Incomplete Markets and Calibrations for Coherence with Hedged Portfolios. Thought of the Day 154.0

 

comnatSWD_2018_0252_FIN2.ENG.xhtml.SWD_2018_0252_FIN2_ENG_01027.jpg

In complete market models such as the Black-Scholes model, probability does not really matter: the “objective” evolution of the asset is only there to define the set of “impossible” events and serves to specify the class of equivalent measures. Thus, two statistical models P1 ∼ P2 with equivalent measures lead to the same option prices in a complete market setting.

This is not true anymore in incomplete markets: probabilities matter and model specification has to be taken seriously since it will affect hedging decisions. This situation is more realistic but also more challenging and calls for an integrated approach between option pricing methods and statistical modeling. In incomplete markets, not only does probability matter but attitudes to risk also matter: utility based methods explicitly incorporate these into the hedging problem via utility functions. While these methods are focused on hedging with the underlying asset, common practice is to use liquid call/put options to hedge exotic options. In incomplete markets, options are not redundant assets; therefore, if options are available as hedging instruments they can and should be used to improve hedging performance.

While the lack of liquidity in the options market prevents in practice from using dynamic hedges involving options, options are commonly used for static hedging: call options are frequently used for dealing with volatility or convexity exposures and for hedging barrier options.

What are the implications of hedging with options for the choice of a pricing rule? Consider a contingent claim H and assume that we have as hedging instruments a set of benchmark options with prices Ci, i = 1 . . . n and terminal payoffs Hi, i = 1 . . . n. A static hedge of H is a portfolio composed from the options Hi, i = 1 . . . n and the numeraire, in order to match as closely as possible the terminal payoff of H:

H = V0 + ∑i=1n xiHi + ∫0T φdS + ε —– (1)

where ε is an hedging error representing the nonhedgeable risk. Typically Hi are payoffs of call or put options and are not possible to replicate using the underlying so adding them to the hedge portfolio increases the span of hedgeable claims and reduces residual risk.

Consider a pricing rule Q. Assume that EQ[ε] = 0 (otherwise EQ[ε] can be added to V0). Then the claim H is valued under Q as:

e-rTEQ[H] = V0 ∑i=1n xe-rTEQ[Hi] —– (2)

since the stochastic integral term, being a Q-martingale, has zero expectation. On the other hand, the cost of setting up the hedging portfolio is:

V0 + ∑i=1n xCi —– (3)

So the value of the claim given by the pricing rule Q corresponds to the cost of the hedging portfolio if the model prices of the benchmark options Hi correspond to their market prices Ci:

∀i = 1, …, n

e-rTEQ[Hi] = Ci∗ —– (4)

This condition is called calibration, where a pricing rule verifies the calibration of the option prices Ci, i = 1, . . . , n. This condition is necessary to guarantee the coherence between model prices and the cost of hedging with portfolios and if the model is not calibrated then the model price for a claim H may have no relation with the effective cost of hedging it using the available options Hi. If a pricing rule Q is specified in an ad hoc way, the calibration conditions will not be verified, and thus one way to ensure them is to incorporate them as constraints in the choice of the pricing measure Q.

Advertisements

Self-Financing and Dynamically Hedged Portfolio – Robert Merton’s Option Pricing. Thought of the Day 153.0

hedge2

As an alternative to the riskless hedging approach, Robert Merton derived the option pricing equation via the construction of a self-financing and dynamically hedged portfolio containing the risky asset, option and riskless asset (in the form of money market account). Let QS(t) and QV(t) denote the number of units of asset and option in the portfolio, respectively, and MS(t) and MV(t) denote the currency value of QS(t) units of asset and QV(t) units of option, respectively. The self-financing portfolio is set up with zero initial net investment cost and no additional funds are added or withdrawn afterwards. The additional units acquired for one security in the portfolio is completely financed by the sale of another security in the same portfolio. The portfolio is said to be dynamic since its composition is allowed to change over time. For notational convenience, dropping the subscript t for the asset price process St, the option value process Vt and the standard Brownian process Zt. The portfolio value at time t can be expressed as

Π(t) = MS(t) + MV(t) + M(t) = QS(t)S + QV(t)V + M(t) —– (1)

where M(t) is the currency value of the riskless asset invested in a riskless money market account. Suppose the asset price process is governed by the stochastic differential equation (1) in here, we apply the Ito lemma to obtain the differential of the option value V as:

dV = ∂V/∂t dt + ∂V/∂S dS + σ2/2 S22V/∂S2 dt = (∂V/∂t + μS ∂V/∂S σ2/2 S22V/∂S2)dt + σS ∂V/∂S dZ —– (2)

If we formally write the stochastic dynamics of V as

dV/V = μV dt + σV dZ —– (3)

then μV and σV are given by

μV = (∂V/∂t + ρS ∂V/∂S + σ2/2 S22V/∂S2)/V —– (4)

and

σV = (σS ∂V/∂S)/V —– (5)

The instantaneous currency return dΠ(t) of the above portfolio is attributed to the differential price changes of asset and option and interest accrued, and the differential changes in the amount of asset, option and money market account held. The differential of Π(t) is computed as:

dΠ(t) = [QS(t) dS + QV(t) dV + rM(t) dt] + [S dQS(t) + V dQV(t) + dM(t)] —– (6)

where rM(t)dt gives the interest amount earned from the money market account over dt and dM(t) represents the change in the money market account held due to net currency gained/lost from the sale of the underlying asset and option in the portfolio. And if the portfolio is self-financing, the sum of the last three terms in the above equation is zero. The instantaneous portfolio return dΠ(t) can then be expressed as:

dΠ(t) = QS(t) dS + QV(t) dV + rM(t) dt = MS(t) dS/S + MV(t) dV/V +  rM(t) dt —– (7)

Eliminating M(t) between (1) and (7) and expressing dS/S and dV/V in terms of their stochastic dynamics, we obtain

dΠ(t) = [(μ − r)MS(t) + (μV − r)MV(t)]dt + [σMS(t) + σV MV(t)]dZ —– (8)

How can we make the above self-financing portfolio instantaneously riskless so that its return is non-stochastic? This can be achieved by choosing an appropriate proportion of asset and option according to

σMS(t) + σV MV(t) = σS QS(t) + σS ∂V/∂S QV(t) = 0

that is, the number of units of asset and option in the self-financing portfolio must be in the ratio

QS(t)/QV(t) = -∂V/∂S —– (9)

at all times. The above ratio is time dependent, so continuous readjustment of the portfolio is necessary. We now have a dynamic replicating portfolio that is riskless and requires zero initial net investment, so the non-stochastic portfolio return dΠ(t) must be zero.

(8) becomes

0 = [(μ − r)MS(t) + (μV − r)MV(t)]dt

substituting the ratio factor in the above equation, we get

(μ − r)S ∂V/∂S = (μV − r)V —– (10)

Now substituting μfrom (4) into the above equation, we get the black-Scholes equation for V,

∂V/∂t + σ2/2 S22V/∂S2 + rS ∂V/∂S – rV = 0

Suppose we take QV(t) = −1 in the above dynamically hedged self-financing portfolio, that is, the portfolio always shorts one unit of the option. By the ratio factor, the number of units of risky asset held is always kept at the level of ∂V/∂S units, which is changing continuously over time. To maintain a self-financing hedged portfolio that constantly keeps shorting one unit of the option, we need to have both the underlying asset and the riskfree asset (money market account) in the portfolio. The net cash flow resulting in the buying/selling of the risky asset in the dynamic procedure of maintaining ∂V/∂S units of the risky asset is siphoned to the money market account.

Derivative Pricing Theory: Call, Put Options and “Black, Scholes'” Hedged Portfolio.Thought of the Day 152.0

black-scholes-formula-excel-here-is-the-formula-for-the-black-model-for-pricing-call-and-put-option-contracts-black-scholes-formula-excel-spreadsheet

screenshot

Fischer Black and Myron Scholes revolutionized the pricing theory of options by showing how to hedge continuously the exposure on the short position of an option. Consider the writer of a call option on a risky asset. S/he is exposed to the risk of unlimited liability if the asset price rises above the strike price. To protect the writer’s short position in the call option, s/he should consider purchasing a certain amount of the underlying asset so that the loss in the short position in the call option is offset by the long position in the asset. In this way, the writer is adopting the hedging procedure. A hedged position combines an option with its underlying asset so as to achieve the goal that either the asset compensates the option against loss or otherwise. By adjusting the proportion of the underlying asset and option continuously in a portfolio, Black and Scholes demonstrated that investors can create a riskless hedging portfolio where the risk exposure associated with the stochastic asset price is eliminated. In an efficient market with no riskless arbitrage opportunity, a riskless portfolio must earn an expected rate of return equal to the riskless interest rate.

Black and Scholes made the following assumptions on the financial market.

  1. Trading takes place continuously in time.
  2. The riskless interest rate r is known and constant over time.
  3. The asset pays no dividend.
  4. There are no transaction costs in buying or selling the asset or the option, and no taxes.
  5. The assets are perfectly divisible.
  6. There are no penalties to short selling and the full use of proceeds is permitted.
  7. There are no riskless arbitrage opportunities.

The stochastic process of the asset price St is assumed to follow the geometric Brownian motion

dSt/St = μ dt + σ dZt —– (1)

where μ is the expected rate of return, σ is the volatility and Zt is the standard Brownian process. Both μ and σ are assumed to be constant. Consider a portfolio that involves short selling of one unit of a call option and long holding of Δt units of the underlying asset. The portfolio value Π (St, t) at time t is given by

Π = −c + Δt St —– (2)

where c = c(St, t) denotes the call price. Note that Δt changes with time t, reflecting the dynamic nature of hedging. Since c is a stochastic function of St, we apply the Ito lemma to compute its differential as follows:

dc = ∂c/∂t dt + ∂c/∂St dSt + σ2/2 St2 ∂2c/∂St2 dt

such that

-dc + Δt dS= (-∂c/∂t – σ2/2 St2 ∂2c/∂St2)dt + (Δ– ∂c/∂St)dSt

= [-∂c/∂t – σ2/2 St2 ∂2c/∂St+ (Δ– ∂c/∂St)μSt]dt + (Δ– ∂c/∂St)σSdZt

The cumulative financial gain on the portfolio at time t is given by

G(Π (St, t )) = ∫0t -dc + ∫0t Δu dSu

= ∫0t [-∂c/∂u – σ2/2 Su22c/∂Su2 + (Δ– ∂c/∂Su)μSu]du + ∫0t (Δ– ∂c/∂Su)σSdZ—– (3)

The stochastic component of the portfolio gain stems from the last term, ∫0t (Δ– ∂c/∂Su)σSdZu. Suppose we adopt the dynamic hedging strategy by choosing Δu = ∂c/∂Su at all times u < t, then the financial gain becomes deterministic at all times. By virtue of no arbitrage, the financial gain should be the same as the gain from investing on the risk free asset with dynamic position whose value equals -c + Su∂c/∂Su. The deterministic gain from this dynamic position of riskless asset is given by

Mt = ∫0tr(-c + Su∂c/∂Su)du —– (4)

By equating these two deterministic gains, G(Π (St, t)) and Mt, we have

-∂c/∂u – σ2/2 Su22c/∂Su2 = r(-c + Su∂c/∂Su), 0 < u < t

which is satisfied for any asset price S if c(S, t) satisfies the equation

∂c/∂t + σ2/2 S22c/∂S+ rS∂c/∂S – rc = 0 —– (5)

This parabolic partial differential equation is called the Black–Scholes equation. Strangely, the parameter μ, which is the expected rate of return of the asset, does not appear in the equation.

To complete the formulation of the option pricing model, let’s prescribe the auxiliary condition. The terminal payoff at time T of the call with strike price X is translated into the following terminal condition:

c(S, T ) = max(S − X, 0) —– (6)

for the differential equation.

Since both the equation and the auxiliary condition do not contain ρ, one concludes that the call price does not depend on the actual expected rate of return of the asset price. The option pricing model involves five parameters: S, T, X, r and σ. Except for the volatility σ, all others are directly observable parameters. The independence of the pricing model on μ is related to the concept of risk neutrality. In a risk neutral world, investors do not demand extra returns above the riskless interest rate for bearing risks. This is in contrast to usual risk averse investors who would demand extra returns above r for risks borne in their investment portfolios. Apparently, the option is priced as if the rates of return on the underlying asset and the option are both equal to the riskless interest rate. This risk neutral valuation approach is viable if the risks from holding the underlying asset and option are hedgeable.

The governing equation for a put option can be derived similarly and the same Black–Scholes equation is obtained. Let V (S, t) denote the price of a derivative security with dependence on S and t, it can be shown that V is governed by

∂V/∂t + σ2/2 S22V/∂S+ rS∂V/∂S – rV = 0 —– (7)

The price of a particular derivative security is obtained by solving the Black–Scholes equation subject to an appropriate set of auxiliary conditions that model the corresponding contractual specifications in the derivative security.

The original derivation of the governing partial differential equation by Black and Scholes focuses on the financial notion of riskless hedging but misses the precise analysis of the dynamic change in the value of the hedged portfolio. The inconsistencies in their derivation stem from the assumption of keeping the number of units of the underlying asset in the hedged portfolio to be instantaneously constant. They take the differential change of portfolio value Π to be

dΠ =−dc + Δt dSt,

which misses the effect arising from the differential change in Δt. The ability to construct a perfectly hedged portfolio relies on the assumption of continuous trading and continuous asset price path. It has been commonly agreed that the assumed Geometric Brownian process of the asset price may not truly reflect the actual behavior of the asset price process. The asset price may exhibit jumps upon the arrival of a sudden news in the financial market. The interest rate is widely recognized to be fluctuating over time in an irregular manner rather than being constant. For an option on a risky asset, the interest rate appears only in the discount factor so that the assumption of constant/deterministic interest rate is quite acceptable for a short-lived option. The Black–Scholes pricing approach assumes continuous hedging at all times. In the real world of trading with transaction costs, this would lead to infinite transaction costs in the hedging procedure.

Conjuncted: Long-Term Capital Management. Note Quote.

3022051-14415416419579177-Shock-Exchange_origin

From Lowenstein‘s

The real culprit in 1994 was leverage. If you aren’t in debt, you can’t go broke and can’t be made to sell, in which case “liquidity” is irrelevant. but, a leveraged firm may be forced to sell, lest fast accumulating losses put it out of business. Leverage always gives rise to this same brutal dynamic, and its dangers cannot be stressed too often…

One of LTCM‘s first trades involved the thirty-year Treasury bond, which are issued by the US Government to finance the federal budget. Some $170 billion of them trade everyday, and are considered the least risky investments in the world. but a funny thing happens to thirty-year Treasurys six months or so after they are issued: they are kept in safes and drawers for long-term keeps. with fewer left in the circulation, the bonds become harder to trade. Meanwhile, the Treasury issues new thirty-year bond, which has its day in the sun. On Wall Street, the older bond, which has about 29-and-a-half years left to mature, is known as off the run; while the shiny new one is on the run. Being less liquid, the older one is considered less desirable, and begins to trade at a slight discount. And as arbitrageurs would say, a spread opens.

LTCM with its trademark precision calculated that owning one bond and shorting another was twenty-fifth as risky as owning either outright. Thus, it reckoned, it would prudently leverage this long/short arbitrage twenty-five times. This multiplied its potential for profit, but also its potential for loss. In any case, borrow it did. It paid for the cheaper off the run bonds with money it had borrowed from a Wall Street bank, or from several banks. And the other bonds, the ones it sold short, it obtained through a loan, as well. Actually, the transaction was more involved, though it was among the simplest in LTCM’s repertoire. No sooner than LTCM buy off the run bonds than it loaned them to some other Wall street firm, which then wired cash to LTCM as collateral. Then LTCM turned around and used this cash as a collateral on the bonds it borrowed. On Wall street, such short-term, collateralized loans are known as “repo financing”. The beauty of the trade was that LTCM’s cash transactions were in perfect balance. The money that LTCM spent going long matched the money that it collected going short. The collateral it paid equalled the collateral it collected. In other words, LTCM pulled off the entire transaction without using a single dime of its own cash. Maintaining the position wasn’t completely cost free, however. Though, a simple trade, it actually entailed four different payment streams. LTCM collected interest on the collateral it paid out and paid interest at a slightly higher-rate on the collateral it took in. It made some of this deficit back because of the difference in the initial margin, or the slightly higher coupon on the bond it owned as compared to the bond it shorted. This, overall cost a few basis points to LTCM each month.

Algorithmic Trading. Thought of the Day 151.0

HFT order routing

One of the first algorithmic trading strategies consisted of using a volume-weighted average price, as the price at which orders would be executed. The VWAP introduced by Berkowitz et al. can be calculated as the dollar amount traded for every transaction (price times shares traded) divided by the total shares traded for a given period. If the price of a buy order is lower than the VWAP, the trade is executed; if the price is higher, then the trade is not executed. Participants wishing to lower the market impact of their trades stress the importance of market volume. Market volume impact can be measured through comparing the execution price of an order to a benchmark. The VWAP benchmark is the sum of every transaction price paid, weighted by its volume. VWAP strategies allow the order to dilute the impact of orders through the day. Most institutional trading occurs in filling orders that exceed the daily volume. When large numbers of shares must be traded, liquidity concerns can affect price goals. For this reason, some firms offer multiday VWAP strategies to respond to customers’ requests. In order to further reduce the market impact of large orders, customers can specify their own volume participation by limiting the volume of their orders to coincide with low expected volume days. Each order is sliced into several days’ orders and then sent to a VWAP engine for the corresponding days. VWAP strategies fall into three categories: sell order to a broker-dealer who guarantees VWAP; cross the order at a future date at VWAP; or trade the order with the goal of achieving a price of VWAP or better.

The second algorithmic trading strategy is the time-weighted average price (TWAP). TWAP allows traders to slice a trade over a certain period of time, thus an order can be cut into several equal parts and be traded throughout the time period specified by the order. TWAP is used for orders which are not dependent on volume. TWAP can overcome obstacles such as fulfilling orders in illiquid stocks with unpredictable volume. Conversely, high-volume traders can also use TWAP to execute their orders over a specific time by slicing the order into several parts so that the impact of the execution does not significantly distort the market.

Yet, another type of algorithmic trading strategy is the implementation shortfall or the arrival price. The implementation shortfall is defined as the difference in return between a theoretical portfolio and an implemented portfolio. When deciding to buy or sell stocks during portfolio construction, a portfolio manager looks at the prevailing prices (decision prices). However, several factors can cause execution prices to be different from decision prices. This results in returns that differ from the portfolio manager’s expectations. Implementation shortfall is measured as the difference between the dollar return of a paper portfolio (paper return) where all shares are assumed to transact at the prevailing market prices at the time of the investment decision and the actual dollar return of the portfolio (real portfolio return). The main advantage of the implementation shortfall-based algorithmic system is to manage transactions costs (most notably market impact and timing risk) over the specified trading horizon while adapting to changing market conditions and prices.

The participation algorithm or volume participation algorithm is used to trade up to the order quantity using a rate of execution that is in proportion to the actual volume trading in the market. It is ideal for trading large orders in liquid instruments where controlling market impact is a priority. The participation algorithm is similar to the VWAP except that a trader can set the volume to a constant percentage of total volume of a given order. This algorithm can represent a method of minimizing supply and demand imbalances (Kendall Kim – Electronic and Algorithmic Trading Technology).

Smart order routing (SOR) algorithms allow a single order to exist simultaneously in multiple markets. They are critical for algorithmic execution models. It is highly desirable for algorithmic systems to have the ability to connect different markets in a manner that permits trades to flow quickly and efficiently from market to market. Smart routing algorithms provide full integration of information among all the participants in the different markets where the trades are routed. SOR algorithms allow traders to place large blocks of shares in the order book without fear of sending out a signal to other market participants. The algorithm matches limit orders and executes them at the midpoint of the bid-ask price quoted in different exchanges.

Handbook of Trading Strategies for Navigating and Profiting From Currency, Bond, Stock Markets

“The Scam” – Debashis Basu and Sucheta Dalal – Was it the Beginning of the End?

harshad-mehta-pti

“India is a turnaround scrip in the world market.”

“Either you kill, or you get killed” 

— Harshad Mehta

“Though normally quite reasonable and courteous, there was one breed of brokers he truly detested. to him and other kids in the money markets, brokers were meant to be treated like loyal dogs.”

— Broker

The first two claims by Harshad Mehta could be said to form the central theme of the book, The Scam, while the third statement is testimony to the fact of how compartmentalization within the camaraderie proved efficacious to the broker-trader nexus getting nixed, albeit briefly. The authors Debasish Basu and Sucheta Dalal have put a rigorous investigation into unraveling the complexity of what in popular culture has come to be known as the first big securities scam in India in the early 90s. That was only the beginning, for securities scams, banking frauds and financial crimes have since become a recurrent feature, thanks to increasing mathematization and financialization of market practices, stark mismatches on regulatory scales of The Reserve Bank of India (RBI), Public Sector Banks and foreign banks, and stock-market-oriented economization. The last in particular has severed the myth that stock markets are speculative and had no truck with the banking system, by capitalizing and furthering the only link between the two, and that being banks providing loans against shares subject to high margins.  

The scam which took the country by storm in 1992 had a central figure in Harshad Mehta, though the book does a most amazing archaeology into unearthing other equally, if not more important figures that formed a collusive network of deceit and bilk. The almost spider-like weave, not anywhere near in comparison to a similar network that emanated from London and spread out from Tokyo and billed as the largest financial scandal of manipulating LIBOR, thanks to Thomas Hayes by the turn of the century, nevertheless magnified the crevices existing within the banking system and bridging it with the once-closed secretive and closed bond market. So, what exactly was the scam and why did it rock India’s economic boat, especially when the country was opening up to liberal policies and amalgamating itself with globalization? 

As Basu and Dalal say, simply put, the first traces of the scam were observed when the State Bank of India (SBI), Main Branch, Mumbai discovered that it was short by Rs. 574 crore in securities. In other words, the antiquated manually written books kept at the Office of Public Debt at the RBI showed that Rs. 1170.95 crore of an 11.5% of central government loan of 2010 maturity was standing against SBI’s name on the 29th February 1992 figure of Rs. 1744.95 crore in SBI’s books, a clear gap of Rs. 574 crore, with the discrepancy apparently held in Securities General Ledger (SGL). Of the Rs. 574 crore missing, Rs. 500 crore were transferred to Harshad Mehta’s account. Now, an SGL contains the details to support the general ledger control account. For instance, the subsidiary ledger for accounts receivable contains all the information on each of the credit sales to customers, each customer’s remittance, return of merchandise, discounts and so on. Now, SGLs were a prime culprit when it came to conceiving the illegalities that followed. SGLs were issued as substitutes for actual securities by a cleverly worked out machination. Bank Receipts (BRs) were invoked as replacement for SGLs, which on the one hand confirmed that the bank had sold the securities at the rates mentioned therein, while on the other prevented the SGLs from bouncing. BRs is a shrewd plot line whereby the bank could put a deal through, even if their Public Debt Office (PDO) was in the negative. Why was this circumvention clever was precisely because had the transactions taken place through SGLs, they would have simply bounced, and BRs acted as a convenient run-around, and also because BRs were unsupported by securities. In order to derive the most from BRs, a Ready Forward Deal (RFD) was introduced that prevented the securities from moving back and forth in actuality. Sucheta Dalal had already exposed the use of this instrument by Harshad Mehta way back in 1992 while writing for the Times of India. The RFD was essentially a secured short-term (generally 15 day) loan from open bank to another, where the banks would lend against Government securities. The borrowing bank sells the securities to the lending bank and buys them back at the end of the period of the loan, typically at a slightly higher price. Harshad Mehta roped in two relatively obscure and unknown little banks in Bank of Karad and Mumbai Mercantile Cooperative Bank (MMCB) to issue fake BRs, or BRs not backed by Government securities. It were these fake BRs that were eventually exchanged with other banks that paid Mehta unbeknownst of the fact that they were in fact dealing with fake BRs. 

By a cunning turn of reason, and not to rest till such payments were made to reflect on the stock market, Harshad Mehta began to artificially enhance share prices by going on a buying spree. To maximize profits on such investments, the broker, now the darling of the stock market and referred to as the Big Bull decided to sell off the shares and in the process retiring the BRs. Little did anyone know then, that the day shares were sold, the market would crash, and crash it did. Mehta’s maneuvers lent a feel-good factor to the stock market until the scam erupted, and when it did erupt, many banks were swindled to a massive loss of Rs. 4000 crore, for they held on to BRs that had no value attached to them. The one that took the most stinging loss was the State Bank of India and it was payback time. The mechanism by which the money was paid back cannot be understood unless one gets to the root of an RBI subsidiary, National Housing Bank (NHB). When the State Bank of India directed Harshad Mehta to produce either the securities or return the money, Mehta approached the NHB seeking help, for the thaw between the broker and RBI’s subsidiary had grown over the years, the discovery of which had appalled officials at the Reserve Bank. This only lends credibility to the broker-banker collusion, the likes of which only got murkier as the scam was getting unravelled. NHB did come to rescue Harshad Mehta by issuing a cheque in favor of ANZ Grindlays Bank. The deal again proved to be one-handed as NHB did not get securities in return from Harshad Mehta, and eventually the cheque found its way into Mehta’s ANZ account, which helped clear the dues due to the SBI. The most pertinent question here was why did RBI’s subsidiary act so collusively? This could only make sense, once one is in the clear that Harshad Mehta delivered considerable profits to the NHB by way of ready forward deals (RFDs). If this has been the flow chart of payment routes to SBI, the authors of The Scam point out to how the SBI once again debited Harshad Mehta’s account, which had by then exhausted its balance. This was done by releasing a massive overdraft of Rs. 707 crore, which is essentially an extension of a credit by a lending institution when the account gets exhausted. Then the incredulous happened! This overdraft was released against no security!, and the deal was acquiesced to since there was a widespread belief within the director-fold of the SBI that most of what was paid to the NHB would have come back to SBI subsidies from where SBI had got its money in the first place. 

The Scam is neatly divided into two books comprising 23 chapters, with the first part delineating the rise of Harshad Mehta as a broker superstar, The Big Bull. He is not the only character to be pilloried as the nexus meshed all the way from Mumbai (then Bombay) to Kolkata (then Calcutta) to Bengaluru (then Bangalore) to Delhi and Chennai (then Madras) with a host of jobbers, market makers, brokers and traders who were embezzling funds off the banks, colluded by the banks on overheating the stock market in a country that was only officially trying to jettison the tag of Nehruvian socialism. But, it wasn’t merely individuated, but the range of complicitous relations also grabbed governmental and private institutions and firms. Be it the Standard Chartered, or the Citibank, or monetizing the not-even in possession of assets bought; forward selling the transaction to make it appear cash-neutral; or lending money to the corporate sector as clean credit implying banks taking risks on the borrowers unapproved by the banks because it did not fall under the mainline corporate lending, rules and regulations of the RBI were flouted and breached with increasing alacrity and in clear violations of guidelines. But credit is definitely due to S Venkitaraman, the Governor of the RBI, who in his two-year at the helm of affairs exposed the scam, but was meted out a disturbing treatment at the hands of some of members of the Joint Parliamentary Committee. Harshad Mehta had grown increasingly confident of his means and mechanisms to siphon-off money using inter-bank transactions, and when he was finally apprehended, he was charged with 72 criminal offenses and more than 600 civil action suits were filed against him leading to his arrest by the CBI in the November of 1992. Banished from the stock market, he did make a comeback as a market guru before the Bombay High Court convicted him to prison. But, the seamster that he was projected to be, he wouldn’t rest without creating chaos and commotion, and one such bomb was dropped by him claiming to have paid the Congress Prime minister PV Narsimha Rao a hefty sum to knock him off the scandal. Harshad Mehta passed away from a cardiac arrest while in prison in Thane, but his legacy continued within the folds he had inspired and spread far and wide. 

684482-parekhketan-052118

Ketan Parekh forms a substantial character of Book 2 of The Scam. Often referred to as Midas in privy for his ability to turn whatever he touched into gold on Dalal Street by his financial trickery, he decided to take the unfinished project of Harshad Mehta to fruition. Known for his timid demeanor, Parekh from a brokers family and with his training as a Chartered Accountant, he was able to devise a trading ring that helped him rig stock prices keeping his vested interests at the forefront. He was a bull on the wild run, whose match was found in a bear cartel that hammered prices of K-10 stocks precipitating payment crisis. K-10 stocks were colloquially named for these driven in sets of 10, and the promotion of these was done through creating bellwethers and seeking support fro Foreign Institutional Investors (FIIs). India was already seven years old into the LPG regime, but still sailing the rough seas of economic transitioning into smooth sailing. This wasn’t the most conducive of timing to appropriate profits, but a prodigy that he was, his ingenuity lay in instrumentalizing the jacking up of shares prices to translate it into the much needed liquidity. this way, he was able to keep FIIs and promoters satisfied and multiply money on his own end. This, in financial jargon goes by the name circular trading, but his brilliance was epitomized by his timing of dumping devalued shares with institutions like the Life Insurance Corporation of India (LIC) and Unit Trust of India (UTI). But, what differentiated him from Harshad Mehta was his staying off public money or expropriating public institutions. such was his prowess that share markets would tend to catch cold when he sneezed and his modus operandi was invest into small companies through private placements, manipulate the markets to rig shares and sell them to devalue the same. But lady luck wouldn’t continue to shine on him as with the turn of the century, Parekh, who had invested heavily into information stocks was hit large by the collapse of the dotcom bubble. Add to that when NDA government headed by Atal Bihari Vajpayee presented the Union Budget in 2001, the Bombay Stock Exchange (BSE) Sensex crashed prompting the Government to dig deep into such a market reaction. SEBI’s (Securities and Exchange Board of India) investigation revealed the rogue nature of Ketan Parekh as a trader, who was charged with shaking the very foundations of Indian financial markets. Ketan Parekh has been banned from trading until 2017, but SEBI isn’t too comfortable with the fact that his proteges are carrying forward the master’s legacy. Though such allegations are yet to be put to rest. 

The legacy of Harshad Mehta and Ketan Parekh continue to haunt financial markets in the country to date, and were only signatures of what was to follow in the form of plaguing banking crisis, public sector banks are faced with. As Basu and Dalal write, “in money markets the first signs of rot began to appear in the mid-1980s. After more than a decade of so-called social banking, banks found themselves groaning under a load of investments they were forced to make to maintain the Statutory Liquidity Ratio. The investments were in low-interest bearing loans issued by the central and state governments that financed the government’s ever-increasing appetite for cash. Banks intended to hold these low-interest government bonds till maturity. But each time a new set of loans came with a slightly higher interest rate called the coupon rate, the market price of older securities fell, and thereafter banks began to book losses, which eroded their profitability,” the situation is a lot more grim today. RBI’s autonomy has come under increased threat, and the question that requires the most incision is to find a resolution to what one Citibank executive said, “RBI guidelines are just that, guidelines. Not the law of the land.” 

The Scam, as much as a personal element of deceit faced during the tumultuous times, is a brisk read, with some minor hurdles in the form of technicalities that intersperse the volume and tend to disrupt the plot lines. Such technical details are in the realm of share markets and unless negotiated well with either a prior knowledge, or hyperlinking tends to derail the speed, but in no should be considered as a book not worth looking at. As a matter of fact, the third edition with its fifth reprint is testimony to the fact that the book’s market is alive and ever-growing. One only wonders at the end of it as to where have all such journalists disappeared from this country. That Debashis Basu and Sucheta Dalal, partners in real life are indeed partners in crime if they aim at exposing financial crimes of such magnitudes for the multitude in this country who would otherwise be bereft of such understandings had it not been for them. 

Skeletal of the Presentation on AIIB and Blue Economy in Mumbai during the Peoples’ Convention on 22nd June 2018

Main features in AIIB Financing

  1. investments in regional members
  2. supports longer tenors and appropriate grace period
  3. mobilize funding through insurance, banks, funds and sovereign wealth (like the China Investment Corporation (CIC) in the case of China)
  4. funds on economic/financial considerations and on project benefits, eg. global climate, energy security, productivity improvement etc.

Public Sector:

  1. sovereign-backed financing (sovereign guarantee)
  2. loan/guarantee

Private Sector:

  1. non-sovereign-backed financing (private sector, State Owned Enterprises (SOEs), sub-sovereign and municipalities)
  2. loans and equity
  3. bonds, credit enhancement, funds etc.

—— portfolio is expected to grow steadily with increasing share of standalone projects from 27% in 2016 to 39% in 2017 and 42% in 2018 (projected)

—— share of non-sovereign-backed projects has increased from 1% in 2016 to 36% of portfolio in 2017. share of non-sovereign-backed projects is projected to account for about 30% in 2018

Untitled

Why would AIIB be interested in the Blue Economy?

  1. To appropriate (expropriate) the potential of hinterlands
  2. increasing industrialization
  3. increasing GDP
  4. increasing trade
  5. infrastructure development
  6. Energy and Minerals in order to bring about a changing landscape
  7. Container: regional collaboration and competition

AIIB wishes to change the landscape of infrastructure funding across its partner countries, laying emphasis on cross-country and cross-sectoral investments in the shipping sector — Yee Ean Pang, Director General, Investment Operations, AIIB.

He also opined that in the shipping sector there is a need for private players to step in, with 40-45 per cent of stake in partnership being offered to private players.

Untitled

Projects aligned with Sagarmala are being considered for financial assistance by the Ministry of Shipping under two main headings:

1. Budgetary Allocations from the Ministry of Shipping

    a. up to 50% of the project cost in the form of budgetary grant

    b. Projects having high social impact but low/no Internal Rate of Return (IRR) may be provided funding, in convergence with schemes of other central line ministries. IRR is a metric used in capital budgeting to estimate the profitability of potential investments. It is a discount rate that makes the net present value (NPV) of all cash flows from a particular project equal to zero. NPV is the difference between the present value of cash inflows and present value of cash outflows over a period of time. IRR is sometimes referred to as “economic rate of return” or “discounted cash flow rate of return.” The use of “internal” refers to the omission of external factors, such as the cost of capital or inflation, from the calculation.

2. Funding in the form of equity by Sagarmala Development Co. Ltd.

    a. SDCL to provide 49% equity funding to residual projects

    b. monitoring is to be jointly done by SDCL and implementing agency at the SPV level

    c.  project proponent to bear operation and maintenance costs of the project

     i. importantly, expenses incurred for project development to be treated as part of SDCL’s equity contribution

     ii. preferences to be given to projects where land is being contributed by the project proponent

What are the main financing issues?

  1. Role of MDBs and BDBs for promotion of shipping sector in the country
  2. provision of long-term low-cost loans to shipping companies for procurement of vessels
  3. PPPs (coastal employment zones, port connectivity projects), EPCs, ECBs (port expansion and new port development), FDI in Make in India 2.0 of which shipping is a major sector identified, and conventional bank financing for port modernization and port connectivity

the major constraining factors, however, are:

  1. uncertainty in the shipping sector, cyclical business nature
  2. immature financial markets