an argument against the typical centralized model of flying to hubs in the States.

I stayed at Sam's Lodge in downtown Bangkok after Jeff recommended it. It is considered a "Guest House," but pretty much runs the same as a hotel, but much cheaper. I paid ฿1200 (Baht) or about $35/night. It's a really good rate, considering the location. The accommodations are nothing special. The rooms are small and the bath room smaller, but it is clean and good enough for me.

Friday night we took a walk down Sukhumvit Road. It is the main road in Bangkok for tourist and was packed with cars and side walk street shops. The market place was incredible and goes on for blocks and blocks. We had a quick drink at some bar, with a bunch of other foreigners and freelancers. We then had dinner at Bully's Pub. The food was good and you could tell that only tourist eat there.

We decided to head to Nana Entertainment Plaza. It is one the the main areas of night life entertainment in Bangkok. Needless to say, it's a crazy place and it was everything I had envisioned it would be. I have heard all the stories and it is pretty much as the stories go. It was a long night and I got to bed around 5am.

Saturday we ate some lunch at The Old Dutch. I had the best Chicken Fried Rice and Shrimp ever! Then we did some shopping and I got to see the largest textile street shop in the world. At night we checked out Soi Cowboy and Patpong. Two more famous places in Bangkok for the adventuring types.