Complete post-module assignment for the course Investments. The attachments are NOT the assignment. These are sample in-class exercises/cases and the assignment will be based on the excel summary. The assignment questions will not be provided until I log in to Canvas and share the questions. As soon as I log in the timer starts and I have four hours to complete the entire assignment. The expert will contact me when they are ready and we will log in together. We can select any time to do this. It is the same format as the prior assignment. There will be three multi-part questions and I must submit one excel file with all of the answers with one tab for each question. I would like the same expert as last time.
PowerPoint Presentation MSGF TUTORIAL VICTOR YIP 1 Topics 1. Return 2. Risk 3. Correlation Coefficient 4. Portfolio 5. Expected Return & Variance 6. Sharpe Ratio 7. Compounding Frequency 2 Return • A set of data on return is a sample • Recording the return on a stock investment − Sample space ?? = −100%,+0.5%,+1%,+20%,… ,? 3 Return • Arithmetic Average Return • Geometric Average Return • Holding Period Return • Excess Return • Expected Return • Risk Premium 4 Arithmetic Average Return ? ? = ? ? ? ? ? = 1 ? ? ? ? ?????? ???? • Each observation is equally likely • Equal probability, ? ? = 1 ? • Without compounding/reinvestment • Simple mean is an unbiased estimator of the expected rate of return 5 Geometric Average Return 1 + ?1 1 + ?2 ⋯ 1 + ?? = 1 + ? ? • 1 + ? is the geometric average of the gross returns 1 + ?? • Actual performance of the portfolio over the past period • With compounding/reinvestment • Indication of investment performance 6 Return • Holding Period Return ??? = ?? − ??−1 ??−1 ??????? ???? + ????????? ??−1 ???????? ????? • Excess Return ?? = ??? − ?? • Expected Return ? ? = ? ? ? ? ? • Risk Premium ?? = ? ??? − ?? 7 Risk • Variance ?2 = ? ? ? ? ? − ? ? 2 • Standard deviation ? is the square root of ?2 • Using historical data ─ Population N ො?2 = 1 ? ? ? ? − ? 2 ─ Sample n ො?2 = 1 n− 1 ? ? ? − ҧ? 2 8 Risk • Covariance ??? ?? , ?? = ? ? ? ?? ? − ? ?? ?? ? − ? ?? • Using historical data ─ Population N ො??? = 1 ? ? ?? ? − ?? ?? ? − ?? ─ Sample n ො??? = 1 n− 1 ? ?? ? − ҧ?? ?? ? − ҧ?? 9 Correlation Coefficient • Correlation Coefficient ? measures the relationship of two sets of data ??? = ??? ??,?? ???????? ?????????? × ???????? ?????????? = ??? ???? • ? is positive if the values move in the same direction • ? is negative if the values move in the opposite direction • ? lies between −1 and 1, i.e., −1 ≤ ? ≤ 1 10 Portfolio • A portfolio is a collection of investment assets − Stocks − Bonds − Derivatives − Cash − etc. 11 Expected Return & Variance • The expected return of a portfolio ? ?? = ?=1 ? ??? ?? • The variance of a portfolio ?? 2 = ?=1 ? ?=1 ? ??????? ?? , ?? 12 Example ???? ???? ???? ?1 2??? ?1, ?1 ?1?2??? ?1, ?2 ???? ?1?2??? ?2, ?1 ?2 2??? ?2, ?2 13 1. ??? ?? , ?? = ??? ?? 2. ??? ?? , ?? = ??? ?? , ?? ?? 2 = ?1 2??? ?1 + ?2 2??? ?2 + 2?1?2 ??? ?1, ?2 ??????? Sharpe Ratio • Measure the attractiveness of an investment portfolio • Trade-off − Reward − Risk ?ℎ???? ????? = ???? ??????? ???????? ????????? ?? ????????? ?????? = ? ?? − ?? ?? 14 Compounding Frequency • Annually, Semi-annually, Quarterly, Monthly, Continuously • Effective Annual Rate (EAR): actual interest earned over a year • Annual Percentage Rate (APR): quoted interest rate 1 + ??? = 1 + ??? ? ? • Continuously Compounding Rate (???): for theoretical modeling lim ?→∞ 1 + ??? ? ? = ???? 15 Excel 16 Case: Chinese Copper Financing Deals GEORGE PANAYOTOV∗ JIALIN YU† 1 Introduction Ms Mulan Wong works for a hedge fund in Hong Kong. In early 2012, She starts to analyze investing opportunities from the interest rate spread between the Chinese Yuan (CNY) and the US Dollar (USD). In recent years, the interest rate spread is as wide as 5% per year (Fig- ure 1). After the Great Recession, the US Federal Reserve lowers the short-term dollar interest rate to almost zero. At the same time, the mainland China’s short-term interest rate is around 5% (higher if Mu- lan invests in wealth management products in China). Combined with the low exchange rate volatility, she thinks a strategy of borrowing USD and investing in the Yuan can be profitable. An obstacle for Mulan is that China has currency control that limits the foreign exchange flow into and out of the country. 2 CCFD Mulan noticed Chinese Copper Financing Deal (CCFD), a form of collat- eralized borrowing, can help her circumvent the currency control. Here is a simplified example of how a CCFD works. Figure 2 shows that a typical CCFD involves 4 parties: • Party A – Typically an offshore trading house • Party B – Typically an onshore trading house • Party C – Typically offshore subsidiary of B ∗Hong Kong University of Science and Technology. Email:
[email protected] †Hong Kong University of Science and Technology. Email:
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[email protected] • Party D – Onshore or offshore banks registered onshore serving B as a client A typical CCFD involves 4 steps: • Step 1) offshore trader A sells warrant of bonded copper (copper in China’s bonded warehouse that is exempted from VAT payment before customs declaration) or inbound copper (i.e. copper on ship in transit to bonded) to onshore party B at price X (i.e. B imports copper from A), and A is paid USD Letter of Credit (LC), issued by onshore bank D. • Step 2) onshore entity B sells and re-exports the copper by sending the warrant documentation (not the physical copper which stays in bonded warehouse ‘offshore’) to the offshore subsidiary C (B owns C), and C pays B USD or CNH cash (CNH = offshore CNY). Using the cash from C, B gets bank D to convert the USD or CNH into onshore CNY, and trader B can then use CNY as it sees fit. • Step 3) Offshore subsidiary C sells the warrant back to A (again, no move in physical copper which stays in bonded warehouse ‘off- shore’), and A pays C USD or CNH cash with a price of X minus $10-20/t, i.e. a discount to the price sold by A to B in Step 1. • Step 4) Repeat Step 1-Step 3 as many times as possible, during the period of LC (usually 6 months, with range of 3-12 months). Multiple LC can be obtained on the same copper warrant, with the limitation being the amount of time it takes to clear the paper- work. In this way, the total notional LCs issued over a particular tonne of bonded or inbound copper over the course of a year would be 3–10 times the value of the physical copper involved. In the last step, pledging the same copper collateral multiple times is a form of rehypothecation, which is popular in the financial markets of other countries, too. Rehypothecation occurs when banks or broker- dealers re-use the collateral posted by clients such as hedge funds to back the broker’s own trades and borrowing. In the UK, there is no limit on the amount of a clients assets that can be rehypothecated, except if the client has negotiated an agreement with their broker that includes 2 a limit or prohibition. In the US, rehypothecation is capped at 140% of a client’s debit balance.1 International Monetary Fund (IMF) estimates that rehypothecation accounted for half the activity in the shadow bank- ing system in 2007. Rehypothecation does not show up on conventional balance sheet accounting. Before the Lehman collapse, IMF calculated that US banks were receiving over $4 trillion worth of funding by rehy- pothecation, much of it sourced from the UK where there are no statu- tory limits governing the reuse of a client’s collateral. It is estimated that only $1 trillion of original collateral was being used, meaning that collateral was being rehypothecated several times over, with an esti- mated churn factor of 4 (see [1]). 3 Risks of CCFD Mulan identifies several risks of CCFD. First, the physical copper in- curs storage cost. The physical copper is often stored in the bonded warehouses in the free-trade zones of China. According to Reuters2, “At Shanghai’s Waigaoqiao port, a sprawling 10 square kilo- meter free-trade zone, thousands of tonnes of copper cathode plates sit in stacks turning green after years of exposure to the elements. "They don’t get shipped to end-users because they were bought for speculative reasons," said a warehouse manager at the port, who would only give his surname Zhu, standing in the port’s control room overlooking yards piled high with metal. The average time these copper stocks spend in bonded ware- houses have stretched on to nearly one year from just two months in the past. Some of this growing stockpile may never leave the port as fancy footwork from China’s traders keeps them several steps 1Assume a hedge fund customer has $500 in pledged securities with its prime broker bank, and borrows a debit balance of $200, resulting in net equity of $300. The prime broker bank can pledge (rehypothecate) up to $280 of the customer’s assets (140% × $200) for the broker bank’s own borrowing. 2Insight: China’s copper traders play yuan for profits 3 http://www.reuters.com/article/2012/03/30/us-china-copper-credit-insight-idUSBRE82R06Y20120330#OTdBc6kvRx5Piy4E.97 ahead of official attempts to stop copper being used as proxy cash to punt on other markets.” The physical copper may also come from the warehouses of metal ex- changes like the London Metal Exchange (LME), or off-exchange ware- houses which may be more cost effective compared to exchange ware- houses. Figure 4 shows Shanghai has the lion’s share of global copper inventory. This includes both the inventory at the Shanghai Future Ex- change and the bonded warehouses in Shanghai. Hong Kong Exchanges and Clearing acquires the London Metal Exchange in 2012, potentially boosting its competitive advantage in the region. Figure 5 shows the historical copper inventory at the LME warehouses. The copper price fluctuation is another risk. Figure 6 shows copper price tripled from USD 3,000 per ton in 2008 to USD 10,000 per ton in 2011, before declining to 5,000 per ton in 2015. Mulan plans to hedge this risk using futures. Commodity futures price can be volatile. Figure 7 shows an example of futures price backwardation. In backwardation, the futures price is downward sloping against maturity. If Mulan sells futures to hedge the copper price risk, the futures price is below the spot copper price. Sometimes, the futures price is under contango. Contango means the futures price is upward sloping against maturity, as shown in Figure 8. If Mulan sells futures to hedge the copper price risk and the market is under contango, Mulan will sell at a higher price than the spot copper price. Mulan also needs to plan for currency risk. Figure 9 shows that the Chinese Yuan experienced a large devaluation in the early 1990s. In the recent years, the Yuan has been slowly appreciating