Financial Managment
Shaker.ahmed@uwasa.fi
Lecture 2 – Trading in financial markets
Forwards:
- Agreement to buy or sell an asset for a certain price at a certain time
Futures:
- Forward contract is traded over the counter, futures are traded on exchange,
standardized, formalized
Swaps:
- A swap is an agreement to exchange cash flows at specified future times according to
certain specified rules
Options:
- A call (put) option is an option to buy (sell) a certain asset buy a certain date for a
certain price (strike price)
Exotics:
- Asian option (ST is the mean of S over lifetime), Barrier option (max(ST-X,0) if
St>barrier), Basket option (ST is the weighted mean of underlying assets over
lifetime), Binary options (‘yes or no’, if ST > X, payoff that is predetermined),
Compound options (underlying asset is an option) , Lookback options (ST is max(St))
Problem 5.34:
ST < 25:0
40 > ST >25 : 170x(ST-25)
ST > 40 : 2, 255
0-25 rak, 25-40 upp, 40> rak?
Bull spread on oil.
Types of traders:
- Hedgers: purpose defines if it is a hedge?
- Arbitrageurs
- Speculators
Instruments have become very complex
AI is developing very fast
Same instruments are used:
- Satisfy clients
- Hedge bank’s risk
- Profit from arbitrage
- Speculate
Controlling activities of traders and setting risk is not easy
Exchange traded – standard contracts, standard rules, Central Clearing House (no credit risk)
Over the counter (two people) – tailored contracts, some credit risk, trad directly with each
other
Variation margin:
- Margin reflecting change in value of a position
Initial margin:
- Additional protection
- Even if variation margin has been posted, A may still lose
money if B defaults because:
– B is not up to date with margin payments at time of default
– A may be subject to bid-offer spreads when it replaces transactions
Cover adverse movements:
- Can be cash or securities
- Margins have a cost of capital and a return
- May be a haircut, market value of securities is reduced by a certain percentage to
determine their value for collateral proposes
Traders borrow, margin is the collateral:
- Small margin for buy
- Large margin for short sell
Lessons from credit crisis:
2001-09-11 American dream of buying a house
Mortgage lenders relaxed their lending standards for subprime mortgages
Very low interest rates, the demand for real estate increased and prices rose
Mortgages were packages in financial products and securitized
Institutional investors invested in these AAA rated assets
2007 the bubble burst -> asset-backed securities began to be viewed as risky
What went wrong:
Regulatory arbitrage, if mortgage loans are not kept on the balance sheet then capital required
is considerably less
Compensation for traders sis nor create the right incentives
Do not rely on ratings? Forecasting models, complex instruments, general opinion
Securitization creates systematic risk
Lecture 3 – Volatility, correlation and valuation
If Si is the value of a variable on day I, the volatility is the standard deviation of the daily
return
Daily return = ln(Si/Si-1) = ui, using geometric return
Else: r = (Si – Si-1)/Si
Business days = 252
Volatility can be calculated or estimated.
Unconditional volatility does not care about time series.
Conditional volatility cares about time series, it´s time-varying.
Daily rate changes ar not normally distributes
Small and large changes are more likely
 Excess kurtosis
P(v<x) = Kx^-alpha
This seems to fit the behavior of the returns on many market variables better than the normal
distribution, when x is large
Lecture 4 - correlations
Corr(V1,V2) = (E(V1,V2) – E(V1)E(V2))/Std(V1)Std(V2)
Used to asses risk exposure, use correlation or covariance
Based on the formula, there might not be a correlation, however there actually is.
Corr = Cov/Sqrt(varx*vary)
Under assumption that E(x), E(y) = 0
Do you use the same lambda for x and y, we do for the models.
The constant omega is not the same (the risk is not the same).
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The credit default correlation between two assets is a measure of their tendency to
default at about the same time
Default correlation is important in risk management when analyzing the benefits of
diversification
It is also important in the valuation of credit derivatives
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Testing the effect on the value of a portfolio of different assumptions concerning asset
prices and their volatilities.
-
The time period chosen depends on the liquidity of the instruments.
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Make assumptions for stock prices, commodity prices, exchange rates, interest rates
and use probabilities
-
value different types of financial instruments, including derivatives
-
Evaluate behavior of asset prices and generate VaR and stressed VaR
Lecture 6 – Credit risk
Different types of risk: Market risk, Credit risk & Liquidity risk, Sustainability risk &
operational risk. Try to build models to predict/estimate risk. Data driven.
Measure risk, default for loans and derivatives
Manage = keep appropriate amount of capital
- Regulatory capital
- Economical capital – to maximize the value of capital
Credit process:
Internal data collection and control – semi automated
External data collection and control – semi automated
Decision – manual
Data:
May default, for principal, for interest
Main role for rating agencies
Risk of default:
- Calculate PD = Prob. Of default
- Calculate RR = Recovery rate (1-RR= Loss given default)
- RR depend on the collaterals
Methods for estimating:
1. Historical data and scoring model
- Define ratings and calibrate model
- Create model
- Prepare data
EAD = expoture at default, Original / year = LGD (loss given default)
2. Ratings
- Provided by agencies
- Hazard rate is p o d over short period of time on no earlier default
- Valuing the bonds
- Ratings are not changing often -> long term change
- Transition risk = how you move between ratings
3. Bond pricis of asset swaps
4. CDS spread (credit default swap)
- Source of systemic risk
- Credit risk traded as market risk
5. Advanced models
- Merton´s model:
- Equity as an option of assets (call option)
- Default when option is not exercised
- Volatility of equity predicts probability of default
Credit Value at Risk:
Credit risk at a certain time not exceeding a confidence interval.
Varsicek´s model