Risk Management in Shipping Modeling, Measuring,

1
Risk Management in ShippingModeling,
Measuring, Managing Freight Market Uncertainty
Presented at National Technical University of
Athens School of Naval Architecture Marine
Engineering 29 May 2003
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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
  Fr8MetricsTM Methodology
• 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.
• 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
• Risk Modeling Before any attempt to take
  decisions on risk considerations, we must
  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.
• Risk Measurement After identifying and modeling
  the underlying risk factors, we must determine
  their significance and quantify their influence
  on portfolio value and financial results.
• Risk Management Having identified and measured
  our risks, we are then able to take 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 assessors 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
• Allocation of capital
• Risk-based pricing
• Risk limits
• 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
• 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

• 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 (PL) 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 415 report
  indicating risk and potential losses over the
  next 24 hours, across the banks 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.
• 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.
• 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

• VaR in practice
• 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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Risk Management in Shipping Measuring Market
Risk The Value-at-Risk (VaR) Approach

• VaR in practice
• 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)
• 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, RD, 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 worlds 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 worlds
  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.

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Risk Management in Shipping Measuring Market
Risk in Shipping Justification for Risk
Management in Shipping

• Industry inefficiencies
• Capital needs vs. sources of funds
• 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.
• Asset Liability (mis)matching
• Asset economic life gtgt term of debt financing
• Variable (uncertain) revenues to meet fixed debt
  claims
• Pro-cyclical lending practices
• 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
• 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 Moodys (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 Moodys (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)

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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)

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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)

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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
• 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
• Geometric Brownian Motion (GBM)
• Ornstein-Uhlenbeck (O-U) process
• Jump-Diffusion (O-U with Jumps)

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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
• The parameters of an O-U process can be estimated
  using discrete-time data by running the
  regression
• and then calculating
• 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 ?t ? 0 of the following AR(1) process
• where e(t) is normally distributed with mean
  zero and standard deviation se
• Thus, we can simulate an O-U process, by drawing
  random numbers from a normal distribution with
  mean zero and standard deviation se and
  generating r(t) as follows

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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
• What is the probability that the fleet will
  breakeven?
• What is the maximum possible cash deficit at the
  95 probability level?

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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.

34
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).

35
Risk Management in Shipping Measuring Market
Risk with Fr8MetricsTM How Does Fr8MetricsTM
Work?

• Step 1 Portfolio Definition
• Input details for the fleet (charter agreements,
  cost data, etc.), loans (repayment schedules,
  interest cost, collateral vessels, etc.), and
  derivatives.
• Step 2 Risk Mapping
• 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.)

36
Risk Management in Shipping Measuring Market
Risk with Fr8MetricsTM How Does Fr8MetricsTM
Work?

• Step 4 Scenario Generation
• 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

37
Risk Management in Shipping Measuring Market
Risk with Fr8MetricsTM How Does Fr8MetricsTM
Work?

• Step 5 Metric Computation
• 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

38
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

39
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.

40
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

41
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

42
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)
Strategy A is dominant (Strategy A higher
expected return AND lower risk)
43
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 currently- lower 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?

44
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?vaderivati
  ve
• 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.

45
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
• 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.

46
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
• Understand the distribution / dynamics of freight
  rates.
• Estimate the impact of adverse freight rate
  movements on fleet cash flow.
• Decide whether to hedge, depending on external
  and internal considerations.
• Choose the appropriate financial instruments.
• Determine how much to hedge.

47
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
• Moodys 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, Lloyds 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 RiskMetrics Group
• www.gloriamundi.org GloriaMundi (the best
  internet source on VaR material)
• www.balticexchange.com The Baltic Exchange