Transcript Risk Management in Shipping - Laboratory for Maritime

Risk Management in Shipping
Modeling, Measuring, & Managing Freight Market Uncertainty
Presented at:
National Technical University of Athens
School of Naval Architecture & Marine Engineering
29 May 2003
Risk Management in Shipping
Presentation Outline
 Introduction


About this presentation
About FreightMetrics
 About Risk Management




Defining risk
The risk management process
Scope of risk management
Modern applications of risk management
 Measuring Market Risk

The traditional approach to market risk measurement

The Value-at-Risk (VaR) approach
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Risk Management in Shipping
Presentation Outline
 Measuring Market Risk in Shipping




Justification for risk management in shipping
Market risk measurement vs. market forecasting
Identifying the impact of freight market risk on fleet cash flow
Developing a framework for measuring freight market risk
 Measuring Market Risk in Shipping Using the Fr8Metrics TM Methodology
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

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Main methodological features
How does Fr8MetricsTM work?
Benefits of the Fr8MetricsTM methodology
Potential users and managerial applications
Software implementation
 Managing Freight Market Risk

Altering the risk profile using managerial decisions

Altering the risk profile using freight derivatives
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Risk Management in Shipping
Introduction
About This Presentation


Our objective

Shipping is a business activity exposed to a wide variety of risks.

In this presentation we are concerned with the measurement of one particular form of risk
– namely freight market risk, or the risk of loss arising from unexpected changes in freight
rates.
Our motivation

Risk management is a notion that exists in financial markets for decades, having
experienced significant technological and modeling advances over the years.

Shipping has proved rather slow in adopting modern risk management techniques and
best practices from other industries.

Our motivation is to present a modern framework for measuring freight market risk, using
the paradigm of other market-sensitive industries.
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Risk Management in Shipping
Introduction
About FreightMetrics


What FreightMetrics is…

A provider of consulting services and software solutions for measuring and managing
freight market risk.

Working closely with Shipping Banks, Shipowners, and Freight Traders, in order to
quantify their exposure to freight market risk in terms of cash-flow sensitivity.

Our approach lies in transferring best practices and modern methodologies from the area
of financial risk management to shipping.
What FreightMetrics is NOT…

Shipbroker.

Forecasting agency.
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Market news vendor.

Financial intermediary.
For more information about FreightMetrics, visit our website at www.freightmetrics.com
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Risk Management in Shipping
About Risk Management
Defining Risk

Definition of Risk


We define (financial) risk as the prospect of financial loss due to unforeseen changes in
underlying “risk factors”. These risk factors are the key drivers affecting portfolio value
and financial results. Such risk factors are equity prices, interest rates, exchange rates,
commodity prices, freight rates, etc.
Types of Risks

Business:
The risk of loss due to unforeseen changes in demand, technology,
competition, etc., affecting the fundamentals of a business activity.

Market:
The risk of loss arising from unexpected changes in market prices
or market rates.

Credit:
The risk of loss arising from the failure of a counterparty to make a
promised payment.

Operational:
The risk of loss arising from the failures of internal systems or the
people who operate in them.

Other types:
Legal, Liquidity, etc.
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Risk Management in Shipping
About Risk Management
The Risk Management Process

The Risk Management process

There is a wide misconception amongst practitioners, especially within the shipping industry,
who consider risk management as synonymous to hedging. This is an oversimplification and
does not reflect the true dimension of risk management.

In fact, risk management is a process that involves three separate steps:
1.
Risk Modeling: Before any attempt to take decisions on risk considerations, we must
2.
Risk Measurement: After identifying and modeling the underlying risk factors, we must
3.
Risk Management : Having identified and measured our risks, we are then able to take
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identify the underlying risk factors, understand their behavior, and try to model their
dynamics. This is the basic foundation on which the other phases of the risk management
cycle are built.
determine their significance and quantify their influence on portfolio value and financial
results.
informed decisions on whether to reduce our exposure or alter our risk profile based on our
risk preferences – hedging is one such alternative course of action.
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Risk Management in Shipping
About Risk Management
Scope of Risk Management

Risk Management ≠ Hedging

As already mentioned, risk management is not synonymous to hedging.
Hedging is just one alternative for the active management of risk.

Moreover, risk management does not necessarily imply risk reduction. In fact, the
objective of risk management is NOT to reduce risk, but – more importantly –
to quantify and control risk.

Most of the times, the objective is not to eliminate risk, but rather to alter our risk profile
according to the prevailing market conditions, our risk preferences, and potential
regulatory or contractual requirements.

Risks are embedded in any business activity. For a shipowner, the decision to invest in a
vessel signifies his belief that freight rates will go up, earning him a return on his
investment that is higher than the “risk-free” interest rate. However, there is no “free
lunch” in the economy; his decision to invest creates at the same time a natural exposure
to freight rates, accepting the risk that freight rates may in fact go down. Risks are simply
unavoidable in any profit-taking activity.
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Risk Management in Shipping
About Risk Management
Scope of Risk Management

Uncertainty vs. Variability 1
“Variability is a phenomenon in the physical world to be measured, analysed and where
appropriate explained. By contrast, uncertainty is an aspect of knowledge.”
Sir David Cox

Risk management is only useful for the mere fact that we cannot predict the future. There
are two components of our inability to be able to precisely predict what the future holds:
these are variability and uncertainty.

Variability is the effect of chance and is a function of the system. It is not reducible
through either study or further measurement, but may be reduced through changing the
physical system.

Uncertainty is the assessor’s lack of knowledge (level of ignorance) about the parameters
that characterize the physical system that is being modeled. It is sometimes reducible
through further study, or through consulting more experts.

Risk management can do very little to reduce variability (markets will continue to fluctuate
no matter how advanced risk management gets), but can be very effective in reducing
uncertainty for those involved in risk-taking decisions.
1 Adapted from the book Risk Analysis by David Vose, Chapter 2: “Quantitative Risk Analysis, Uncertainty and Variability”
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Risk Management in Shipping
About Risk Management
Modern Applications of Risk Management

Modern applications of Risk Management

Exposure measurement and reporting

Market risk (since early 90s)

Credit risk (since late 90s)

Operational risk (new area)

Economic capital estimation
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Allocation of capital
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Risk-based pricing
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Risk limits
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Risk-adjusted performance evaluation
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Risk Management in Shipping
About Risk Management
Modern Applications of Risk Management

Example: Risk-Adjusted Performance Evaluation
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Consider two traders who are evaluated on the basis of their realized profits at some
future date. Trader B ended up with higher profits compared to Trader A. Does this mean
he is more skilled than Trader A? Does he deserve a higher bonus? What about the risk
incurred by each trader through their trading strategy?
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Risk Management in Shipping
Measuring Market Risk
The Traditional Approach to Risk Measurement
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
The Mean-Variance framework

Under the Mean-Variance framework, we model financial risk in terms of the mean and
variance (or standard deviation, the square root of variance) of the Profit/Loss (P&L) or
the returns of our portfolio.

The Mean-Variance framework often makes the assumption that returns obey a normal
distribution (strictly speaking, the mean-variance framework does not require normality,
but it is easier to understand its statistics).
Portfolio Theory

The origin of portfolio theory can be traced back to the work of Markowitz (1952) which
earned him the Nobel prize.

Portfolio theory starts with the premise that investors choose between portfolios on the
basis of maximizing expected return for any given portfolio standard deviation or
minimizing standard deviation for any given expected return.

One of the key insights of portfolio theory is that the risk of any individual asset is
measured by the extent to which that asset contributes to overall portfolio risk which
depends on the correlation of its return with the returns to the other assets in the portfolio
(a result known as diversification effect).

Portfolio theory typically makes the assumption of normally distributed returns.
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Risk Management in Shipping
Measuring Market Risk
The Value-at-Risk (VaR) Approach

The origin and development of VaR

In the late 70s and 80s, a number of major financial institutions started working on internal
models to measure and aggregate risks across the institution as a whole.

The best known of these models is the RiskMetrics model developed by JP Morgan.
According to industry legend, this model is said to have originated when the chairman of
JP Morgan, Dennis Weatherstone, asked his staff to give him a daily one-page report –
the famous “4:15 report” – indicating risk and potential losses over the next 24 hours,
across the bank’s entire trading portfolio.

The report was ready by around 1990 and the measure used was Value-at-Risk (VaR), or
the maximum likely loss over the next trading day. VaR was estimated from a system
based on standard portfolio theory, using estimates of the standard deviations and
correlations between the returns of different traded instruments.
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In early 1994, JP Morgan set up the RiskMetrics unit to make its data and basic
methodology available to outside parties. This bold move attracted a lot of attention and
raised awareness of VaR techniques and risk management systems.

The subsequent adoption of VaR systems was very rapid, first among securities houses
and investment banks, and then among commercial banks, other financial institutions and
non-financial corporates.
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Today, VaR is widely used in almost every market-sensitive industry (with the exception
perhaps of shipping!) and has even gained recognition from regulatory authorities.
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Risk Management in Shipping
Measuring Market Risk
The Value-at-Risk (VaR) Approach
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VaR in practice
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VaR Basics:

VaR on a portfolio is the maximum loss we might expect over a given holding or
horizon period, at a given level of confidence (probability).

VaR is less restrictive on the choice of the distribution of returns and the focus is
on the tail of that distribution – the worst p percent of outcomes.
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Measuring Market Risk
The Value-at-Risk (VaR) Approach
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VaR in practice
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Estimating VaR: The various methodologies for estimating VaR actually differ on their
particular technique for constructing the distribution of possible portfolio values from which
VaR is inferred. The most common methodologies are:

Analytical methods (Variance/Covariance)
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Historical simulation

Monte-Carlo simulation
Attractions of VaR:

VaR is a single, summary, statistical measure of possible portfolio losses,
providing a common and consistent measure of risk across different positions and
risk factors.

It takes account of the correlations between different risk factors.

It is fairly straightforward to understand, even for non-technical people.
VaR variants: Following the same logic, other “at risk” measures have been proposed to
quantify risk in various settings: Cash Flow at Risk (CaR), Earnings at Risk (EaR), etc.
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Risk Management in Shipping
Measuring Market Risk in Shipping
Justification for Risk Management in Shipping

Business and market risk in shipping: the two faces of the same coin

Most industries can distinguish between business risks and market risks. These industries
have to worry about business risks and try to hedge away market risks which may have
an adverse side-effect on financial results. For example, an auto manufacturer has to
worry about business risks such as technology, competition, production, R&D, but may
also have an exposure to FX risk, which may hamper exports, or interest rate exposure
which may increase debt service on floating rate obligations.

Other industries cannot distinguish between business risks and market risks. The most
pronounced example is maybe that of financial institutions. A significant part of the
business of financial institutions is to take direct exposure in the world’s equity, interest
rate, currency, and commodity markets.

Shipping can be said to belong to the industries that cannot distinguish between business
risks and market risks. Financial results in shipping are directly affected by movements in
the world’s freight rate markets.
Shipowners are in effect in the business of managing shipping risk affecting a portfolio of
physical assets, rather than simply managing a fleet of vessels.
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Risk Management in Shipping
Measuring Market Risk in Shipping
Justification for Risk Management in Shipping

High market volatility

Freight rates have historically been very volatile. The impact of unforeseen geo-political
events and the slow speed of adjusting supply to demand have often resulted in dramatic
fluctuations in the level of freight rates.
Example of Freight Market Fluctuations
BFI
(Baltic Freight Index, BCI Route 2, BPI Route 1)
BCIr2
Standardised scaling (base=100 @1/3/99)
BPIr1
300
250
200
150
100
50
0
1/4/1985
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1/4/1987
1/4/1989
1/4/1991
1/4/1993
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1/4/1995
1/4/1997
1/4/1999
1/4/2001
1/4/2003
Risk Management in Shipping
Measuring Market Risk in Shipping
Justification for Risk Management in Shipping
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Industry inefficiencies
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Capital needs vs. sources of funds:

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
Asset – Liability (mis)matching

Asset economic life >> term of debt financing

Variable (uncertain) revenues to meet fixed debt claims
Pro-cyclical lending practices

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Shipping is a capital intensive industry with significant funding needs for fleet
expansion and replacement purposes. Yet, it has very limited opportunities to
diversify its sources of funding, as most of its financing comes in the form of bank
debt.
Many banks tend to be influenced by the general sentiment of the market and
ignore the cyclical nature of the business. Thus, they appear more willing to lend
when the market (and vessel prices) is high, despite the fact that the market will
eventually revert back to lower levels. In contrast, they appear rather hesitant to
extend credit at a period of low freight rates, although they are likely to rise to
more sustainable levels.
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Risk Management in Shipping
Measuring Market Risk in Shipping
Justification for Risk Management in Shipping

Lessons from the High Yield disaster of the late 90s
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In the late 90s, many shipping companies decided to tap the public debt markets with
high-yield bonds. Historically, it was the first massive attempt of the industry to diversify
away from bank debt, using an alternative source of external funding.

Unfortunately, nearly all of these high yield issues subsequently defaulted, mainly due to
insufficient cash flow generation.

This indicated poor risk assessment and a lack of appropriate tools to evaluate shipping
market risks.

Example:
Source: Moody’s (2002)
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Risk Management in Shipping
Measuring Market Risk in Shipping
Justification for Risk Management in Shipping

Lessons from the High Yield disaster of the late 90s

Examples:
Source: Moody’s (2002)
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Risk Management in Shipping
Measuring Market Risk in Shipping
Market Risk Measurement vs. Market Forecasting


Types of maritime forecasting

Structural econometric models: Model freight rates as a dependent variable, driven by a
number of independent variables, usually representing macro-economic factors that
influence shipping demand, e.g. GDP growth, oil prices, industrial output, etc.

Time series models: Model freight rates using the structure (serial correlation) in the past
history of the data itself. Future freight rates are determined based on lagged values of
their own history and do not exploit or infer causality with other economic variables.
Difficulties in maritime forecasting

Demand for shipping is characterized as
derived demand, meaning that it depends on
the demand of the commodities that are
shipped by sea.

Econometric models are prone to specification
problems. Model fit can always improve by
including more explanatory variables, which
may introduce multicollinearity problems.

It is possible that a small error in demand
estimation may lead to a gross mis-estimation
of freight rates (compare D1→D2 vs. D2→D3).
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Risk Management in Shipping
Measuring Market Risk in Shipping
Market Risk Measurement vs. Market Forecasting


Differences in scope

Scope:
Prepare for future vs. Predict the future

Motivation:
Prevent unexpected losses vs. Make a profit (beat the market)

Horizon:
Long-term vs. Short-term

Emphasis:
Tail of the distribution vs. Mean of the distribution
Differences in methodology

Measurement does not presuppose causality relations between economic variables.

Forecasting models have potentially infinite specifications (depending on choice of
explanatory variables).

Measurement focuses on producing the complete picture of potential outcomes (entire
distribution) rather than producing a point estimate (the mean of the distribution).
So, do we discard forecasting? NO, it can serve a useful complementary role, especially
in revealing causality relations between economic variables. Forecasting may also assist
in certain chartering or trading decisions in the short-run, where it is most effective.
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Risk Management in Shipping
Measuring Market Risk in Shipping
Impact of Freight Rate Volatility on Cash Flow


Identifying the impact of freight rate volatility on fleet cash flow

Fluctuations in freight rates directly affect fleet cash flow.

Cash flow performance is the topmost concern in shipping.

Ship financing belongs to the family of “Project Financing” (other forms of project financing
include airlines, infrastructure, real estate, etc.). There is a principle in project financing:
repayment MUST come from the operating cash flows of the financed asset.

So, what really matters in measuring freight market risk is the impact of freight rate
variability on cash flow performance.
Case study

We performed a simple exercise (historical simulation) of the cash flow generation of a
handysize dry bulker over two separate time periods (period A: Jan-1980 to Dec-1986,
and period B: Jan-1987 to Dec-1993).

We used the same set of assumptions in both cases (see the following slide), except for
using the actual freight rates for each period and the actual second-hand value of the
financed vessel at the start of the each period.

This exercise not only illustrates the impact of freight rate volatility on cash flow, but also
emphasizes the impact of shipping cycles and the importance of proper timing in maritime
decision-making.
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Risk Management in Shipping
Measuring Market Risk in Shipping
Impact of Freight Rate Volatility on Cash Flow

Case study: impact of freight rate volatility on fleet cash flow

Historical simulation assumptions and actual freight rate data (source: Clarksons)
ASSUMPTIONS
Operating
Rev. days
Opex days
Opex ($/day)
Loan
Vessel value
Loan amount
Advance ratio
Installments
Balloon
Loan interest
Start. Liquidity Cash balance
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PERIOD
A (80-86)
B (87-93)
25
25
30
30
3,500
3,500
10,000,000
3,800,000
7,000,000
2,660,000
70%
70%
84
84
30%
30%
5%
5%
0
0
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Risk Management in Shipping
Measuring Market Risk in Shipping
Impact of Freight Rate Volatility on Cash Flow

Case study: impact of freight rate volatility on fleet cash flow
Chart of monthly net cash flow (after debt service, but excluding balloon payment):

A (80-86)
B (87-93)
Impact of freight rate volatility on fleet cash flow
150,000
Monthly Net Cash Flow ($)
100,000
50,000
0
4
8
12
16
20
24
28
32
36
40
44
-50,000
-100,000
-150,000
Month
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52
56
60
64
68
72
76
80
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Risk Management in Shipping
Measuring Market Risk in Shipping
Impact of Freight Rate Volatility on Cash Flow

Case study: impact of freight rate volatility on fleet cash flow
Chart of accumulated liquidity (excluding balloon payment):

A (80-86)
B (87-93)
Impact of freight rate volatility on fleet cash flow
5,000,000
4,000,000
Accumulated Liquidity ($)
3,000,000
2,000,000
1,000,000
0
4
8
12
16
20
24
28
32
36
40
44
-1,000,000
-2,000,000
-3,000,000
-4,000,000
-5,000,000
Month
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Risk Management in Shipping
Measuring Market Risk in Shipping
A Framework for Measuring Freight Market Risk

Basic Assumptions and Objectives

It is possible to develop a framework for measuring freight market risk using the VaR
paradigm.

The risk factors in this framework consist of freight rates.

Freight rates are assumed to follow a random walk and are modeled using appropriate
stochastic processes.

The stochastic processes that describe the evolution of freight rates are able to
replicate certain known characteristics of freight rate dynamics (cyclicality, seasonality,
random shocks).

Monte-Carlo simulation is used to generate future freight rate scenarios, in accordance
with the underlying stochastic process for each risk factor.

The key measure of risk is fleet cash flow.

For each freight rate scenario, we re-compute future cash flow, using an appropriate
cash flow model which takes into account debt repayment and other cost items (e.g.
drydocking costs, special surveys, etc.).

Thus we construct the entire distribution of future cash flow, from which we can make
VaR-type inferences based on a specified confidence level.
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Risk Management in Shipping
Measuring Market Risk in Shipping
A Framework for Measuring Freight Market Risk

Modeling the stochastic behavior of freight rates

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Any time series data can be thought of as being generated by a particular stochastic or
random process, the “true” data-generating process (DGP). A concrete set of data, such
as a historical data-series on a freight rate, can be regarded as a particular realization (i.e.
a sample) of the underlying true DGP. The distinction between the stochastic process and
its realization is akin to the distinction between population and sample in cross-sectional
data. Just as we use sample data to draw inferences about a population, in time series we
use the realization to draw inferences about the underlying stochastic process.
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Risk Management in Shipping
Measuring Market Risk in Shipping
A Framework for Measuring Freight Market Risk

Selecting a stochastic process

The modeling of any price variable begins with the choice of a particular stochastic
process which captures the characteristics of asset price dynamics. In order to make this
selection, we are guided by theoretical considerations, such as the theory concerning the
operation of freight equilibrating mechanisms, as well as by empirical analysis of historical
data (e.g. mean reversion, fat tails, autocorrelation, volatility clustering, etc.)

There is a large number of alternative stochastic processes that can be tested to capture
the dynamics of freight rates. Below we provide a few indicative (simplistic) models:
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
Geometric Brownian Motion (GBM):
dr (t )  r (t )dt  r (t )dz (t )

Ornstein-Uhlenbeck (O-U) process:
dr (t )  a(  r (t ))dt  dz (t )

Jump-Diffusion (O-U with Jumps):
dr (t )  a(  r (t ))dt  dz (t )  Jd (h)
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Risk Management in Shipping
Measuring Market Risk in Shipping
A Framework for Measuring Freight Market Risk

Estimating model parameters

As discussed previously, a concrete set of historical data can be regarded as a particular
realization (i.e. a sample) of the underlying “true” DGP. The objective is to find a
theoretical DGP that provides the best fit for the actual data. This is accomplished by
estimating the parameters of each theoretical DGP and comparing the various models in
terms of some measure of goodness-of-fit.

Many stochastic processes admit exact discretization or numerical approximations (using
the Euler or higher-order methods) which allow the testing of the underlying processes
using standard econometric techniques. Both the GBM and O-U processes admit such
discretizations which lead to time-series specifications of a linear autoregressive form.

For example, the GBM model can be estimated by running the following regression:
r (t )  r (t  1)  c   (t )

The parameters of an O-U process can be estimated using discrete-time data by running
the regression:
r (t )  r (t  1)  c  br (t  1)   (t )
and then calculating

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  cˆ / bˆ
a   log( 1  bˆ)
  ˆ 
log(1  bˆ)
(1  bˆ) 2  1
More advanced models require other techniques, e.g. Maximum Likelihood Estimation
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Risk Management in Shipping
Measuring Market Risk in Shipping
A Framework for Measuring Freight Market Risk

Simulating the stochastic evolution of freight rates

Having discretized the stochastic process and estimated its parameters, we proceed with
iterative sampling from the probability distribution(s) used in our model, in order to
generate future freight rate scenarios.

This technique is known as Monte-Carlo simulation and includes several steps:


Random number generation (pseudo-random or low-discrepancy numbers)

Transformation of independent random number into correlated random numbers

Variance reduction methods (to improve the accuracy for a given number of runs)
Example: As discussed in the previous slide, the O-U process can be interpreted as the
continuous-time version of a first-order autoregressive process in discrete time.
Specifically, the O-U process is the limiting case as Δt  0 of the following AR(1)
process:
r (t )  r (t  1)   (1  e  a )  (e  a  1)r (t  1)   (t )
where ε(t) is normally distributed with mean zero and standard deviation σε

Thus, we can simulate an O-U process, by drawing random numbers from a normal
distribution with mean zero and
deviation σε and generating r(t) as follows:
 a standard
a
r (t )   (1  e )  e r (t 1)   (t )
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Risk Management in Shipping
Measuring Market Risk in Shipping
A Framework for Measuring Freight Market Risk

Building the distribution of future fleet cash flows


For each freight rate scenario produced by our simulation, we re-compute the fleet cash
flow based on some cash flow model and plot the results in a histogram. This represents
the distribution of future fleet cash flows.
Making risk inferences from the distribution of future fleet cash flows

The distribution of cash flow results reveals the risk profile of the fleet, in terms of the
range of possible cash flows that the fleet is able to generate in the future.

We can “read” this distribution in order to make probabilistic inferences about the risk of
our fleet. For example:
www.freightmetrics.com

What is the probability that the fleet will breakeven?

What is the maximum possible cash deficit at the 95% probability level?
31
Risk Management in Shipping
Measuring Market Risk in Shipping
A Framework for Measuring Freight Market Risk

A practical example

Assuming a simple O-U process, we modeled the 1-year Time-Charter rate for dry bulk
handysize vessels and simulated 1000 different scenarios. Below we compare the
distribution of actual monthly returns (282 observations –from Feb-76 to Jul-99) with the
distribution of simulated (random) monthly returns. From this we can compute the cash
flow of the vessel and produce the distribution of possible cash flows for next month.
90
Empirical
Simulated
80
70
60
50
40
30
20
10
0
-19%
www.freightmetrics.com
-16%
-14%
-11%
-9%
-6%
-4%
-1%
1%
4%
32
6%
9%
11%
14%
16%
19%
Risk Management in Shipping
Measuring Market Risk with Fr8MetricsTM
Main Methodological Features

Main methodological features

Fr8Metrics™ is a framework for quantifying freight market risk in shipping portfolios.
Fr8Metrics™ is generally based on the Value-at-Risk (VaR) concept, but differs in the use
of proprietary stochastic models developed to simulate the evolution of freight rates.
These models are designed to replicate the unique seasonal and cyclical characteristics
of shipping markets.

The Fr8Metrics™ methodology is simulation-based rather than forecast-based. It draws
on advanced Monte-Carlo simulation techniques to generate future freight market
scenarios from which we estimate the likely distribution of various financial measures,
such as cash flow, accumulated liquidity, hull cover, NPV, etc.

Fr8Metrics™ is able to incorporate the influence of correlations, not only across different
market segments within the shipping industry, but also between shipping and financial
markets. Thus, it is possible to capture potential diversification effects within a portfolio
that combines both shipping and financial assets.

Fr8Metrics™ is able to support portfolios that combine both physical assets (vessels) and
“paper” assets (derivatives).
www.freightmetrics.com
33
Risk Management in Shipping
Measuring Market Risk with Fr8MetricsTM
How Does Fr8MetricsTM Work?

Step 1: Portfolio Definition


Step 2: Risk Mapping


Input details for the fleet (charter agreements, cost data, etc.), loans (repayment
schedules, interest cost, collateral vessels, etc.), and derivatives.
Assign risk factors to vessels and derivatives.
Step 3: Project Definition

Select the portfolio(s) which will be simulated.

Specify cash flow model.

Specify risk metric.

Specify simulation parameters (number of scenarios, horizon, confidence level, etc.)
www.freightmetrics.com
34
Risk Management in Shipping
Measuring Market Risk with Fr8MetricsTM
How Does Fr8MetricsTM Work?

Step 4: Scenario Generation

www.freightmetrics.com
Generate n (=number of scenarios specified in Step 3) future realizations for each risk
factor for the time horizon specified in Step 3, in accordance with the underlying
stochastic process of each risk factor:
35
Risk Management in Shipping
Measuring Market Risk with Fr8MetricsTM
How Does Fr8MetricsTM Work?

Step 5: Metric Computation

www.freightmetrics.com
Iteratively substitute values from each of the n scenarios from Step 4 into the cash flow
model specified in Step 3, calculate the n future cash flow results, and plot them in a
histogram:
36
Risk Management in Shipping
Measuring Market Risk with Fr8MetricsTM
How Does Fr8MetricsTM Work?

Step 6: Risk Inference

Using the distribution of cash flow results from Step 5, find the cash flow estimate
corresponding to the desired confidence level specified in Step 3.

Having exposed the complete risk profile of the portfolio(s) specified in Step 3, the user
(banker, shipowner, etc.) is able to take calculated, risk-informed decisions in accordance
with his risk preferences:
www.freightmetrics.com
37
Risk Management in Shipping
Measuring Market Risk with Fr8MetricsTM
Benefits of the Fr8MetricsTM Methodology


General benefits of Monte-Carlo based methods

Flexibility to support a wide range of stochastic processes.

Not restrictive in terms of distributional assumptions.

Ability to incorporate correlations among risk factors.

Ability to incorporate decision rules along the simulated paths (e.g. exercise of charter
options).
Particular benefits of the Fr8MetricsTM methodology

Utilizes stochastic models specifically developed to capture freight rate dynamics.

Reveals diversification effects across shipping assets, as well as between shipping and
financial exposures.

Provides a framework for monitoring derivatives, developing hedging strategies and
assessing hedge effectiveness.
www.freightmetrics.com
38
Risk Management in Shipping
Measuring Market Risk with Fr8MetricsTM
Potential Users and Managerial Applications



Shipping Banks

Determining credit terms: maximum advance ratio, liquidity covenant, loan spread

Risk assessment: repayment risk, probability of covenant breach

Estimating default probabilities, verifying internal risk ratings

Promoting cross-selling, derivatives sales, hedge proposals
Shipowners

Investment decisions, e.g. dry bulk vs. tanker segments

Chartering decisions, e.g. time charter vs. spot employment

Financing decisions, e.g. high-yield bond vs. bank debt

Hedging decisions, e.g. derivatives vs. long-term charter
Freight Traders

Risk assessment and monitoring

Risk-adjusted performance evaluation
www.freightmetrics.com
39
Risk Management in Shipping
Measuring Market Risk with Fr8MetricsTM
Software Implementation


Product features

Hierarchical portfolios

Multi-currency environment

Periodic updates of parameter estimates for underlying stochastic models

3 cash flow model formats (Fleet, GAAP, Sources and Uses)

User-defined cash flow items

Generation of pro-forma cash flow statements
Technology

Windows-based

Developed in .NET environment

Extensive use of XML

Databases: SQL / Access
www.freightmetrics.com
40
Risk Management in Shipping
Managing Freight Market Risk
Altering the Risk Profile with Managerial Decisions

Risk-informed decision-making

As mentioned previously, the objective of risk management is not necessarily to eliminate
risk, but rather to alter our risk profile according to the prevailing market conditions, our
risk preferences, and potential regulatory or contractual requirements.

Having exposed the complete risk profile of a shipping portfolio within a VaR framework,
we are able to decide whether it suits our risk preferences or to make comparisons among
alternative business strategies.
Choice of strategy is subject to risk preference
(Strategy B: higher expected return, but higher risk)
www.freightmetrics.com
Strategy A is dominant
(Strategy A: higher expected return AND lower risk)
41
Risk Management in Shipping
Managing Freight Market Risk
Altering the Risk Profile with Managerial Decisions



Asset Allocation decisions

Expand the fleet in the dry cargo or the tanker segment?

Buy one VLCC or two Aframaxes?

Buy one 5-year old vessel or two 15-year old vessels?

Order a newbuilding in Korea (cheaper, but FX risk) or in the US?
Chartering decisions

Trade in the spot market or lock in a 3-year time charter at a rate that is –currentlylower than the spot rate?

Accept a high time charter rate or a lower time charter rate with an option to renew?

Charter-in or charter-out for the next one year?
Funding decisions

Finance new acquisitions through bank debt or high yield issue?

Go for a 5-year loan with low spread or a 7-year loan with higher spread?
www.freightmetrics.com
42
Risk Management in Shipping
Managing Freight Market Risk
Altering the Risk Profile with Freight Derivatives

Definition of derivatives

In chemistry, a derivative is a “substance related structurally to another substance and
theoretically derivable from it (...) a substance that can be made from another
substance”.1 Derivatives in finance work on the same principle. They are financial
instruments whose promised payoffs are derived from the value of something else,
generally called the underlying.
1 This definition comes from the online version of the Merriam-Webster Collegiate Dictionary. See
http://www.britannica.com/cgi-bin/dict?va=derivative

Types of freight derivatives

Forward Freight Agreements (FFAs): An agreement between two counterparties to settle
a freight rate for a specified quantity of cargo or type of vessel, for a certain route, and at
a certain date in the future.

The underlying asset of the FFA contracts can be any of the routes that constitute the
indices produced by the Baltic Exchange.

FFAs are settled in cash on the difference between the contract price and an appropriate
settlement price at expiration.

To establish an FFA, we need to specify: route, price, duration/quantity, settlement

Other types of derivatives: Options, Swaps, Swaptions, etc.
www.freightmetrics.com
43
Risk Management in Shipping
Managing Freight Market Risk
Altering the Risk Profile with Freight Derivatives

The market of freight derivatives



Historical development of freight derivatives:

Freight derivatives existed since 1985 with the creation of the BFI (Baltic Freight
Index), a basket of individual dry cargo routes. This index served as a settlement
mechanism for freight futures listed on BIFFEX (subsequently merged with
LIFFE, contracts de-listed in April 2002).

Since 1992, the individual shipping routes could be traded “over the counter” (i.e.
not through an exchange) in the form of FFAs.
Current market size and players:

Estimated annual turnover: $4.0 billion in notional value of freight.

Types of players: Shipowners, Charterers, Trading Houses, Shipbrokers
The role of the Baltic Exchange:

www.freightmetrics.com
Sets the rules and oversees the process of collecting and processing the brokers’
assessments of freight rates in more than 30 cargo routes. These prices are used
for the settlement of FFA transactions.
44
Risk Management in Shipping
Managing Freight Market Risk
Altering the Risk Profile with Freight Derivatives

Fundamentals of freight derivatives trading


Trading process

Price discovery through brokers

FFA negotiation

Counterparty clearance

Documentation

Basis risk (sources: correlation, time lag)

Marking-to-Market
Designing a hedging program
1.
Understand the distribution / dynamics of freight rates.
2.
Estimate the impact of adverse freight rate movements on fleet cash flow.
3.
Decide whether to hedge, depending on external and internal considerations.
4.
Choose the appropriate financial instruments.
5.
Determine how much to hedge.
www.freightmetrics.com
45
Risk Management in Shipping
References, Links, and Further Reading

References

Adland, Roar (June 2000), Theoretical Vessel Valuation and Asset Play in Bulk Shipping, Thesis submitted for the
MS in Ocean Systems Management, MIT
Attikouris, Kyriakos (April 2000), Modeling Freight Rates, Thesis submitted for the Diploma in Mathematical Finance,
University of Oxford
Attikouris, Kyriakos (March 1996), Time Series Applications in the Ocean Shipping Business, Project submitted for
the course Applied Time Series Analysis (MBA program), University of Rochester
Concalves, Franklin de Oliveira (September 1992), Optimal Chartering and Investment Policies for Bulk Shipping,
Thesis submitted for the PhD in Ocean Systems Management, MIT
Dowd, Kevin (2002), Measuring Market Risk, Wiley
Drewry Shipping Consultants (1997), Shipping Futures and Derivatives, Briefing Report
Moody’s Investor Services (2002), Default & Recovery Rates of European Corporate Bond Issuers, 1985-2001
Stopford, Martin (1997), Maritime Economics, Routledge
Vose, David (2000), Risk Analysis, Wiley
Wilmott, Paul (1998), Derivatives, Wiley
Magazines: Risk, Marine Money, Lloyd’s Shipping Economist
Seminar notes: Freight Derivatives seminar, organized by the Cambridge Academy of Transport and the Baltic
Exchange (25 November 2002)












Links



www.riskmetrics.com
www.gloriamundi.org
www.balticexchange.com
www.freightmetrics.com
RiskMetrics Group
GloriaMundi (the best internet source on VaR material)
The Baltic Exchange
46